- Email: [email protected]
MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer
Original Study
MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer Renee F. Cattell,1,2 James J. Kang,1 Thomas Ren,1 Pauline B. Huang,1 Ashima Muttreja,1 Sarah Dacosta,1 Haifang Li,1 Lea Baer,3 Sean Clouston,4 Roxanne Palermo,1 Paul Fisher,1 Cliff Bernstein,1 Jules A. Cohen,3 Tim Q. Duong1 Abstract Early prediction of pathologic response can impact patient management and treatment planning for patients with breast cancer. This study analyzed patients with stage 2 or 3 breast cancer undergoing neoadjuvant chemotherapy (N [ 132). Axillary lymph node initial volume could not discriminate between patients who achieved pathologic complete response and those who did not, whereas the rate of volume changes was informative. Introduction: Longitudinal monitoring of breast tumor volume over the course of chemotherapy is informative of pathologic response. This study aims to determine whether axillary lymph node (aLN) volume by magnetic resonance imaging (MRI) could augment the prediction accuracy of treatment response to neoadjuvant chemotherapy (NAC). Materials and Methods: Level-2a curated data from the I-SPY-1 TRIAL (2002-2006) were used. Patients had stage 2 or 3 breast cancer. MRI was acquired pre-, during, and post-NAC. A subset with visible aLNs on MRI was identified (N ¼ 132). Prediction of pathologic complete response (PCR) was made using breast tumor volume changes, nodal volume changes, and combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes (3 mL or w1.79 cm diameter cutoff). Receiver operating characteristic curve analysis was used to quantify prediction performance. Results: The rate of change of aLN and breast tumor volume were informative of pathologic response, with prediction being most informative early in treatment (area under the curve (AUC), 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75). Larger aLN volume was associated with hormone receptor negativity, with the largest nodal volume for triple negative subtypes. Sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having AUC of 0.87. Conclusion: aLN MRI offers clinically relevant information and has the potential to predict treatment response to NAC in patients with breast cancer. Clinical Breast Cancer, Vol. -, No. -, --- ª 2019 Elsevier Inc. All rights reserved. Keywords: Breast tumor volume, Dynamic contrast-enhanced MRI, Magnetic resonance imaging, Molecular subtypes, Sentinel lymph node biopsy
Introduction Breast cancer becomes most concerning when it metastasizes to axillary lymph nodes (aLNs).1 aLN involvement also increases 1 Department of Radiology, Stony Brook University School of Medicine, Stony Brook, NY 2 Department of Biomedical Engineering 3 Department of Medical Oncology 4 Department of Preventive Medicine and Population Health, Stony Brook University, Stony Brook, NY
Submitted: Jan 17, 2019; Revised: May 24, 2019; Accepted: Jun 13, 2019 Address for correspondence: Tim Q. Duong, PhD, Department of Radiology, Stony Brook University Hospital, 101 Nicolls Rd, Stony Brook, NY 11794 E-mail contact: [email protected]
1526-8209/$ - see frontmatter ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.clbc.2019.06.006
the risk of recurrence.2,3 For patients with nodal metastasis, disease-free survival at 8 years and 5 years decreases from 70% to 40%,4 and 98% to 85%,5 respectively. As a result, aLN characteristics are critical for diagnosis, prognosis, and monitoring treatment.6 Although aLN management has become less invasive with the introduction of sentinel biopsy as opposed to aLN dissection, there remain significant side effects including shoulder dysfunction, lymphedema, and nerve damage in as much as one-fourth of patients.6,7 Moreover, > 70% of biopsied sentinel aLNs are negative,7 highlighting significant unnecessary risks. Accurate non-invasive imaging of nodal involvement has the potential to avoid unnecessary invasive aLN biopsy and dissection while offering a predictive imaging biomarker for neoadjuvant chemotherapy (NAC).
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Axillary Lymph Node Volume and Pathological Complete Responses Breast magnetic resonance imaging (MRI) is a standard part of evaluating NAC response because it can reliably identify residual inbreast disease (positive predictive value of 93%).8 It is, however, less successful at predicting the absence of disease (negative predictive value of 65%)8 and pathologic complete response (PCR) (area under curve [AUC]: w0.7-0.8).9 MRI of aLN has the potential to improve prediction of PCR, and the Response Evaluation Criteria of Solid Tumors (RECIST) recommend careful evaluation of the aLN in assessing response to therapy in breast cancer.10 To date, efforts to utilize aLN MRI has been limited by small aLN sizes, breast coil sensitivity in regions near aLNs, and exclusion of aLNs from field of view. Although nodal morphologic features on MRI are predictive of malignancy,2,10 the use of aLN MRI to predict treatment response to NAC has not been adequately studied. The goal of this study was to determine whether aLN MRI could augment the prediction accuracy of treatment response to NAC in a longitudinal manner. We utilized a subset of the ISPY-1 (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis) clinical trial data.9,11 The volume of aLNs were quantified as a function of pre-, concomitant, and post-NAC, and pathologic response, and stratified by aLN size. The AUC was used to evaluate the efficacy of breast and/or aLN volume to predict PCR. We tested the hypothesis that changes in aLN volume in conjunction with breast tumor volume can more accurately predict PCR to NAC. If aLN volume and/or rate of change of volume are found to be associated with presence of disease or treatment response, it could change clinical practice. This could have immediate clinical value because aLN volume can be readily measured by MRI.
Materials and Methods Patient Cohort
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The full ISPY-1 TRIAL (2002-2006) had 221 participants. The current study utilizes the Level 2a curated dataset, which had 207 participants.9,11-13 Of the 207 participants, 132 participants had usable nodal MRI data as determined by agreements by 2 independent observers that aLN volume could be reliably contoured. The typical causes for exclusion were aLNs were out of field of view, were obscured by respiratory motion artifacts or saturation pulses to remove respiratory artifacts, and/or had limited visibility owing to breast coil sensitivity. Previous reports using this open dataset (ISPY-1) focused on MRI in-breast tumor volume to predict PCR,9,11,14,15 but did not analyze nodal MRI information. Our study focused on evaluating the contribution of the aLN MRI volume in the prediction of PCR. All patients had stage 2 or 3 breast cancer with breast tumors 3 cm in size, and underwent anthracycline-cyclophosphamide (AC) with or without taxane treatment. MRI was acquired pre-NAC (time point 1 [TP1]), w2 weeks after the first cycle of AC (TP2), after all AC was administered but prior to taxane if any (TP3), and post-NAC and before surgery (TP4). In our cohort, 4 subjects received only AC, but no taxane. Data were analyzed for response to neoadjuvant therapy with respect to the PCR, defined as no residual invasive disease in breast or node after NAC based on final surgical pathology (pT0/is pN0) (non-PCR, n ¼ 92; PCR, n ¼ 35). Relation of nodal and tumor volume was also analyzed with respective to breast PCR (pT0/is), defined as no residual
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invasive disease in the breast regardless of aLN status (non-PCR, n ¼ 82; PCR, n ¼ 44), and nodal PCR (pN0), defined as no residual invasive disease in the aLN regardless of breast status (nonPCR, n ¼ 63; PCR, n ¼ 60). Patients who did or did not achieve PCR are labeled as PCR or non-PCR, respectively. We note that sample sizes varied slightly for each time point, given that aLN was not consistently visible on the MR image. A second cohort were used to quantify normal aLN volume. We performed a retrospective study from the Stony Brook Hospital imaging database from January 1, 2012 to July 30, 2018 with Institutional Review Board approval. These patients were newly diagnosed patients with locally advanced unilateral breast cancer with visible aLNs on a pre-chemotherapy standard breast MRI, similar to ISPY-1.
MRI Analysis MRI of this multicenter study was performed on 1.5-T scanners using a standard dedicated clinical breast protocol.9 All visible aLNs on the first post-contrast image (2 minutes post-contrast MRI) were manually segmented (ITK-SNAP, http://www.itksnap.org/pmwiki/ pmwiki.php) on TP1. The same aLNs were carefully identified on TP2 through TP4 based on anatomical landmarks. For consistency, the largest node pre-treatment per patient was identified to follow over time. Although individualized node-specific histopathologic validation was not available, retrospective review under guidance of our breast radiologists confirmed essentially all (95%) of the largest aLNs were the most suspicious for malignancy. The breast tumor volumes, as reported in the ISPY-1 dataset,9,11 were also analyzed. Breast tumor and nodal volumes of the PCR and non-PCR groups were plotted for each treatment time point (TP1, TP2, TP3, and TP4). Absolute (as well as %) breast tumor and nodal volume changes between 2 adjacent time points (TP2-TP1, TP3TP2, and TP4-TP3) were also plotted for the PCR and non-PCR groups. Similar conclusions were reached for absolute and % changes, so % changes were not reported. In addition, volume characteristics were sub-stratified by patients with and without large aLN, using the threshold of 3 mL (equivalent to w1.79 cm diameter, assuming spheres). This threshold was 4 times the standard deviation across nodes of the average unaffected node volume (0.60 0.59 mL), which was obtained from the contralateral side of patients with unilateral breast cancer of an independent group of 61 patients (192 nodes) at our 1.5 T MRI scanner. Prediction of PCR (pT0/is pN0) was made using breast tumor volume changes, nodal volume changes, and the combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes.
Statistical Analysis Analyses examining differences in outcomes across different time points used unpaired 2-tailed t tests with unequal variance. Multivariable prediction models used the generalized linear modeling with logistic regression. Receiver operating characteristic (ROC) curve analysis was performed with PCR as ground truth. AUC, sensitivity, specificity, and accuracy were tabulated. The Youden index was used for determining the optimal cut-point. Matlab (R2017b, MathWorks, Natick, MA) was used for all statistical analyses. A P-value of .05 with multiple-comparison correction16 was used to determine statistical significance.
Renee F. Cattell et al Table 1 Patient Demographics Lymph Node Dataset (n [ 132), n (%)
ISPY-1 (n [ 221), n (%)
48.96 9.29
48.25 8.90
Caucasian
97 (73.5)
165 (74.7)
African American
28 (21.2)
42 (19.0)
Asian
6 (4.5)
9 (4.0)
Native Hawaiian/Pacific Islander
0 (0.0)
1 (0.5)
Multiple race
1 (0.8)
2 (0.9)
Missing
0 (0.0)
2 (0.9)
Mean age, y (standard deviation) Race
Estrogen receptor status Negative
54 (40.9)
94 (42.5)
Positive
76 (57.6)
125 (56.6)
Missing
2 (1.5)
2 (0.9)
Negative
66 (50.0)
115 (52.0)
Positive
64 (48.5)
104 (47.1)
Missing
2 (1.5)
2 (0.9)
Negative
81 (61.4)
149 (67.4)
Positive
47 (35.6)
67 (30.3)
Missing
4 (3.0)
5 (2.3)
HRþ/HER2
57 (43.2)
96 (43.4)
HER2þ
47 (35.6)
67 (30.3)
Triple negative
24 (18.2)
53 (24.0)
4 (3.0)
5 (2.3)
Unilateral
130 (98.5)
217 (98.2)
Bilateral
2 (1.5)
4 (1.8)
Progesterone receptor status
HER2 status
3-Level HR/HER2 category
Missing Cancer laterality
Tumor laterality Left
60 (45.5)
107 (48.4)
Right
72 (54.5)
114 (51.6)
No
92 (69.7)
157 (71.1)
Yes
35 (26.5)
58 (26.2)
5 (3.8)
6 (2.7)
No
82 (62.1)
146 (66.1)
Yes
44 (33.3)
69 (31.2)
6 (4.6)
6 (2.7)
No
63 (47.7)
97 (43.9)
Yes
60 (45.5)
99 (44.8)
9 (6.8)
25 (11.3)
Full pathologic complete response (pT0/is pN0)
Missing Breast pathologic complete response (pT0/is)
Missing Nodal pathologic complete response (pN0)
Missing
None of the demographic data of our sub-cohort differed significantly from the parent data by proportional test (P > .05). Abbreviations: HER2 ¼ human epidermal growth factor receptor 2; HR ¼ hormone receptor.
Results Dataset Characteristics Of the 207 patients in the parent Level 2a ISPY-1 dataset, 132 (64%) patients had usable aLN MRI quality. There were 119, 103,
99, and 114 patients with acceptable MRI data of the nodes at TP1, TP2, TP3, and TP4. There were 93, 89, and 102 patients with acceptable MRI data of the nodes at TP2-TP1, TP3-TP1, and TP4-TP1.
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Axillary Lymph Node Volume and Pathological Complete Responses Demographic and receptor subtype data (Table 1) were not significantly different between this subset and the parent dataset as determined by proportion tests (P > .05).
aLN and Breast Tumor Volume Profiles Representative images pre- and post-NAC from a patient with large nodes and a patient without large nodes are shown in Figure 1. aLN had a variety of sizes, shapes, and heterogeneous enhancement. Most of the large nodes were clearly modulated by NAC. Figure 2A shows average aLN volumes at different time points, with separation into the PCR and non-PCR groups using definitions of full, breast, and nodal PCR. At each individual time point, whether separated by full, breast, or nodal PCR, there was no difference among the averages (P > .05). Similar results are shown in Figure 2B with breast tumor volume. Given that full, breast, and nodal PCR have similar trends, moving forward, we report results
only for full PCR, defined as no invasive disease in the breast and axillary nodes at final pathology (pT0/is pN0). We note that sample sizes for the PCR and non-PCR groups varied owing to missing aLN volume data. The non-PCR group sample for full, breast, and nodal ranged from 64 to 84, 57 to 74, and 44 to 58, respectively. The PCR group sample for full, breast, and nodal ranged from 25 to 31, 32 to 39, and 43 to 54, respectively. Figure 3A shows average aLN volumes at different time points. As mentioned above, we use full PCR (pT0/is pN0) moving forward, for which sample sizes varied across time for the non-PCR (n ¼ 64-84) and PCR (n ¼ 25-31) groups. The PCR group aLN volume was relatively large at TP1, whereas the non-PCR aLN volume was comparatively smaller (non-PCR, 3.28 0.47 mL vs. PCR, 5.51 1.36 mL; P ¼ .18) at the first time point. Non-PCR aLN volume decreased more gradually with time compared with the PCR group. Both the PCR and non-PCR group aLN volume at
Figure 1 Representative Magnetic Resonance Imaging of the Breast and Axillary Lymph Nodes. A Patient Without Large Nodes (A) and a Patient With Large Nodes (B) are Shown. Data Were Obtained at (Early) 2 Minutes Post Contrast Magnetic Resonance Imaging. Breast Lesion Is Hyperintense. Lymph Nodes Are of Various Size and Shape. Thin Red Arrows Point to Representative Small Nodes, and Thick Red Arrows Point to Representative Large Nodes. The Patient in (A) Was 49 Years of Age at Breast Cancer Diagnosis and Achieved Pathologic Complete Response. The Patient in (B) Was 53 Years of Age at Breast Cancer Diagnosis and did Not Achieve a Pathologic Complete Response
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Renee F. Cattell et al Figure 2 Average Volume of Axillary Lymph Nodes (A) and Breast Tumor (B) Over Time With Separation Into Full, Breast, and Nodal PCR. We Note That Sample Sizes for PCR and Non-PCR Groups Varied due to Missing Axillary Lymph Node Volume Data. The Non-PCR Group Sample for Full, Breast, and Nodal Volume Ranged From 64 to 84, 57 to 74, and 44 to 58, Respectively. The PCR Group Sample for Full, Breast, and Nodal Volume Ranged From 25 to 31, 32 to 39, and 43 to 54, Respectively
Abbreviation: PCR ¼ pathologic complete response.
TP1 was significantly different from TP2, TP3, and TP4. There was a significant difference at TP4 between the PCR and non-PCR groups, with the non-PCR group having a larger volume (nonPCR, 0.88 0.14 mL vs. PCR, 0.53 0.06 mL; P ¼ .04). When plotted as magnitude changes between 2 adjacent time points, the biggest difference in volume change were from TP2 to TP1, with the PCR group reducing in volume by a significantly larger amount (non-PCR, 0.86 0.15 mL vs. PCR, 3.93 1.42 mL; P ¼ .07). Figure 3B shows the breast tumor volumes at different time points. The PCR and non-PCR groups’ breast tumor volumes were similar at the first time point (non-PCR, 25.18 2.69 mL vs. PCR, 27.77 6.07 mL; P ¼ .69). However, the tumor volume of the non-PCR group decreased very gradually with time. Both the PCR and non-PCR group breast tumor volumes at TP1 were significantly different from TP2, TP3, and TP4. Breast tumor volumes were statistically different between the non-PCR and PCR groups at TP2 (non-PCR, 18.10 2.55 mL vs. PCR, 9.32 2.11 mL; P ¼ .02), TP3 (non-PCR, 8.68 3.24 mL vs. PCR, 1.08 0.37 mL; P ¼ .04), and TP4 (non-PCR, 3.18 1.23 mL vs. PCR, 0.27 0.09 mL; P ¼ .04). When plotted as magnitude changes between 2 adjacent time points, the biggest volume changes were at the TP2 to TP1
(non-PCR, 6.96 1.47 mL vs. PCR, 20.77 7.36 mL; P ¼ .09). The magnitude differences between the non-PCR and PCR groups were less apparent for breast tumor volume compared with aLN volume.
Association of Clinical Characteristics and Pathologic Outcome With aLN Volume Some patients were observed to have large aLNs on MRI whereas some did not. Figure 4 shows the relation of average node volume for pathologic response, hormone receptor (HR) and growth receptor status. PCR defined as pT0/is pN0. The sample size varied based on available data at each time point, resulting in sample sizes for the non-PCR group of 64 to 84 and for the PCR group of 25 to 31. The PCR group had a relatively larger average nodal volume compared with the non-PCR group (non-PCR, 3.28 0.47 mL vs. PCR, 5.51 1.36 mL; P ¼ .18). Patients negative for estrogen and progesterone receptors had significantly larger nodal volume (negative, 4.98-5.12 mL vs. positive, 2.15-2.22 mL; P ¼ .0024). Nodal volume was similar between patients who were positive and negative for human epidermal growth factor 2 (HER2) (negative, 3.14 0.46 mL vs. positive, 4.58 0.88 mL; P ¼ .18). Three common subtype categories based on receptor expression are: (1) HR-positive (HRþ)/HER2-negative (HER2), (2) HER2-positive
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Axillary Lymph Node Volume and Pathological Complete Responses Figure 3 Axillary Lymph Node Volumes (A) and Breast Tumor Volumes (B) Separated by Pathologic Response to Neoadjuvant Chemotherapy at Different Time Points. The Horizontal Dash Line is 4 Times the Mean Unaffected Contralateral Node Volume (0.60 ± 0.59 mL; N [ 61 Patients [192 Nodes] From a Separate Data Set of Our Hospital Image Database). This Threshold of 3 mL Was Used to Separate Patients With and Without Large Lymph Nodes. PCR Is Defined as pT0/is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31). Significantly Different Compared With Time Point 4 Within Group After False Discovery Rate (FDR) Adjustment of 0.1. Significantly Different Compared With Time Point 3 and 4 Within Group After FDR Adjustment of 0.1. Significantly Different Compared With Time Point 2, 3, and 4 Within Group After FDR Adjustment of 0.1. #Significantly Different Compared With Time Point 4 Less Time Point 3 After FDR Adjustment of 0.1. $Significantly Different Between Non-PCR and PCR Group After FDR Adjustment of 0.1
Abbreviations: PCR ¼ pathologic complete response; TP ¼ time point.
(HER2þ), and (3) triple negative. HER2þ and triple negative subtypes had larger average nodal volume compared with HRþ/ HER2 (4.58-5.48 mL vs. 2.19 mL; P ¼ .033). This finding suggests an association between aLN size and receptor subtypes in breast cancer, although the sample size is small.
Sub-stratification of Patients With Large and Without Large aLN Volume
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Pre-chemotherapy lymph node volume distributions are shown in Figure 5. Ipsilateral nodes were from the ISPY-1 dataset on the same side as the breast cancer. Contralateral nodes were on the side opposite of the cancer from the independent Stony Brook Hospital dataset, and treated as ‘normal.’ There was a wider distribution associated with ipsilateral aLNs. The average aLN volume of contralateral nodes was 0.60 0.59 mL from 192 nodes (61 patients). We defined our threshold separating small and large nodes
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to be 3 mL, which corresponded to 4 times the standard deviation of normal nodes. Figure 6A shows aLN volume at different time points for patients with and without large aLN. PCR is defined as pT0/is pN0. The sample size varied based on available data at each time point, resulting in non-PCR group samples of 64 to 84 and PCR group samples of 25 to 31. For patients with large nodes, PCR group aLN volumes decreased sharply with time, whereas non-PCR aLN volumes decreased gradually. The biggest volume changes were at earlier time points. There was statistical significance between groups at TP4 (nonPCR, 1.68 0.33 mL vs. PCR, 0.65 0.13 mL; P ¼ .02). For patients without large nodes, both PCR and non-PCR aLN volumes showed small changes, with changes less than 1 mL. Note that small nodes were also less reliably measured owing to their small sizes. When plotted as magnitude changes between 2 adjacent time points, patients with large nodes showed larger magnitude changes
Renee F. Cattell et al Figure 4 Comparison of Average Nodal Volume for Pathological Response (A), Hormone and Growth Receptor Status (B), and Common Three-Level Subgroups (C). PCR Is Defined as pT0/is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31). $Significantly Different Between Group After False Discovery Rate Adjustment of 0.1
Abbreviations: ER ¼ estrogen receptor; HER2 ¼ human epidermal growth factor 2; PCR ¼ pathologic complete response; PGR ¼ progesterone receptor; TN ¼ triple negative.
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Axillary Lymph Node Volume and Pathological Complete Responses Figure 5 Pre-chemotherapy Lymph Node Volume for Ipsilateral Nodes Versus Contralateral Nodes. Ipsilateral Nodes Were From the ISPY-1 Dataset on the Same Side as Breast Cancer. Contralateral Nodes Were on the Side Opposite the Breast Cancer From Stony Brook Hospital Dataset. Contralateral Nodes Were Treated as ‘Normal’
for the PCR group compared with the non-PCR group at the TP2TP1, with the PCR group having a larger volume change compared with the non-PCR group (PCR, 8.9 2.9 mL vs. nonPCR, 1.5 0.3 mL; P ¼ .04). Magnitude differences were smaller at later time points of TP3-TP2 (PCR, 2.8 1.1 mL vs. non-PCR, 2.7 0.6 mL; P ¼ .96) and TP4-TP3 (PCR, 0.7 0.5 mL vs. non-PCR, 0.2 0.4 mL; P ¼ .51). Patients without large nodes showed small changes in aLN volumes as expected. Similarly, breast tumor volume with sub-stratification based on patients with and without large aLNs was also quantified (Figure 6B). Breast tumor volumes of the PCR group with and without large aLNs showed sharp decreases in breast tumor volume with time. By contrast, breast tumor volume of the non-PCR group with large nodes decreased only gradually and by smaller magnitude with time (expected for non-PCR), whereas the breast tumor volume of the non-PCR group without large nodes decreased sharply with time (unexpected for non-PCR). Similar conclusions were reached when results are plotted as magnitude changes between 2 adjacent time points. For patients with large nodes, the largest magnitude changes took place early in treatment (non-PCR, 7.80 2.67 mL vs. PCR, 31.59 18.15 mL; P ¼ .33). For patients with no large nodes, magnitude changes remained similar, with early changes in breast tumor volume being different between PCR and non-PCR group (non-PCR, 6.40 1.72 mL vs. PCR, 13.82 3.35 mL; P ¼ .08).
ROC Analysis Model performance was analyzed for prediction of PCR using changes in aLN volume and breast tumor volume independently and combined. PCR is defined as pT0/is pN0. Sample size varied based on available data at each time point, resulting in sample sizes for the non-PCR group of 64 to 84 and for the PCR group of 25 to
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31. Results are summarized in Table 2. Overall, the AUC ranged from 0.50 to 0.87, with the highest AUC predictor for all nodes being the combination of breast tumor and aLN volume (TP2-TP1) (AUC, 0.74), for large nodes being the combination of aLN volume and breast tumor volume (TP2-TP1) (AUC, 0.87), and for nonlarge nodes being the breast tumor volume alone (TP2-TP1) (AUC, 0.70). This indicates that aLN volume was more informative for large node prediction, whereas the breast tumor volume was more informative for patients with smaller nodes. Accuracy ranged from 41% to 90%, with the highest accuracy predictor for all nodes being node volume (TP3-TP1) (accuracy, 77%), for large nodes being combination of node volume and breast tumor volume (TP2-TP1) (accuracy, 90%), and for non-large nodes being the nodal volume (TP3-TP1) (accuracy, 79%). These findings indicated that differentiating between the PCR and non-PCR groups was most accurate for the large nodes at the earliest time point. To highlight some key points in Table 2, Supplemental Figure 1 (in the online version) plotted the ROC AUC, sensitivity and specificity for early and late prediction where breast tumor volume, aLN volume, and the combined breast tumor and aLN volumes were used as predictors of PCR. For all nodes, the AUC of the aLN volume and breast tumor volume were comparable, and combining breast tumor and aLN volumes did not improve the AUC. For large nodes, the AUCs were generally higher compared with those without large nodes and all nodes, and combining breast tumor and aLN volumes improved the AUC.
Discussion This study evaluated whether aLN MRI augments prediction accuracy of treatment response to NAC in a longitudinal study. The rate of change of aLN and breast tumor volume were informative of
Renee F. Cattell et al Figure 6 Sub-stratification of Patients With and Without Large Axillary Lymph Nodes. Axillary Lymph Node Volumes (A) and Breast Tumor Volumes (B) Grouped by Pathologic Response to Neoadjuvant Chemotherapy at Different Time Points. The Horizontal Dash Line is 4 Times the Mean Unaffected Contralateral Node Volume (0.60 ± 0.59 mL; N [ 61 Patients [192 Nodes] From a Separate Data Set of Our Hospital Image Database). A Threshold of 3 mL Was Used to Separate Patients With and Without Large Lymph Nodes. PCR Is Defined as pT0/is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31). Significantly Different Compared With Time Point 4 Within Group After False Discovery Rate (FDR) Adjustment of 0.1. Significantly Different Compared With Time Point 3 and 4 Within Group After FDR Adjustment of 0.1. Significantly Different Compared With Time Point 2, 3, and 4 Within Group After FDR Adjustment of 0.1. #Significantly Different Compared With Time Point 4 to Time Point 3 After FDR Adjustment of 0.1. @Significantly Different Compared to Time Point 3 to Time Point 2 after FDR Adjustment of 0.1. $ Significantly Different Between Non-PCR and PCR group after FDR Adjustment of 0.1
Abbreviations: aLN ¼ axillary lymph node; PCR ¼ pathologic complete response; TP ¼ time point.
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Axillary Lymph Node Volume and Pathological Complete Responses Table 2 Receiver Operating Characteristic Curve Analysis Results for Prediction of PCR, Defined as pT0/is pN0 Volume Change Predictor
AUC
Sensitivity, %
Specificity, %
Accuracy, %
Tumor
0.69
78
58
64
aLN
0.65
61
65
64
Tumor þ aLN
0.74
61
80
74
Tumor
0.68
89
50
61
aLN
0.83
56
100
89
Early prediction (TP2-TP1) All nodes
Large nodes
Non-large nodes
Tumor þ aLN
0.87
67
100
90
Tumor
0.70
79
61
66
aLN
0.57
85
41
52
Tumor þ aLN
0.66
46
88
76
Tumor
0.57
95
27
47
Intermediate prediction (TP3-TP1) All nodes
Large nodes
Non-large nodes
aLN
0.58
24
95
77
Tumor þ aLN
0.63
76
54
60
Tumor
0.52
52
67
56
aLN
0.73
56
89
81
Tumor þ aLN
0.67
56
87
78
Tumor
0.66
83
55
63
aLN
0.57
25
97
79
Tumor þ aLN
0.63
58
69
66
Tumor
0.54
85
34
49
aLN
0.62
38
82
71
Late prediction (TP4-TP1) All nodes
Large nodes
Non-large nodes
Tumor þ aLN
0.61
27
92
73
Tumor
0.50
83
33
50
aLN
0.73
58
83
78
Tumor þ aLN
0.71
67
75
72
Tumor
0.58
93
34
50
aLN
0.60
100
26
41
Tumor þ aLN
0.60
86
33
52
Tumor refers to in-breast tumor. Sample size varied based on available data at each time point resulting in the non-PCR group (n ¼ 64-84) and the PCR group (n ¼ 25-31). Abbreviations: aLN ¼ axillary lymph node; AUC ¼ area under the receiver operating characteristic curve; PCR ¼ pathologic complete response; TP1 ¼ Time Point 1; TP2 ¼ Time Point 2; TP3 ¼ Time Point 3; TP4 ¼ Time Point 4.
pathologic response, with prediction of pathologic response being most informative early in treatment (AUC, 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75). Larger aLN volume was associated with HR negativity, with the largest nodal volume for triple negative subtypes. Initial aLN volume was not indicative of pathologic response. However, sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having an AUC of 0.87.
Temporal Profiles
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Temporal profiles of both aLN and breast tumor volume associated with NAC differed between the PCR and non-PCR groups, with the aLNs being more apparent. These findings suggest that aLN volume is more strongly modulated by NAC. Early changes in aLN volume associated with NAC were more predictive. Another key finding is the marked difference in initial aLN size between the PCR and non-PCR groups. For patients with large
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nodes, the PCR group showed markedly sharper decrease in aLN volumes with time compared with the non-PCR group. For patients without large nodes, the PCR and non-PCR groups showed no appreciable changes in aLN volume with time. This finding confirmed that sub-stratifying patients by aLN size provided clinically useful information. A possible explanation is that large nodes were more likely to be malignant and decrease in size with NAC; however, without individualized nodal pathologic sampling within each patient, it is not possible to discriminate between reactive benign nodes and malignant nodes.
Prediction of PCR We were surprised that aLN volume was similarly predictive of response compared with breast tumor volume. Breast tumor volume predicted PCR with an AUC of w0.7 across different time points for the parent ISPY-1 dataset.9 Our cohort yielded similar AUCs using breast tumor volume. However, the use of aLN
Renee F. Cattell et al volume to predict PCR in this cohort has not been reported previously. For patients with large nodes, the prediction performance of change in breast tumor and aLN volumes combined was better compared with either alone, whereas without sub-stratification of node size, the prediction performance of the combined breast tumor and aLN volumes did not significantly improve. This is not unexpected given that breast tumor volume of non-PCR with non-large nodes decreased sharply with time. For large nodes only, the combined aLN and breast tumor volume substantially improved prediction performance compared with either aLN or breast tumor volume alone, underscoring the notion that large node size is more informative. Changes in volume at the early time points (TP2-TP1) were more predictive of PCR than later time points. In contrast, Hylton et al found prediction of response was slightly better for TP4-TP1, rather than TP2-TP1.9 Although this discrepancy could be owing to differences in patient cohorts and the exact parameters involved, it is possible that the change in volume at the earlier time points could be more predictive because both the PCR and non-PCR groups showed significantly reduced breast tumor volume at the post neoadjuvant time point, and consequently, this was less discriminatory. Of particular interest is the ability to predict response early in treatment to aid personalized treatment planning decisions. Lesion diameter change,17 radiologic complete response,18 and contrast enhancement characteristics19 have been found to be predictors of PCR. Similarly, our study showed that changes in aLN and in-breast lesion volume at an early time point can differentiate between the PCR and non-PCR groups, particularly in patients with large nodes. Axillary imaging in general to assess response has not been wellstudied, and its diagnostic performances vary across modalities. Axillary ultrasound varied in sensitivity from 58% to 86% and specificity from 70% to 100%; MRI and positron emission tomography (PET)/computed tomography (CT) had a 48% to 85% sensitivity and 61% to 95% specificity20 for identifying axillary PCR. Balu-Maestro et al found MRI to be superior to physical examination, mammography, and ultrasound for identifying complete responders.21 A meta-analysis found the AUC of MRI to be higher compared with ultrasonography and mammography, but similar to PET/CT,22 whereas another meta-analysis found PET/ CT to be slightly superior to MRI for predicting response.23 Few studies focused on qualitative radiologic assessment of aLNs. One found conventional imaging to be inadequate in predicting PCR,24 and another conventional imaging could not exclude pathology examination after treatment.25 The predictive value of contrast enhancement and morphologic criteria, such as the size and shape of aLNs, was found to be controversial.26,27 Our study found that aLN initial volume could not discriminate between pathologic response groups to NAC, whereas the rate of volume changes was informative.
Study Limitations A major limitation of this study is that there was no node-specific histopathologic validation of aLNs used in our analysis. Pathology validation of the same node is challenging because aLNs are small and are often clustered together. Nonetheless, a retrospective review under guidance of our breast radiologists confirmed that essentially
all of the largest aLNs were the most suspicious nodes in our cohort. It is possible to use the average volume changes of all aLNs, instead of the largest node, but this would weigh heavily by non-involved aLNs. Another limitation of our study is our arbitrary choice of threshold to separate aLNs into small and large aLNs. With a larger sample size, future studies will employ ROC type analysis to determine the best threshold, with validation on an independent dataset using that threshold. Future studies could include multivariate models to incorporate additional imaging sequences, image analysis (signal enhancement, texture) and non-imaging data (such as race, age, residual cancer burden, and receptor subtypes) as predictors. Additionally, future studies could analyze breast PCR and nodal PCR separately as they may provide valuable information for their respective surgical planning.
Conclusion This study evaluated aLN MRI volumes associated with NAC in patients with breast cancer. Our findings support the hypothesis that aLN MRI offers clinically relevant information and added value to predict treatment response to NAC in patients with breast cancer.
Clinical Practice Points The extent of breast tumor volume reduction by MRI is known
to be associated with pathologic response after NAC. We found that aLN MRI characteristics, such as changes in
volume and rate of changes, are associated with treatment response to NAC. MRI of aLN could minimize the need for unnecessary sentinel biopsy in the context of breast cancer NAC and aid in personalized treatment planning.
Acknowledgments The authors thank Drs Nola Hylton, Gillian Hirst, Christina Yau, and David Newitt for providing additional data from ISPY-1 (ie, detailed pathology, treatment, and survival data) that were not available on the website. The authors also acknowledge the students who have contributed to this project, including Jason Ha, Karamoko Soumahoro, Ankita Katukota, and Nikita Katukota. This work was supported in part by pilot grants from the Stony Brook Cancer Center and a Carol Baldwin pilot grant through the Stony Brook University School of Medicine. The authors also would like to acknowledge the resources of the Advanced Imaging Shared Resource of the Stony Brook Cancer Center and the Biomedical Imaging Research Center (Radiology).
Disclosure The authors have stated that they have no conflicts of interest.
Supplemental Data Supplemental figure accompanying this article can be found in the online version at https://doi.org/10.1016/j.clbc.2019.06.006.
References 1. DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017; 67: 439-48.
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Axillary Lymph Node Volume and Pathological Complete Responses 2. Anderson TL, Glazebrook KN, Murphy BL, Viers LD, Hieken TJ. Cross-sectional imaging to evaluate the extent of regional nodal disease in breast cancer patients undergoing neoadjuvant systemic therapy. Eur J Radiol 2017; 89:163-8. 3. Nagar H, Boothe D, Ginter PS, et al. Disease-free survival according to the use of postmastectomy radiation therapy after neoadjuvant chemotherapy. Clin Breast Cancer 2015; 15:128-34. 4. Rastogi P, Anderson SJ, Bear HD, et al. Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. J Clin Oncol 2008; 26:778-85. 5. van Nijnatten TJA, Goorts B, Vöö S, et al. Added value of dedicated axillary hybrid 18F-FDG PET/MRI for improved axillary nodal staging in clinically node-positive breast cancer patients: a feasibility study. Eur J Nucl Med Mol Imaging 2018; 45: 179-86. 6. Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 2018; 28:582-91. 7. van Nijnatten TJA, Schipper RJ, Lobbes MBI, et al. Diagnostic performance of gadofosveset-enhanced axillary MRI for nodal (re)staging in breast cancer patients: results of a validation study. Clin Radiol 2018; 73:168-75. 8. Fowler AM, Mankoff DA, Joe BN. Imaging neoadjuvant therapy response in breast cancer. Radiology 2017; 285:358-75. 9. Hylton NM, Blume JD, Bernreuter WK, et al, ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy–results from ACRIN 6657/ I-SPY TRIAL. Radiology 2012; 263:663-72. 10. Mattingly AE, Mooney B, Lin HY, et al. Magnetic resonance imaging for axillary breast cancer metastasis in the neoadjuvant setting: a prospective study. Clin Breast Cancer 2017; 17:180-7. 11. Hylton NM, Gatsonis CA, Rosen MA, et al, ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free survival-results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology 2016; 279:44-55. 12. Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26: 1045-57. 13. Newitt DC, Hylton NM. Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive. 2016. Available at: https://wiki.cancerimagingarchive.net/display/Public/ISPY1 #f888892a52a944e9974cd3d6766026c6. Accessed: June 30, 2017. 14. Mukhtar RA, Yau C, Rosen M, et al, I-SPY 1 TRIAL and ACRIN 6657 Investigators. Clinically meaningful tumor reduction rates vary by prechemotherapy
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-
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15.
16. 17. 18. 19. 20. 21. 22.
23. 24. 25. 26. 27.
MRI phenotype and tumor subtype in the I-SPY 1 TRIAL (CALGB 150007/ 150012; ACRIN 6657). Ann Surg Oncol 2013; 20:3823-30. Li W, Arasu V, Newitt DC, et al, ACRIN 6657 Trial Team and I-SPY Investigators Network. Effect of MR imaging contrast thresholds on prediction of neoadjuvant chemotherapy response in breast cancer subtypes: a subgroup analysis of the ACRIN 6657/I-SPY 1 TRIAL. Tomography 2016; 2:378-87. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 1995:289-300. Minarikova L, Bogner W, Pinker K, et al. Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 2017; 27:1901-11. Goorts B, Dreuning KMA, Houwers JB, et al. MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer. Breast Cancer Res 2018; 20:34. Tudorica A, Oh KY, Chui SY, et al. Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. Transl Oncol 2016; 9:8-17. Schipper RJ, Moossdorff M, Beets-Tan RGH, Smidt ML, Lobbes MBI. Noninvasive nodal restaging in clinically node positive breast cancer patients after neoadjuvant systemic therapy: a systematic review. Eur J Radiol 2015; 84:41-7. Balu-Maestro C, Chapellier C, Bleuse A, Chanalet I, Chauvel C, Largillier R. Imaging in evaluation of response to neoadjuvant breast cancer treatment benefits of MRI. Breast Cancer Res Treat 2002; 72:145-52. Gu YL, Pan SM, Ren J, Yang ZX, Jiang GQ. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: a meta-analysis. Clin Breast Cancer 2017; 17:245-55. Chen L, Yang Q, Bao J, Liu D, Huang X, Wang J. Direct comparison of PET/CT and MRI to predict the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Sci Rep 2017; 7:8479. Weber JJ, Jochelson MS, Eaton A, et al. MRI and prediction of pathologic complete response in the breast and axilla after neoadjuvant chemotherapy for breast cancer. J Am Coll Surg 2017; 225:740-6. Hieken TJ, Boughey JC, Jones KN, Shah SS, Glazebrook KN. Imaging response and residual metastatic axillary lymph node disease after neoadjuvant chemotherapy for primary breast cancer. Ann Surg Oncol 2013; 20:3199-204. Kvistad KA, Rydland J, Smethurst HB, Lundgren S, Fjøsne HE, Haraldseth O. Axillary lymph node metastases in breast cancer: preoperative detection with dynamic contrast-enhanced MRI. Eur Radiol 2000; 10:1464-71. Mortellaro VE, Marshall J, Singer L, et al. Magnetic resonance imaging for axillary staging in patients with breast cancer. J Magn Reson Imaging 2009; 30:309-12.
Renee F. Cattell et al Supplemental Data Supplemental Figure 1 Receiver Operating Characteristic Curve Analysis for Change in Breast Tumor Volume Alone, Changes in aLN Volume Alone, and Changes in Both Breast Tumor and aLN Volumes for 2 TPs. AUC, Sensitivity, and Specificity are Plotted for Changes for Early Prediction (TP2-TP1) and Late Prediction (TP4-TP1). Sub-stratification Was Done for all Nodes (Blue), Large aLN (Yellow), and Non-large aLN (Black/White). A Threshold of 3 mL Was Used to Separate Patients With and Without Large Lymph Nodes. PCR is Defined as pT0/Is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31)
Abbreviations: aLN ¼ axillary lymph node; AUC ¼ area under the receiver operating characteristic curve; TP ¼ time point.
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MRI Volume Changes of Axillary Lymph Nodes as Predictor of Pathologic Complete Responses to Neoadjuvant Chemotherapy in Breast Cancer Renee F. Cattell,1,2 James J. Kang,1 Thomas Ren,1 Pauline B. Huang,1 Ashima Muttreja,1 Sarah Dacosta,1 Haifang Li,1 Lea Baer,3 Sean Clouston,4 Roxanne Palermo,1 Paul Fisher,1 Cliff Bernstein,1 Jules A. Cohen,3 Tim Q. Duong1 Abstract Early prediction of pathologic response can impact patient management and treatment planning for patients with breast cancer. This study analyzed patients with stage 2 or 3 breast cancer undergoing neoadjuvant chemotherapy (N [ 132). Axillary lymph node initial volume could not discriminate between patients who achieved pathologic complete response and those who did not, whereas the rate of volume changes was informative. Introduction: Longitudinal monitoring of breast tumor volume over the course of chemotherapy is informative of pathologic response. This study aims to determine whether axillary lymph node (aLN) volume by magnetic resonance imaging (MRI) could augment the prediction accuracy of treatment response to neoadjuvant chemotherapy (NAC). Materials and Methods: Level-2a curated data from the I-SPY-1 TRIAL (2002-2006) were used. Patients had stage 2 or 3 breast cancer. MRI was acquired pre-, during, and post-NAC. A subset with visible aLNs on MRI was identified (N ¼ 132). Prediction of pathologic complete response (PCR) was made using breast tumor volume changes, nodal volume changes, and combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes (3 mL or w1.79 cm diameter cutoff). Receiver operating characteristic curve analysis was used to quantify prediction performance. Results: The rate of change of aLN and breast tumor volume were informative of pathologic response, with prediction being most informative early in treatment (area under the curve (AUC), 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75). Larger aLN volume was associated with hormone receptor negativity, with the largest nodal volume for triple negative subtypes. Sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having AUC of 0.87. Conclusion: aLN MRI offers clinically relevant information and has the potential to predict treatment response to NAC in patients with breast cancer. Clinical Breast Cancer, Vol. -, No. -, --- ª 2019 Elsevier Inc. All rights reserved. Keywords: Breast tumor volume, Dynamic contrast-enhanced MRI, Magnetic resonance imaging, Molecular subtypes, Sentinel lymph node biopsy
Introduction Breast cancer becomes most concerning when it metastasizes to axillary lymph nodes (aLNs).1 aLN involvement also increases 1 Department of Radiology, Stony Brook University School of Medicine, Stony Brook, NY 2 Department of Biomedical Engineering 3 Department of Medical Oncology 4 Department of Preventive Medicine and Population Health, Stony Brook University, Stony Brook, NY
Submitted: Jan 17, 2019; Revised: May 24, 2019; Accepted: Jun 13, 2019 Address for correspondence: Tim Q. Duong, PhD, Department of Radiology, Stony Brook University Hospital, 101 Nicolls Rd, Stony Brook, NY 11794 E-mail contact: [email protected]
1526-8209/$ - see frontmatter ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.clbc.2019.06.006
the risk of recurrence.2,3 For patients with nodal metastasis, disease-free survival at 8 years and 5 years decreases from 70% to 40%,4 and 98% to 85%,5 respectively. As a result, aLN characteristics are critical for diagnosis, prognosis, and monitoring treatment.6 Although aLN management has become less invasive with the introduction of sentinel biopsy as opposed to aLN dissection, there remain significant side effects including shoulder dysfunction, lymphedema, and nerve damage in as much as one-fourth of patients.6,7 Moreover, > 70% of biopsied sentinel aLNs are negative,7 highlighting significant unnecessary risks. Accurate non-invasive imaging of nodal involvement has the potential to avoid unnecessary invasive aLN biopsy and dissection while offering a predictive imaging biomarker for neoadjuvant chemotherapy (NAC).
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Axillary Lymph Node Volume and Pathological Complete Responses Breast magnetic resonance imaging (MRI) is a standard part of evaluating NAC response because it can reliably identify residual inbreast disease (positive predictive value of 93%).8 It is, however, less successful at predicting the absence of disease (negative predictive value of 65%)8 and pathologic complete response (PCR) (area under curve [AUC]: w0.7-0.8).9 MRI of aLN has the potential to improve prediction of PCR, and the Response Evaluation Criteria of Solid Tumors (RECIST) recommend careful evaluation of the aLN in assessing response to therapy in breast cancer.10 To date, efforts to utilize aLN MRI has been limited by small aLN sizes, breast coil sensitivity in regions near aLNs, and exclusion of aLNs from field of view. Although nodal morphologic features on MRI are predictive of malignancy,2,10 the use of aLN MRI to predict treatment response to NAC has not been adequately studied. The goal of this study was to determine whether aLN MRI could augment the prediction accuracy of treatment response to NAC in a longitudinal manner. We utilized a subset of the ISPY-1 (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis) clinical trial data.9,11 The volume of aLNs were quantified as a function of pre-, concomitant, and post-NAC, and pathologic response, and stratified by aLN size. The AUC was used to evaluate the efficacy of breast and/or aLN volume to predict PCR. We tested the hypothesis that changes in aLN volume in conjunction with breast tumor volume can more accurately predict PCR to NAC. If aLN volume and/or rate of change of volume are found to be associated with presence of disease or treatment response, it could change clinical practice. This could have immediate clinical value because aLN volume can be readily measured by MRI.
Materials and Methods Patient Cohort
2
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The full ISPY-1 TRIAL (2002-2006) had 221 participants. The current study utilizes the Level 2a curated dataset, which had 207 participants.9,11-13 Of the 207 participants, 132 participants had usable nodal MRI data as determined by agreements by 2 independent observers that aLN volume could be reliably contoured. The typical causes for exclusion were aLNs were out of field of view, were obscured by respiratory motion artifacts or saturation pulses to remove respiratory artifacts, and/or had limited visibility owing to breast coil sensitivity. Previous reports using this open dataset (ISPY-1) focused on MRI in-breast tumor volume to predict PCR,9,11,14,15 but did not analyze nodal MRI information. Our study focused on evaluating the contribution of the aLN MRI volume in the prediction of PCR. All patients had stage 2 or 3 breast cancer with breast tumors 3 cm in size, and underwent anthracycline-cyclophosphamide (AC) with or without taxane treatment. MRI was acquired pre-NAC (time point 1 [TP1]), w2 weeks after the first cycle of AC (TP2), after all AC was administered but prior to taxane if any (TP3), and post-NAC and before surgery (TP4). In our cohort, 4 subjects received only AC, but no taxane. Data were analyzed for response to neoadjuvant therapy with respect to the PCR, defined as no residual invasive disease in breast or node after NAC based on final surgical pathology (pT0/is pN0) (non-PCR, n ¼ 92; PCR, n ¼ 35). Relation of nodal and tumor volume was also analyzed with respective to breast PCR (pT0/is), defined as no residual
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invasive disease in the breast regardless of aLN status (non-PCR, n ¼ 82; PCR, n ¼ 44), and nodal PCR (pN0), defined as no residual invasive disease in the aLN regardless of breast status (nonPCR, n ¼ 63; PCR, n ¼ 60). Patients who did or did not achieve PCR are labeled as PCR or non-PCR, respectively. We note that sample sizes varied slightly for each time point, given that aLN was not consistently visible on the MR image. A second cohort were used to quantify normal aLN volume. We performed a retrospective study from the Stony Brook Hospital imaging database from January 1, 2012 to July 30, 2018 with Institutional Review Board approval. These patients were newly diagnosed patients with locally advanced unilateral breast cancer with visible aLNs on a pre-chemotherapy standard breast MRI, similar to ISPY-1.
MRI Analysis MRI of this multicenter study was performed on 1.5-T scanners using a standard dedicated clinical breast protocol.9 All visible aLNs on the first post-contrast image (2 minutes post-contrast MRI) were manually segmented (ITK-SNAP, http://www.itksnap.org/pmwiki/ pmwiki.php) on TP1. The same aLNs were carefully identified on TP2 through TP4 based on anatomical landmarks. For consistency, the largest node pre-treatment per patient was identified to follow over time. Although individualized node-specific histopathologic validation was not available, retrospective review under guidance of our breast radiologists confirmed essentially all (95%) of the largest aLNs were the most suspicious for malignancy. The breast tumor volumes, as reported in the ISPY-1 dataset,9,11 were also analyzed. Breast tumor and nodal volumes of the PCR and non-PCR groups were plotted for each treatment time point (TP1, TP2, TP3, and TP4). Absolute (as well as %) breast tumor and nodal volume changes between 2 adjacent time points (TP2-TP1, TP3TP2, and TP4-TP3) were also plotted for the PCR and non-PCR groups. Similar conclusions were reached for absolute and % changes, so % changes were not reported. In addition, volume characteristics were sub-stratified by patients with and without large aLN, using the threshold of 3 mL (equivalent to w1.79 cm diameter, assuming spheres). This threshold was 4 times the standard deviation across nodes of the average unaffected node volume (0.60 0.59 mL), which was obtained from the contralateral side of patients with unilateral breast cancer of an independent group of 61 patients (192 nodes) at our 1.5 T MRI scanner. Prediction of PCR (pT0/is pN0) was made using breast tumor volume changes, nodal volume changes, and the combined breast tumor and nodal volume changes with sub-stratification with and without large lymph nodes.
Statistical Analysis Analyses examining differences in outcomes across different time points used unpaired 2-tailed t tests with unequal variance. Multivariable prediction models used the generalized linear modeling with logistic regression. Receiver operating characteristic (ROC) curve analysis was performed with PCR as ground truth. AUC, sensitivity, specificity, and accuracy were tabulated. The Youden index was used for determining the optimal cut-point. Matlab (R2017b, MathWorks, Natick, MA) was used for all statistical analyses. A P-value of .05 with multiple-comparison correction16 was used to determine statistical significance.
Renee F. Cattell et al Table 1 Patient Demographics Lymph Node Dataset (n [ 132), n (%)
ISPY-1 (n [ 221), n (%)
48.96 9.29
48.25 8.90
Caucasian
97 (73.5)
165 (74.7)
African American
28 (21.2)
42 (19.0)
Asian
6 (4.5)
9 (4.0)
Native Hawaiian/Pacific Islander
0 (0.0)
1 (0.5)
Multiple race
1 (0.8)
2 (0.9)
Missing
0 (0.0)
2 (0.9)
Mean age, y (standard deviation) Race
Estrogen receptor status Negative
54 (40.9)
94 (42.5)
Positive
76 (57.6)
125 (56.6)
Missing
2 (1.5)
2 (0.9)
Negative
66 (50.0)
115 (52.0)
Positive
64 (48.5)
104 (47.1)
Missing
2 (1.5)
2 (0.9)
Negative
81 (61.4)
149 (67.4)
Positive
47 (35.6)
67 (30.3)
Missing
4 (3.0)
5 (2.3)
HRþ/HER2
57 (43.2)
96 (43.4)
HER2þ
47 (35.6)
67 (30.3)
Triple negative
24 (18.2)
53 (24.0)
4 (3.0)
5 (2.3)
Unilateral
130 (98.5)
217 (98.2)
Bilateral
2 (1.5)
4 (1.8)
Progesterone receptor status
HER2 status
3-Level HR/HER2 category
Missing Cancer laterality
Tumor laterality Left
60 (45.5)
107 (48.4)
Right
72 (54.5)
114 (51.6)
No
92 (69.7)
157 (71.1)
Yes
35 (26.5)
58 (26.2)
5 (3.8)
6 (2.7)
No
82 (62.1)
146 (66.1)
Yes
44 (33.3)
69 (31.2)
6 (4.6)
6 (2.7)
No
63 (47.7)
97 (43.9)
Yes
60 (45.5)
99 (44.8)
9 (6.8)
25 (11.3)
Full pathologic complete response (pT0/is pN0)
Missing Breast pathologic complete response (pT0/is)
Missing Nodal pathologic complete response (pN0)
Missing
None of the demographic data of our sub-cohort differed significantly from the parent data by proportional test (P > .05). Abbreviations: HER2 ¼ human epidermal growth factor receptor 2; HR ¼ hormone receptor.
Results Dataset Characteristics Of the 207 patients in the parent Level 2a ISPY-1 dataset, 132 (64%) patients had usable aLN MRI quality. There were 119, 103,
99, and 114 patients with acceptable MRI data of the nodes at TP1, TP2, TP3, and TP4. There were 93, 89, and 102 patients with acceptable MRI data of the nodes at TP2-TP1, TP3-TP1, and TP4-TP1.
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Axillary Lymph Node Volume and Pathological Complete Responses Demographic and receptor subtype data (Table 1) were not significantly different between this subset and the parent dataset as determined by proportion tests (P > .05).
aLN and Breast Tumor Volume Profiles Representative images pre- and post-NAC from a patient with large nodes and a patient without large nodes are shown in Figure 1. aLN had a variety of sizes, shapes, and heterogeneous enhancement. Most of the large nodes were clearly modulated by NAC. Figure 2A shows average aLN volumes at different time points, with separation into the PCR and non-PCR groups using definitions of full, breast, and nodal PCR. At each individual time point, whether separated by full, breast, or nodal PCR, there was no difference among the averages (P > .05). Similar results are shown in Figure 2B with breast tumor volume. Given that full, breast, and nodal PCR have similar trends, moving forward, we report results
only for full PCR, defined as no invasive disease in the breast and axillary nodes at final pathology (pT0/is pN0). We note that sample sizes for the PCR and non-PCR groups varied owing to missing aLN volume data. The non-PCR group sample for full, breast, and nodal ranged from 64 to 84, 57 to 74, and 44 to 58, respectively. The PCR group sample for full, breast, and nodal ranged from 25 to 31, 32 to 39, and 43 to 54, respectively. Figure 3A shows average aLN volumes at different time points. As mentioned above, we use full PCR (pT0/is pN0) moving forward, for which sample sizes varied across time for the non-PCR (n ¼ 64-84) and PCR (n ¼ 25-31) groups. The PCR group aLN volume was relatively large at TP1, whereas the non-PCR aLN volume was comparatively smaller (non-PCR, 3.28 0.47 mL vs. PCR, 5.51 1.36 mL; P ¼ .18) at the first time point. Non-PCR aLN volume decreased more gradually with time compared with the PCR group. Both the PCR and non-PCR group aLN volume at
Figure 1 Representative Magnetic Resonance Imaging of the Breast and Axillary Lymph Nodes. A Patient Without Large Nodes (A) and a Patient With Large Nodes (B) are Shown. Data Were Obtained at (Early) 2 Minutes Post Contrast Magnetic Resonance Imaging. Breast Lesion Is Hyperintense. Lymph Nodes Are of Various Size and Shape. Thin Red Arrows Point to Representative Small Nodes, and Thick Red Arrows Point to Representative Large Nodes. The Patient in (A) Was 49 Years of Age at Breast Cancer Diagnosis and Achieved Pathologic Complete Response. The Patient in (B) Was 53 Years of Age at Breast Cancer Diagnosis and did Not Achieve a Pathologic Complete Response
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Renee F. Cattell et al Figure 2 Average Volume of Axillary Lymph Nodes (A) and Breast Tumor (B) Over Time With Separation Into Full, Breast, and Nodal PCR. We Note That Sample Sizes for PCR and Non-PCR Groups Varied due to Missing Axillary Lymph Node Volume Data. The Non-PCR Group Sample for Full, Breast, and Nodal Volume Ranged From 64 to 84, 57 to 74, and 44 to 58, Respectively. The PCR Group Sample for Full, Breast, and Nodal Volume Ranged From 25 to 31, 32 to 39, and 43 to 54, Respectively
Abbreviation: PCR ¼ pathologic complete response.
TP1 was significantly different from TP2, TP3, and TP4. There was a significant difference at TP4 between the PCR and non-PCR groups, with the non-PCR group having a larger volume (nonPCR, 0.88 0.14 mL vs. PCR, 0.53 0.06 mL; P ¼ .04). When plotted as magnitude changes between 2 adjacent time points, the biggest difference in volume change were from TP2 to TP1, with the PCR group reducing in volume by a significantly larger amount (non-PCR, 0.86 0.15 mL vs. PCR, 3.93 1.42 mL; P ¼ .07). Figure 3B shows the breast tumor volumes at different time points. The PCR and non-PCR groups’ breast tumor volumes were similar at the first time point (non-PCR, 25.18 2.69 mL vs. PCR, 27.77 6.07 mL; P ¼ .69). However, the tumor volume of the non-PCR group decreased very gradually with time. Both the PCR and non-PCR group breast tumor volumes at TP1 were significantly different from TP2, TP3, and TP4. Breast tumor volumes were statistically different between the non-PCR and PCR groups at TP2 (non-PCR, 18.10 2.55 mL vs. PCR, 9.32 2.11 mL; P ¼ .02), TP3 (non-PCR, 8.68 3.24 mL vs. PCR, 1.08 0.37 mL; P ¼ .04), and TP4 (non-PCR, 3.18 1.23 mL vs. PCR, 0.27 0.09 mL; P ¼ .04). When plotted as magnitude changes between 2 adjacent time points, the biggest volume changes were at the TP2 to TP1
(non-PCR, 6.96 1.47 mL vs. PCR, 20.77 7.36 mL; P ¼ .09). The magnitude differences between the non-PCR and PCR groups were less apparent for breast tumor volume compared with aLN volume.
Association of Clinical Characteristics and Pathologic Outcome With aLN Volume Some patients were observed to have large aLNs on MRI whereas some did not. Figure 4 shows the relation of average node volume for pathologic response, hormone receptor (HR) and growth receptor status. PCR defined as pT0/is pN0. The sample size varied based on available data at each time point, resulting in sample sizes for the non-PCR group of 64 to 84 and for the PCR group of 25 to 31. The PCR group had a relatively larger average nodal volume compared with the non-PCR group (non-PCR, 3.28 0.47 mL vs. PCR, 5.51 1.36 mL; P ¼ .18). Patients negative for estrogen and progesterone receptors had significantly larger nodal volume (negative, 4.98-5.12 mL vs. positive, 2.15-2.22 mL; P ¼ .0024). Nodal volume was similar between patients who were positive and negative for human epidermal growth factor 2 (HER2) (negative, 3.14 0.46 mL vs. positive, 4.58 0.88 mL; P ¼ .18). Three common subtype categories based on receptor expression are: (1) HR-positive (HRþ)/HER2-negative (HER2), (2) HER2-positive
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Axillary Lymph Node Volume and Pathological Complete Responses Figure 3 Axillary Lymph Node Volumes (A) and Breast Tumor Volumes (B) Separated by Pathologic Response to Neoadjuvant Chemotherapy at Different Time Points. The Horizontal Dash Line is 4 Times the Mean Unaffected Contralateral Node Volume (0.60 ± 0.59 mL; N [ 61 Patients [192 Nodes] From a Separate Data Set of Our Hospital Image Database). This Threshold of 3 mL Was Used to Separate Patients With and Without Large Lymph Nodes. PCR Is Defined as pT0/is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31). Significantly Different Compared With Time Point 4 Within Group After False Discovery Rate (FDR) Adjustment of 0.1. Significantly Different Compared With Time Point 3 and 4 Within Group After FDR Adjustment of 0.1. Significantly Different Compared With Time Point 2, 3, and 4 Within Group After FDR Adjustment of 0.1. #Significantly Different Compared With Time Point 4 Less Time Point 3 After FDR Adjustment of 0.1. $Significantly Different Between Non-PCR and PCR Group After FDR Adjustment of 0.1
Abbreviations: PCR ¼ pathologic complete response; TP ¼ time point.
(HER2þ), and (3) triple negative. HER2þ and triple negative subtypes had larger average nodal volume compared with HRþ/ HER2 (4.58-5.48 mL vs. 2.19 mL; P ¼ .033). This finding suggests an association between aLN size and receptor subtypes in breast cancer, although the sample size is small.
Sub-stratification of Patients With Large and Without Large aLN Volume
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Pre-chemotherapy lymph node volume distributions are shown in Figure 5. Ipsilateral nodes were from the ISPY-1 dataset on the same side as the breast cancer. Contralateral nodes were on the side opposite of the cancer from the independent Stony Brook Hospital dataset, and treated as ‘normal.’ There was a wider distribution associated with ipsilateral aLNs. The average aLN volume of contralateral nodes was 0.60 0.59 mL from 192 nodes (61 patients). We defined our threshold separating small and large nodes
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to be 3 mL, which corresponded to 4 times the standard deviation of normal nodes. Figure 6A shows aLN volume at different time points for patients with and without large aLN. PCR is defined as pT0/is pN0. The sample size varied based on available data at each time point, resulting in non-PCR group samples of 64 to 84 and PCR group samples of 25 to 31. For patients with large nodes, PCR group aLN volumes decreased sharply with time, whereas non-PCR aLN volumes decreased gradually. The biggest volume changes were at earlier time points. There was statistical significance between groups at TP4 (nonPCR, 1.68 0.33 mL vs. PCR, 0.65 0.13 mL; P ¼ .02). For patients without large nodes, both PCR and non-PCR aLN volumes showed small changes, with changes less than 1 mL. Note that small nodes were also less reliably measured owing to their small sizes. When plotted as magnitude changes between 2 adjacent time points, patients with large nodes showed larger magnitude changes
Renee F. Cattell et al Figure 4 Comparison of Average Nodal Volume for Pathological Response (A), Hormone and Growth Receptor Status (B), and Common Three-Level Subgroups (C). PCR Is Defined as pT0/is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31). $Significantly Different Between Group After False Discovery Rate Adjustment of 0.1
Abbreviations: ER ¼ estrogen receptor; HER2 ¼ human epidermal growth factor 2; PCR ¼ pathologic complete response; PGR ¼ progesterone receptor; TN ¼ triple negative.
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Axillary Lymph Node Volume and Pathological Complete Responses Figure 5 Pre-chemotherapy Lymph Node Volume for Ipsilateral Nodes Versus Contralateral Nodes. Ipsilateral Nodes Were From the ISPY-1 Dataset on the Same Side as Breast Cancer. Contralateral Nodes Were on the Side Opposite the Breast Cancer From Stony Brook Hospital Dataset. Contralateral Nodes Were Treated as ‘Normal’
for the PCR group compared with the non-PCR group at the TP2TP1, with the PCR group having a larger volume change compared with the non-PCR group (PCR, 8.9 2.9 mL vs. nonPCR, 1.5 0.3 mL; P ¼ .04). Magnitude differences were smaller at later time points of TP3-TP2 (PCR, 2.8 1.1 mL vs. non-PCR, 2.7 0.6 mL; P ¼ .96) and TP4-TP3 (PCR, 0.7 0.5 mL vs. non-PCR, 0.2 0.4 mL; P ¼ .51). Patients without large nodes showed small changes in aLN volumes as expected. Similarly, breast tumor volume with sub-stratification based on patients with and without large aLNs was also quantified (Figure 6B). Breast tumor volumes of the PCR group with and without large aLNs showed sharp decreases in breast tumor volume with time. By contrast, breast tumor volume of the non-PCR group with large nodes decreased only gradually and by smaller magnitude with time (expected for non-PCR), whereas the breast tumor volume of the non-PCR group without large nodes decreased sharply with time (unexpected for non-PCR). Similar conclusions were reached when results are plotted as magnitude changes between 2 adjacent time points. For patients with large nodes, the largest magnitude changes took place early in treatment (non-PCR, 7.80 2.67 mL vs. PCR, 31.59 18.15 mL; P ¼ .33). For patients with no large nodes, magnitude changes remained similar, with early changes in breast tumor volume being different between PCR and non-PCR group (non-PCR, 6.40 1.72 mL vs. PCR, 13.82 3.35 mL; P ¼ .08).
ROC Analysis Model performance was analyzed for prediction of PCR using changes in aLN volume and breast tumor volume independently and combined. PCR is defined as pT0/is pN0. Sample size varied based on available data at each time point, resulting in sample sizes for the non-PCR group of 64 to 84 and for the PCR group of 25 to
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31. Results are summarized in Table 2. Overall, the AUC ranged from 0.50 to 0.87, with the highest AUC predictor for all nodes being the combination of breast tumor and aLN volume (TP2-TP1) (AUC, 0.74), for large nodes being the combination of aLN volume and breast tumor volume (TP2-TP1) (AUC, 0.87), and for nonlarge nodes being the breast tumor volume alone (TP2-TP1) (AUC, 0.70). This indicates that aLN volume was more informative for large node prediction, whereas the breast tumor volume was more informative for patients with smaller nodes. Accuracy ranged from 41% to 90%, with the highest accuracy predictor for all nodes being node volume (TP3-TP1) (accuracy, 77%), for large nodes being combination of node volume and breast tumor volume (TP2-TP1) (accuracy, 90%), and for non-large nodes being the nodal volume (TP3-TP1) (accuracy, 79%). These findings indicated that differentiating between the PCR and non-PCR groups was most accurate for the large nodes at the earliest time point. To highlight some key points in Table 2, Supplemental Figure 1 (in the online version) plotted the ROC AUC, sensitivity and specificity for early and late prediction where breast tumor volume, aLN volume, and the combined breast tumor and aLN volumes were used as predictors of PCR. For all nodes, the AUC of the aLN volume and breast tumor volume were comparable, and combining breast tumor and aLN volumes did not improve the AUC. For large nodes, the AUCs were generally higher compared with those without large nodes and all nodes, and combining breast tumor and aLN volumes improved the AUC.
Discussion This study evaluated whether aLN MRI augments prediction accuracy of treatment response to NAC in a longitudinal study. The rate of change of aLN and breast tumor volume were informative of
Renee F. Cattell et al Figure 6 Sub-stratification of Patients With and Without Large Axillary Lymph Nodes. Axillary Lymph Node Volumes (A) and Breast Tumor Volumes (B) Grouped by Pathologic Response to Neoadjuvant Chemotherapy at Different Time Points. The Horizontal Dash Line is 4 Times the Mean Unaffected Contralateral Node Volume (0.60 ± 0.59 mL; N [ 61 Patients [192 Nodes] From a Separate Data Set of Our Hospital Image Database). A Threshold of 3 mL Was Used to Separate Patients With and Without Large Lymph Nodes. PCR Is Defined as pT0/is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31). Significantly Different Compared With Time Point 4 Within Group After False Discovery Rate (FDR) Adjustment of 0.1. Significantly Different Compared With Time Point 3 and 4 Within Group After FDR Adjustment of 0.1. Significantly Different Compared With Time Point 2, 3, and 4 Within Group After FDR Adjustment of 0.1. #Significantly Different Compared With Time Point 4 to Time Point 3 After FDR Adjustment of 0.1. @Significantly Different Compared to Time Point 3 to Time Point 2 after FDR Adjustment of 0.1. $ Significantly Different Between Non-PCR and PCR group after FDR Adjustment of 0.1
Abbreviations: aLN ¼ axillary lymph node; PCR ¼ pathologic complete response; TP ¼ time point.
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Axillary Lymph Node Volume and Pathological Complete Responses Table 2 Receiver Operating Characteristic Curve Analysis Results for Prediction of PCR, Defined as pT0/is pN0 Volume Change Predictor
AUC
Sensitivity, %
Specificity, %
Accuracy, %
Tumor
0.69
78
58
64
aLN
0.65
61
65
64
Tumor þ aLN
0.74
61
80
74
Tumor
0.68
89
50
61
aLN
0.83
56
100
89
Early prediction (TP2-TP1) All nodes
Large nodes
Non-large nodes
Tumor þ aLN
0.87
67
100
90
Tumor
0.70
79
61
66
aLN
0.57
85
41
52
Tumor þ aLN
0.66
46
88
76
Tumor
0.57
95
27
47
Intermediate prediction (TP3-TP1) All nodes
Large nodes
Non-large nodes
aLN
0.58
24
95
77
Tumor þ aLN
0.63
76
54
60
Tumor
0.52
52
67
56
aLN
0.73
56
89
81
Tumor þ aLN
0.67
56
87
78
Tumor
0.66
83
55
63
aLN
0.57
25
97
79
Tumor þ aLN
0.63
58
69
66
Tumor
0.54
85
34
49
aLN
0.62
38
82
71
Late prediction (TP4-TP1) All nodes
Large nodes
Non-large nodes
Tumor þ aLN
0.61
27
92
73
Tumor
0.50
83
33
50
aLN
0.73
58
83
78
Tumor þ aLN
0.71
67
75
72
Tumor
0.58
93
34
50
aLN
0.60
100
26
41
Tumor þ aLN
0.60
86
33
52
Tumor refers to in-breast tumor. Sample size varied based on available data at each time point resulting in the non-PCR group (n ¼ 64-84) and the PCR group (n ¼ 25-31). Abbreviations: aLN ¼ axillary lymph node; AUC ¼ area under the receiver operating characteristic curve; PCR ¼ pathologic complete response; TP1 ¼ Time Point 1; TP2 ¼ Time Point 2; TP3 ¼ Time Point 3; TP4 ¼ Time Point 4.
pathologic response, with prediction of pathologic response being most informative early in treatment (AUC, 0.57-0.87) compared with later in treatment (AUC, 0.50-0.75). Larger aLN volume was associated with HR negativity, with the largest nodal volume for triple negative subtypes. Initial aLN volume was not indicative of pathologic response. However, sub-stratification by node size improved predictive performance, with the best predictive model for large nodes having an AUC of 0.87.
Temporal Profiles
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Temporal profiles of both aLN and breast tumor volume associated with NAC differed between the PCR and non-PCR groups, with the aLNs being more apparent. These findings suggest that aLN volume is more strongly modulated by NAC. Early changes in aLN volume associated with NAC were more predictive. Another key finding is the marked difference in initial aLN size between the PCR and non-PCR groups. For patients with large
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nodes, the PCR group showed markedly sharper decrease in aLN volumes with time compared with the non-PCR group. For patients without large nodes, the PCR and non-PCR groups showed no appreciable changes in aLN volume with time. This finding confirmed that sub-stratifying patients by aLN size provided clinically useful information. A possible explanation is that large nodes were more likely to be malignant and decrease in size with NAC; however, without individualized nodal pathologic sampling within each patient, it is not possible to discriminate between reactive benign nodes and malignant nodes.
Prediction of PCR We were surprised that aLN volume was similarly predictive of response compared with breast tumor volume. Breast tumor volume predicted PCR with an AUC of w0.7 across different time points for the parent ISPY-1 dataset.9 Our cohort yielded similar AUCs using breast tumor volume. However, the use of aLN
Renee F. Cattell et al volume to predict PCR in this cohort has not been reported previously. For patients with large nodes, the prediction performance of change in breast tumor and aLN volumes combined was better compared with either alone, whereas without sub-stratification of node size, the prediction performance of the combined breast tumor and aLN volumes did not significantly improve. This is not unexpected given that breast tumor volume of non-PCR with non-large nodes decreased sharply with time. For large nodes only, the combined aLN and breast tumor volume substantially improved prediction performance compared with either aLN or breast tumor volume alone, underscoring the notion that large node size is more informative. Changes in volume at the early time points (TP2-TP1) were more predictive of PCR than later time points. In contrast, Hylton et al found prediction of response was slightly better for TP4-TP1, rather than TP2-TP1.9 Although this discrepancy could be owing to differences in patient cohorts and the exact parameters involved, it is possible that the change in volume at the earlier time points could be more predictive because both the PCR and non-PCR groups showed significantly reduced breast tumor volume at the post neoadjuvant time point, and consequently, this was less discriminatory. Of particular interest is the ability to predict response early in treatment to aid personalized treatment planning decisions. Lesion diameter change,17 radiologic complete response,18 and contrast enhancement characteristics19 have been found to be predictors of PCR. Similarly, our study showed that changes in aLN and in-breast lesion volume at an early time point can differentiate between the PCR and non-PCR groups, particularly in patients with large nodes. Axillary imaging in general to assess response has not been wellstudied, and its diagnostic performances vary across modalities. Axillary ultrasound varied in sensitivity from 58% to 86% and specificity from 70% to 100%; MRI and positron emission tomography (PET)/computed tomography (CT) had a 48% to 85% sensitivity and 61% to 95% specificity20 for identifying axillary PCR. Balu-Maestro et al found MRI to be superior to physical examination, mammography, and ultrasound for identifying complete responders.21 A meta-analysis found the AUC of MRI to be higher compared with ultrasonography and mammography, but similar to PET/CT,22 whereas another meta-analysis found PET/ CT to be slightly superior to MRI for predicting response.23 Few studies focused on qualitative radiologic assessment of aLNs. One found conventional imaging to be inadequate in predicting PCR,24 and another conventional imaging could not exclude pathology examination after treatment.25 The predictive value of contrast enhancement and morphologic criteria, such as the size and shape of aLNs, was found to be controversial.26,27 Our study found that aLN initial volume could not discriminate between pathologic response groups to NAC, whereas the rate of volume changes was informative.
Study Limitations A major limitation of this study is that there was no node-specific histopathologic validation of aLNs used in our analysis. Pathology validation of the same node is challenging because aLNs are small and are often clustered together. Nonetheless, a retrospective review under guidance of our breast radiologists confirmed that essentially
all of the largest aLNs were the most suspicious nodes in our cohort. It is possible to use the average volume changes of all aLNs, instead of the largest node, but this would weigh heavily by non-involved aLNs. Another limitation of our study is our arbitrary choice of threshold to separate aLNs into small and large aLNs. With a larger sample size, future studies will employ ROC type analysis to determine the best threshold, with validation on an independent dataset using that threshold. Future studies could include multivariate models to incorporate additional imaging sequences, image analysis (signal enhancement, texture) and non-imaging data (such as race, age, residual cancer burden, and receptor subtypes) as predictors. Additionally, future studies could analyze breast PCR and nodal PCR separately as they may provide valuable information for their respective surgical planning.
Conclusion This study evaluated aLN MRI volumes associated with NAC in patients with breast cancer. Our findings support the hypothesis that aLN MRI offers clinically relevant information and added value to predict treatment response to NAC in patients with breast cancer.
Clinical Practice Points The extent of breast tumor volume reduction by MRI is known
to be associated with pathologic response after NAC. We found that aLN MRI characteristics, such as changes in
volume and rate of changes, are associated with treatment response to NAC. MRI of aLN could minimize the need for unnecessary sentinel biopsy in the context of breast cancer NAC and aid in personalized treatment planning.
Acknowledgments The authors thank Drs Nola Hylton, Gillian Hirst, Christina Yau, and David Newitt for providing additional data from ISPY-1 (ie, detailed pathology, treatment, and survival data) that were not available on the website. The authors also acknowledge the students who have contributed to this project, including Jason Ha, Karamoko Soumahoro, Ankita Katukota, and Nikita Katukota. This work was supported in part by pilot grants from the Stony Brook Cancer Center and a Carol Baldwin pilot grant through the Stony Brook University School of Medicine. The authors also would like to acknowledge the resources of the Advanced Imaging Shared Resource of the Stony Brook Cancer Center and the Biomedical Imaging Research Center (Radiology).
Disclosure The authors have stated that they have no conflicts of interest.
Supplemental Data Supplemental figure accompanying this article can be found in the online version at https://doi.org/10.1016/j.clbc.2019.06.006.
References 1. DeSantis CE, Ma J, Goding Sauer A, Newman LA, Jemal A. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin 2017; 67: 439-48.
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Axillary Lymph Node Volume and Pathological Complete Responses 2. Anderson TL, Glazebrook KN, Murphy BL, Viers LD, Hieken TJ. Cross-sectional imaging to evaluate the extent of regional nodal disease in breast cancer patients undergoing neoadjuvant systemic therapy. Eur J Radiol 2017; 89:163-8. 3. Nagar H, Boothe D, Ginter PS, et al. Disease-free survival according to the use of postmastectomy radiation therapy after neoadjuvant chemotherapy. Clin Breast Cancer 2015; 15:128-34. 4. Rastogi P, Anderson SJ, Bear HD, et al. Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. J Clin Oncol 2008; 26:778-85. 5. van Nijnatten TJA, Goorts B, Vöö S, et al. Added value of dedicated axillary hybrid 18F-FDG PET/MRI for improved axillary nodal staging in clinically node-positive breast cancer patients: a feasibility study. Eur J Nucl Med Mol Imaging 2018; 45: 179-86. 6. Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 2018; 28:582-91. 7. van Nijnatten TJA, Schipper RJ, Lobbes MBI, et al. Diagnostic performance of gadofosveset-enhanced axillary MRI for nodal (re)staging in breast cancer patients: results of a validation study. Clin Radiol 2018; 73:168-75. 8. Fowler AM, Mankoff DA, Joe BN. Imaging neoadjuvant therapy response in breast cancer. Radiology 2017; 285:358-75. 9. Hylton NM, Blume JD, Bernreuter WK, et al, ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy–results from ACRIN 6657/ I-SPY TRIAL. Radiology 2012; 263:663-72. 10. Mattingly AE, Mooney B, Lin HY, et al. Magnetic resonance imaging for axillary breast cancer metastasis in the neoadjuvant setting: a prospective study. Clin Breast Cancer 2017; 17:180-7. 11. Hylton NM, Gatsonis CA, Rosen MA, et al, ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Neoadjuvant chemotherapy for breast cancer: functional tumor volume by MR imaging predicts recurrence-free survival-results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology 2016; 279:44-55. 12. Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26: 1045-57. 13. Newitt DC, Hylton NM. Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive. 2016. Available at: https://wiki.cancerimagingarchive.net/display/Public/ISPY1 #f888892a52a944e9974cd3d6766026c6. Accessed: June 30, 2017. 14. Mukhtar RA, Yau C, Rosen M, et al, I-SPY 1 TRIAL and ACRIN 6657 Investigators. Clinically meaningful tumor reduction rates vary by prechemotherapy
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15.
16. 17. 18. 19. 20. 21. 22.
23. 24. 25. 26. 27.
MRI phenotype and tumor subtype in the I-SPY 1 TRIAL (CALGB 150007/ 150012; ACRIN 6657). Ann Surg Oncol 2013; 20:3823-30. Li W, Arasu V, Newitt DC, et al, ACRIN 6657 Trial Team and I-SPY Investigators Network. Effect of MR imaging contrast thresholds on prediction of neoadjuvant chemotherapy response in breast cancer subtypes: a subgroup analysis of the ACRIN 6657/I-SPY 1 TRIAL. Tomography 2016; 2:378-87. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 1995:289-300. Minarikova L, Bogner W, Pinker K, et al. Investigating the prediction value of multiparametric magnetic resonance imaging at 3 T in response to neoadjuvant chemotherapy in breast cancer. Eur Radiol 2017; 27:1901-11. Goorts B, Dreuning KMA, Houwers JB, et al. MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer. Breast Cancer Res 2018; 20:34. Tudorica A, Oh KY, Chui SY, et al. Early prediction and evaluation of breast cancer response to neoadjuvant chemotherapy using quantitative DCE-MRI. Transl Oncol 2016; 9:8-17. Schipper RJ, Moossdorff M, Beets-Tan RGH, Smidt ML, Lobbes MBI. Noninvasive nodal restaging in clinically node positive breast cancer patients after neoadjuvant systemic therapy: a systematic review. Eur J Radiol 2015; 84:41-7. Balu-Maestro C, Chapellier C, Bleuse A, Chanalet I, Chauvel C, Largillier R. Imaging in evaluation of response to neoadjuvant breast cancer treatment benefits of MRI. Breast Cancer Res Treat 2002; 72:145-52. Gu YL, Pan SM, Ren J, Yang ZX, Jiang GQ. Role of magnetic resonance imaging in detection of pathologic complete remission in breast cancer patients treated with neoadjuvant chemotherapy: a meta-analysis. Clin Breast Cancer 2017; 17:245-55. Chen L, Yang Q, Bao J, Liu D, Huang X, Wang J. Direct comparison of PET/CT and MRI to predict the pathological response to neoadjuvant chemotherapy in breast cancer: a meta-analysis. Sci Rep 2017; 7:8479. Weber JJ, Jochelson MS, Eaton A, et al. MRI and prediction of pathologic complete response in the breast and axilla after neoadjuvant chemotherapy for breast cancer. J Am Coll Surg 2017; 225:740-6. Hieken TJ, Boughey JC, Jones KN, Shah SS, Glazebrook KN. Imaging response and residual metastatic axillary lymph node disease after neoadjuvant chemotherapy for primary breast cancer. Ann Surg Oncol 2013; 20:3199-204. Kvistad KA, Rydland J, Smethurst HB, Lundgren S, Fjøsne HE, Haraldseth O. Axillary lymph node metastases in breast cancer: preoperative detection with dynamic contrast-enhanced MRI. Eur Radiol 2000; 10:1464-71. Mortellaro VE, Marshall J, Singer L, et al. Magnetic resonance imaging for axillary staging in patients with breast cancer. J Magn Reson Imaging 2009; 30:309-12.
Renee F. Cattell et al Supplemental Data Supplemental Figure 1 Receiver Operating Characteristic Curve Analysis for Change in Breast Tumor Volume Alone, Changes in aLN Volume Alone, and Changes in Both Breast Tumor and aLN Volumes for 2 TPs. AUC, Sensitivity, and Specificity are Plotted for Changes for Early Prediction (TP2-TP1) and Late Prediction (TP4-TP1). Sub-stratification Was Done for all Nodes (Blue), Large aLN (Yellow), and Non-large aLN (Black/White). A Threshold of 3 mL Was Used to Separate Patients With and Without Large Lymph Nodes. PCR is Defined as pT0/Is pN0. The Sample Size Varied Based on Available Data at Each Time Point Resulting in the Non-PCR Group (n [ 64-84) and the PCR Group (n [ 25-31)
Abbreviations: aLN ¼ axillary lymph node; AUC ¼ area under the receiver operating characteristic curve; TP ¼ time point.
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