- Email: [email protected]
Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy
Radiotherapy and Oncology xxx (2016) xxx–xxx
Contents lists available at ScienceDirect
Radiotherapy and Oncology journal homepage: www.thegreenjournal.com
Original article
Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy Isabelle Kindts a,b, Annouschka Laenen c, Stephanie Peeters a,b, Hilde Janssen a,b, Tom Depuydt a,b, Patrick Neven a,d, Erik Van Limbergen a,b, Caroline Weltens a,b,⇑ a KU Leuven – University of Leuven, Department of Oncology; b University Hospitals Leuven, Department of Radiation Oncology; c Leuven Biostatistics and Statistical Bioinformatics Centre (L-Biostat), KU Leuven University; and d University Hospitals Leuven, Department of Obstetrics and Gynaecology, Belgium
a r t i c l e
i n f o
Article history: Received 26 November 2015 Received in revised form 18 January 2016 Accepted 19 January 2016 Available online xxxx Keywords: Radiotherapy Breast cancer Breast-conserving therapy Nomogram Decision making Personalised medicine
a b s t r a c t Background and purpose: A nomogram to predict for the 10-year ipsilateral breast relapse (IBR) after breast-conserving therapy (BCT) for breast cancer (BC) was developed based on the ‘boost-no-boost’-trial with a concordance probability estimate (CPE) of 0.68. The aim of our study was to validate that algorithm. Material and methods: We retrospectively identified 1787 BC cases, treated with BCT and radiotherapy at the University Hospitals Leuven from 2000 to 2007, without missing data of the nomogram variables. Clinicopathologic factors were assessed. Validity of the prediction model was tested in terms of discrimination and calibration. Results: Median follow-up time was 10.75 years. The validation cohort differed with respect to the administration of a radiation boost, chemo- or hormonal therapy, age, tumour diameter or grade, ductal carcinoma in situ and hormone receptor positivity. On multivariable analysis, the omission of the boost was a significant prognosticator of IBR (p < 0.01). The 10-year IBR-rate was 1.4%. The nomogram demonstrated suboptimal discrimination (CPE 0.54) and calibration, with an overestimation of the IBR-risk in general. Conclusions: The predictive model for IBR in BC is imperfect in this more recent study population. Ó 2016 Elsevier Ireland Ltd. All rights reserved. Radiotherapy and Oncology xxx (2016) xxx–xxx
With the worldwide development of mammographic screening and age-increase, the incidence of early-stage breast cancer has increased. Since randomised controlled trials have shown that local control rates and survival are comparable to those of mastectomy, breast-conserving therapy (BCT) – including breast-conserving surgery (BCS) followed by whole breast irradiation (WBI) and optionally a boost to the tumour bed – is the standard therapeutic option [1,2]. Therapeutic failures leading to local or distant recurrences are a major concern, especially since the meta-analysis of the Early Breast Cancer Trialists’ Collaborative Group confirmed the relationship between local control and overall survival. By adding radiotherapy to BCS, the 10-year any first-recurrence rate decreases from 35.0% to 19.3% and breast cancer survival gains 3.8% at 15 years. The prevention of 4 recurrences at 10 years avoids one breast cancer death at 15 years [3].
⇑ Corresponding author at: University Hospitals Leuven, Department of Radiation Oncology, Herestraat 49, B-3000 Leuven, Belgium. E-mail addresses: [email protected] (I. Kindts), caroline.weltens@ uzleuven.be (C. Weltens).
Although boosting the tumour bed after WBI helps to further increase the local control rates, no consensus on its use has been reached because it increases the risk of fibrosis and might worsen cosmetic outcome [4]. The latest National Comprehensive Cancer Network guidelines recommend a boost in patients at higher risk for recurrence; whereas European guidelines advise a boost in the case of at least one of the following risk factors: age <50 years old, grade 3 tumours, vascular invasion, extensive ductal carcinoma in situ (DCIS) and (focally – otherwise further surgery should be advocated) non-radical tumour excision [5,6]. Several predictive algorithms have been developed to assist with the therapy decision making in breast cancer treatment. The main goal of adjuvant radiotherapy after BCS is to decrease local recurrences and to permit breast conservation with low treatment-induced sequels. Sanghani et al. constructed a nomogram that estimated the 10-year risk of ipsilateral breast relapse (IBR), with and without WBI after BCS [7]. The European Organisation for Research and Treatment of Cancer (EORTC) 22881-10882 (boost versus no boost) trial randomised 5318 patients between no boost and a 16 Gray (Gy) boost dose (or interstitial equivalent) after WBI [8]. Pathology slides from the early years of the accrual period (1989–1996) from
http://dx.doi.org/10.1016/j.radonc.2016.01.023 0167-8140/Ó 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
2
Evaluation IBR nomogram
brachytherapy was 15 Gy in low dose rate or pulse dose rate and 8.5 Gy in high dose rate, prescribed at the 85% isodose. Patients in whom no boost was administered, were also included in the analysis. Conform the original article, patients with another boost dose regimen (i.e. lower or higher) were excluded [7,9]. Section margins were considered positive in the case of transection, free if the margin was P2 mm and dubious in other cases. In case of re-excision, section margins thereby were taken into account. Chemotherapy was given according to standard protocol and involved 5 fluorouracil, epirubicin, cyclophosphamide, methotrexate and taxanes. In patients with bilateral breast cancer, data of both sides were included independently. Validation of the EORTC nomogram was performed only on patients who had no missing data of nomogram variables.
Fig. 1. Nomogram developed by van Werkhoven et al. [9]
Ipsilateral breast relapse
1616 patients were collected and reviewed by a single pathologist. A Cox model was then developed based on the clinical and pathological data of 1603 patients to estimate the 10-year IBR risk after BCT (Fig. 1). The nomogram includes 7 factors: histologic grade, DCIS, tumour diameter, age, tamoxifen, chemotherapy and boost, and was internally validated using the bootstrap procedure with a concordance probability estimate (CPE) of 0.68 [9]. The estimated 10-year IBR risk can be calculated online (http://research.nki.nl/ ibr/ibr/index.html). An objective and thorough validation of any predictive algorithm is of critical importance before its widespread implementation as a useful clinical tool. The aim of our study was to evaluate the nomogram by using a large, external and independent cancer centre database.
The event of interest was IBR. Patients without IBR were censored at the time of metastasis, death or end of follow-up. Two definitions of IBR were considered to deal with simultaneous regional or distant recurrence: firstly, patients with simultaneous regional or distant recurrence occurring within 4 months after IBR were censored. This definition is in agreement with the approach in van Werkhoven et al. [7,9]. In the second commonly used definition, patients with simultaneous regional or distant recurrence occurring within 4 months after IBR were considered as local relapse (event). The prognostic value of patient, tumour and treatment characteristics was evaluated in univariable and multivariable analysis. For binary and categorical variables, the same reference category was chosen as in van Werkhoven et al. [9]. The multivariable model included the same set of variables as the final model [9]. These analyses were based on the first definition of local relapse.
Material and methods
Validation
Patient selection and data collection A large database, set up in January 2000 and now containing prospectively obtained data of around 12,200 patient files was used for patient selection. The database includes data of all patients diagnosed with breast cancer and having at least one of the following treatments, i.e. surgery and/or radiotherapy and/or systemic therapy, at the University Hospitals of Leuven (UZL), Belgium. The patient cohort used for validation of the prediction model included patients diagnosed with a non-metastasised invasive breast cancer between January 1, 2000 and December 31, 2007. All radiation treatments had to be administered at UZL. This study was approved by the Clinical Trial Centre and the Ethics Committee of our institution. Treatment Whole breast irradiation was performed with two tangential photon beams with the dose specified at the intersection of the beam axes in the central plane as recommended by the ICRU report 50. The dose given for WBI was 50 Gy in 25 fractions of 2 Gy in all but one patient who received 42.56 Gy in 16 fractions of 2.66 Gy. For the selection of the boost technique, an in-house developed flowchart based on the depth of the tumour bed was used. For a tumour bed lying more than 28 mm beneath the skin, an interstitial or photon boost is chosen over an electron boost because of skin doses and for cosmetic reasons [10]. The standard external boost dose was 16 Gy in 8 fractions. The standard dose with
The validation was performed separately for the two definitions of local relapse. There are two aspects in the evaluation of model performance: discrimination and calibration. Discrimination concerns the relative positioning of patients as the extent to which patients predicted to be at higher risk exhibit higher event rates than those deemed at lower risk. Calibration concerns the absolute risk estimation, or absence of over- or underestimation of the actual risk. The EORTC nomogram was constructed based on a dataset with patients’ age range 27–76 years and tumour size 0–50 mm. When applying the nomogram, no predictions are provided for patients with age or tumour size beyond these ranges. We performed the validation for all data, including patients with values beyond these ranges, and for a restricted dataset, including only patients with values within both ranges. To assess discrimination, the CPE was determined based on a Cox model with time to IBR as outcome and the EORTC nomogram 10-year IBR-free probability as the only covariate [11,12]. For two patients, one of whom had a local relapse and the other did not by a certain time, the CPE estimates the probability that the model will give higher risk to the one patient compared to the other. A model with a perfect discrimination would have a CPE of 1, whereas a value of 0.5 indicates that a coin toss would provide information as accurate as that given by the model. A calibration plot was drawn showing predicted 10-year IBRfree probabilities against observed Kaplan–Meier estimates, grouped into five intervals of equal size. All analyses have been performed using SAS software, version 9.4 of the SAS System for Windows.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
3
I. Kindts et al. / Radiotherapy and Oncology xxx (2016) xxx–xxx
Results Patient, tumour and treatment characteristics Of the 1866 patients without missing nomogram variables, 111 patients were excluded because the radiation boost dose differed from the standard dose described in the Section ‘Treatment’: 4 patients received an insufficient dose, and 107 patients received a higher dose due to involved section margins. 1755 patients were included in this study, accounting for 1787 cases. The median follow-up time was 10.75 years (Q1 8.87–Q3 12.86). Table 1 lists the comparisons between the EORTC and the UZL cohorts, including patient, tumour, and treatment variables. Overall, in the UZL cohort, patients were somewhat older (58 years old vs 54 years old), had a slightly larger tumour diameter (18 mm vs 15 mm) and were more likely to have received chemotherapy
(29.7% vs 15.7%), to have a high grade disease (37.0% vs 23.5%) and to have a DCIS (69.8% vs 57.8%). Twenty-three percent of the patients received tamoxifen in the EORTC group, whereas 81.6% received hormonal therapy in the UZL group. Almost all patients (99.7%) in the UZL group received a boost versus 50.4% in the EORTC cohort. Noteworthy on the variables not included in the nomogram, is that patients in the UZL cohort had a higher percentage of oestrogen (ER) and progesterone receptor (PR) positivity (86.4% vs 71.7% and 75.9% vs 64.3%, respectively) and that 10.2% of the UZL cohort had HER2 overexpression/amplification (Table 1). Ipsilateral breast relapse Among the 1787 cases, 34 (1.9%) developed IBR according to the first definition. Forty-five patients (2.5%) developed IBR
Table 1 Comparison of patient, tumour and treatment characteristics in the EORTC and UZL cohorts. Patients in the EORTC Cohort Characteristic
Patients in the UZL Cohort
No. (n = 1603)
Percentage (%)
No. (n = 1787)
Percentage (%)
Chemotherapy
No Yes
1351 252
84.3 15.7
1257 530
70.3 29.7
Hormonal therapy
No Yes
1234 369
77.0 23.0
328 1459
18.4 81.6
Radiotherapeutic boost
No boost 16 Gy or equivalent Boost 16 Gy or equivalent
795 808
49.6 50.4
5 1782
0.3 99.7
Low Intermediate High
74 missing 778 392 359
50.9 25.6 23.5
343 783 661
19.2 43.8 37.0
No Yes
38 missing 660 905
42.2 57.8
540 1247
30.2 69.8
Age (years)
Median Range
54 27–76
Age (binary) (years)
650 >50
622 981
Diameter (mm)
Median Range
15 0–50
18 0–122
Follow-up time (years)
Median IQ range
11.5
10.75 8.87–12.86
Histologic grade
DCIS
Lymphovascular invasion
58 22–90 38.8 61.2
None Present Doubtful
43 missing 1179 222 159
75.6 14.2 10.2
Close Free Involved
120 missing 304 1130 49
Invasive ductal carcinoma Invasive lobular carcinoma Mixed invasive pattern Other
531 1256
29.7 70.3
623 missing 967 197
83.1 16.9
20.5 76.2 3.3
4 missing 250 1480 53
14.0 83.0 3.0
34 missing 1116 109 163 181
71.1 6.9 10.4 11.5
424 missing 1141 111 3 108
83.7 8.1 0.2 7.9
Node-negative Node-positive
11 missing 1236 356
77.6 22.4
32 missing 1281 474
73.0 27.0
Negative Positive
424 missing 334 845
28.3 71.7
18 missing 241 1528
13.6 86.4
Negative Positive
545 missing 378 680
35.7 64.3
23 missing 426 1338
24.1 75.9
75 missing 1537 175
89.8 10.2
Margin of invasive tumour
Histology
Nodal status
ER
PR
Her2
No data Negative Positive
Characteristics included in the nomogram are in bold.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
4
Evaluation IBR nomogram
according to the second definition (Appendix 1). The Kaplan– Meier estimated IBR-free rates at 5 and 10 years of follow-up were 99.37% and 98.63%, respectively, according to the first definition and 99.02% and 97.91%, respectively, according to the second definition. In univariable analysis, the omission of a boost dose (p < 0.01), a negative ER (p < 0.01) or PR (p = 0.03) and a positive nodal status (p = 0.02) were significantly associated with IBR. Trends towards increased risk of IBR were observed for the omission of hormonal
therapy (p = 0.12) and a younger age (p = 0.09) (Table 2). When applying all of the variables included in the initial nomogram in a multivariable model, only the omission of the boost proved to be a significant risk factor for a local recurrence (HR, 31.52; 95% CI, 3.27–303.62; p < 0.01), with a trend for age (p = 0.06) (Table 3). Given that only five patients did not receive a boost, the multivariable model was also applied excluding these five patients and excluding boost as a variable in the model; the obtained results were similar (Appendix 2).
Table 2 Results of univariable analysis of clinicopathological variable influence on IBR in the EORTC and UZL Cohorts. Patients in the EORTC Cohort Characteristic
HR (95% CI)
Chemotherapy
Patients in the UZL Cohort p
HR (95% CI)
0.77 No Yes
1.00 1.08 (0.67; 1.74)
No Yes
1.00 0.39 (0.21; 0.70)
No boost or equivalent Boost 16 Gy or equivalent
1.00 0.53 (0.36; 0.77)
Low Intermediate High
1.00 1.15 (0.71; 1.86) 1.98 (1.29; 3.03)
Low/intermediate High
1.00 1.89 (1.27; 2.81)
No Yes
1.00 1.84 (1.22; 2.77)
650 >50
1.00 0.40 (0.28; 0.57)
Per 10 mm
1.20 (0.93; 1.54)
None Present Doubtful
1.00 1.30 (0.80; 2.11) 0.51 (0.23; 1.17)
Close Free Involved
1.00 1.24 (0.76; 2.04) 0.31 (0.04; 2.31)
Invasive ductal carcinoma Invasive lobular carcinoma Mixed invasive pattern Other
1.00 0.70 (0.31; 1.60) 0.60 (0.29; 1.23) 0.80 (0.44; 1.46)
Hormonal therapy
0.25 1.00 1.51 (0.75; 3.05)
<0.01
Radiotherapeutic boost
0.12 1.00 0.56 (0.27; 1.17)
<0.01
Histologic grade
<0.01 1.00 0.06 (0.01; 0.42)
0.01
Histologic grade
0.34 1.00 1.78 (0.67; 4.78) 1.13 (0.39; 3.30)
<0.01
DCIS
0.41 1.00 0.74 (0.35; 1.54)
<0.01
Age (binary) (years)
0.98 1.00 1.01 (0.48; 2.12)
<0.01
Diameter (per 10 mm)
0.09 1.00 0.55 (0.28; 1.09)
0.16
Lymphovascular invasion
0.64 0.92 (0.64; 1.31)
0.13
Margin of invasive tumour
0.28 1.00 0.33 (0.04; 2.46)
0.24
Histology
0.17 1.00 0.57 (0.25; 1.26) 1.54 (0.33; 7.29)
0.41
Nodal status
0.13 1.00 3.14 (1.02; 9.63) *
2.54 (0.72; 8.93) 0.41
Node-negative Node-positive
1.00 0.82 (0.52; 1.31)
Negative Positive Unknown
1.00 0.71 (0.46; 1.11) 0.76 (0.46; 1.26)
Negative Positive Unknown
1.00 0.90 (0.57; 1.42) 0.95 (0.59; 1.52)
ER
0.02 1.00 2.30 (1.12; 4.69)
0.31
PR
p
<0.01 1.00 0.30 (0.15; 0.62) 0.97 (0.13; 7.55)
0.89
0.03 1.00 0.58 (0.28; 1.20) 3.09 (0.68; 13.96)
Her2
0.25 Negative Positive Unknown
1.00 1.43 (0.50; 4.11) 2.34 (0.81; 6.82)
* Note: On univariable analysis, there are only 3 cases with mixed histology (all without event), so the HR cannot be estimated. Characteristics included in the nomogram are in bold.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
5
I. Kindts et al. / Radiotherapy and Oncology xxx (2016) xxx–xxx Table 3 Results of multivariable analysis of clinicopathological variable influence on IBR in the EORTC and UZL Cohorts. Patients in the EORTC Cohort Characteristic
HR (95% CI)
Chemotherapy
p
HR (95% CI)
0.13 Yes No
1.00 1.47 (0.89; 2.43)
Yes No
1.00 1.70 (0.91; 3.19)
Boost 16 Gy or equivalent No boost or equivalent
1.00 2.02 (1.39; 2.95)
Low/intermediate High
1.00 1.21 (0.78; 1.87)
No Yes
1.00 1.96 (1.30; 2.94)
Hormonal therapy
0.80
0.23 1.00 1.60 (0.74; 3.48)
<0.01
Histologic grade
<0.01 1.00 31.52 (3.27; 303.62)
0.39
DCIS
0.33 1.00 0.68 (0.32; 1.47)
<0.01
<0.01
Diameter (per 10 mm)
0.39 1.13 (0.86; 1.47)
Table 4 CPE. *
IBR
Dataset
N cases
N events
CPE
First definition
All data Restricted data*
1787 1673
34 32
0.54 (0.51; 0.57) 0.54 (0.51; 0.57)
(95% CI)
Second definition
All data Restricted data*
1787 1673
45 43
0.53 (0.50; 0.56) 0.53 (0.51; 0.56)
Restricted data: age range 27–76 years and tumour size 0–50 mm.
Validation We evaluated the EORTC nomogram in our dataset. To assess discrimination, the CPE was calculated (Table 4). CPE values are 0.54 and 0.53, respectively for the first and the second definition of IBR, indicating poor discriminative ability of the EORTC model in the present cohort. To assess the accuracy of the EORTC nomogram, actual 10-year recurrences were plotted against the predicted 10-year probability of recurrence. Patients were ranked according to their modelpredicted risk and five subgroups of equal size and of increasing
0.98 1.00 1.01 (0.48; 2.14)
Age (per year) Per 10 mm
p
1.00 1.12 (0.47; 2.68) 0.10
Radiotherapeutic boost
*
Patients in the UZL Cohort
0.06 0.68 0.92 (0.62; 1.36)
risk were constructed. For each subgroup the average modelpredicted relapse-free rate was determined, as well as their Kaplan–Meier estimated observed rates, and both were plotted against each other. The number of 5 subgroups was motivated by a low number of events in the data (a larger number of subgroups would lead to very small numbers of event per subgroup). The plot shows that in all subgroups, and foremost in the higher risk groups, the observed relapse-free rates are higher than the modelpredicted relapse-free rates, hence that the model overestimates the risk of ipsilateral breast relapse in general (Fig. 2). Discussion Minimising the risk of local recurrences remains a clinical aim of paramount importance since it significantly correlates with overall survival [3]. This has raised the attention on the one side on more effective treatment strategies that could decrease the risk of local failure after BCT and on the other side on identifying more robust clinical, pathological, molecular and treatment-related predictors of local recurrence. A combination of predictors can
Fig. 2. Model calibration plot for first (left) and second (right) definition of local relapse. Note: For 5 subgroups of equal size, the model-predicted average relapse-free rate was plotted against the Kaplan–Meier estimated observed rates. Black represents ‘All data’, grey represents ‘Restricted data’. The dashed line corresponds to ideal calibration.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
6
Evaluation IBR nomogram
generate a prognostic model which has several critical applications. It can inform patients and their families about the treatment and clinical decision making or about the likely course of a disease; or it can create clinical risk groups by disease severity for clinical trials and risk adjustment [13]. Numerous studies have evaluated clinical, pathological and treatment-related factors that may increase the risk of local recurrence, including age, tumour size, nodal status, margin status, presence of lymphovascular invasion or DCIS, grade, hormone receptor status, Her2/neu gene expression and the administration of radio-/chemo-/hormonal therapy [7,9,10,14–21]. A nomogram to predict the 10-year IBR risk after BCT was developed based on the EORTC boost versus no boost trial [9]. The EORTC nomogram was internally validated using bootstrap re-sampling, with a concordance probability estimate of 0.68. The purpose of our study was to externally validate the predictive model to determine the performance when applied to an independent dataset [13]. From a prospectively collected database, 1787 breast cancer cases with a median follow-up time of 10.75 years at the UZL were included in the analysis. The biggest difference between the EORTC and the UZL cohorts was that almost all patients (99.7%) in the UZL group received a boost versus 50.4% in the EORTC cohort. Besides that, patients in the UZL cohort were older, had a larger tumour diameter and were more likely to have received chemotherapy, to have a high grade disease and to have DCIS. Twenty-three percent of the patients received tamoxifen in the EORTC group, whereas 81.6% received hormonal therapy in the UZL group. Patients in the UZL cohort had a higher percentage of ER and PR positivity (86.4% vs 71.7% and 75.9% vs 64.3%, respectively). Univariable analysis of our study data shows that the omission of a boost dose, the negativity of a hormone receptor and a positive nodal status are significant prognosticators for a local recurrence. We observed a trend towards increased risk of IBR in case of the omission of hormonal therapy and younger age. When applying all of the variables included in the initial nomogram in a multivariable model, only the omission of the boost proved to be a significant risk factor for IBR. This suggests the only prognosticator for an IBR in the UZL cohort is the omission of a boost. However, only five patients did not receive a boost in the UZL cohort and one of them had a local recurrence; so the value of this factor in the multivariable model is arguable (HR 31.52; 95% CI 3.27–303.62). Dropping the boost-variable from the multivariable model and exclusion of these five patients shows unchanged results regarding the other variables: none of the variables show significant association with local recurrence. Multivariable analysis however did demonstrate a trend for age. Young age in BCT remains a disputable topic, mainly since younger women often tend to have more aggressive tumour subtypes and, therefore, are expected to benefit more from systemic therapy [14,15,18,22]. The calculated CPE of 0.53–0.54 indicates that the performance of the nomogram is suboptimal. There are several explanations for the suboptimal discriminative power of the EORTC nomogram in the present dataset. Boost (no/16 Gy) is an important predictor in the EORTC model whereas nearly all patients received a boost in the validation cohort. The substandard discriminative power might thus be attributed to the reduction in variability of the data used for validation. Also with respect to adjuvant therapy there are important differences. Firstly, HER2 status was not determined in the EORTC trial, whereas 10.2% of the UZL cohort had a positive HER2 state and 55% of them targeted therapy (availability or reimbursement from 2006 on). Secondly, 30% were administered chemotherapy in the validation cohort compared to 16% in the EORTC group. Thirdly, 23% received hormonal therapy in the EORTC data versus 82% in the UZL cohort.
The calibration plot shows that in all subgroups the observed relapse-free rates are higher than the model-predicted relapsefree rates, hence that the model overestimates the risk in general. Since the EORTC boost no boost study, on which the EORTC nomogram was based, also included patients of the UZL, our validation dataset is not completely external [13]. However, the dataset used for validation is independent since it is based on patients treated more than 10 years later as those the model was constructed on. We chose to include patients from January 1, 2000 till December 31, 2007 to have on the one side an adequate follow-up time as well as an incorporation of recent treatment techniques on the other side. 10-year local recurrence free rates are 98.6% and 97.9% according to the first and the second definition of local recurrence, respectively. The EORTC prediction model was developed based on diagnoses between 1989 and 1996. The 10-year local recurrence free rate (patients were censored if they experienced regional recurrence, new tumour, distant metastasis or death within 4 months of their local recurrence, except if there was only regional recurrence) for the whole group of 5318 subjects was 91.4% (personal communication van Werkhoven). This is a clearly higher event rate compared to our data, again suggesting a different population. Although the definitions for local recurrence are different, it is indisputable that the number of local recurrences have decreased over time. The effect of the treatment era has been described in literature and can be attributed to the improvement in preoperative breast imaging and postoperative delineation of the lumpectomy cavity, greater attention to the obtainment of negative surgical margins, incorporation of the radiotherapy breast boost and the increased use of (neo-)adjuvant systemic treatment [15,23–26]. Patients in the UZL cohort received more systemic therapy. The EORTC boost no boost trial did not evaluate the HER2 status or the efficacy of the hormone therapy with respect to receptor status, partly because receptor status was unknown for many patients (424 and 545 missing data for ER and PR, respectively). Our data reveal that the hormone receptor status however might be an important prognostic factor for IBR. The treatment era effect might explain the overestimation on the calibration plot. One of the limitations of our study, like all current studies on predicting IBR, is the small number of events. This is a monoinstitutional series compared to a multi-institutional trial. However, as the recurrence rates did not significantly vary from one trial participant to another, this is probably not relevant. Moreover, the boost nomogram is based on the subgroup (of a similar size than this series) that had central pathology review, making both series even more comparable to start from. Another limitation is that we only used patients’ demographic, clinical and pathologic factors to predict the probability of IBR. More studies are needed specifying the implication of the molecular heterogeneity of breast cancer and of tumour biology, factors recognised as an independent predictor on local recurrence rates [27,28]. Based on the EORTC boost no boost trial, a second nomogram has been developed to predict the risk of fibrosis after WBI with or without a boost [29]. Unfortunately in our retrospective series, no standardised reporting of side-effects has been conducted to test this algorithm. With a currently more homogeneous dose distribution, results might also be different, however fibrosis rates are probably more comparable to the past as recurrence rates. The results of our study have demonstrated that the EORTC IBR nomogram overestimates the risk of an IBR in the current treatment era and, what is more troublesome, provides suboptimal differentiation between patients with a low or high risk. The algorithm therefore is only valid for patients treated in a similar way as in 1989–1996. A new nomogram should be developed with inclusion of factors as shown in this series: ER, PR, pN; as well as
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
I. Kindts et al. / Radiotherapy and Oncology xxx (2016) xxx–xxx
other factors, for instance: Her2, molecular subtypes, several types of administered systemic treatment or administration of WBI. Conflict of interest There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Acknowledgments The authors thank Erik van Werkhoven and Harry Bartelink for their cooperation. Myny Vanderpoorten foundation. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.radonc.2016.01. 023. References [1] Fisher B, Anderson S, Bryant J, et al. Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med 2002;347:1233–41. [2] Litiere S, Werutsky G, Fentiman IS, et al. Breast conserving therapy versus mastectomy for stage I–II breast cancer: 20 year follow-up of the EORTC 10801 phase 3 randomised trial. Lancet Oncol 2012;13:412–9. [3] Darby S, McGale P, Correa C, et al. Effect of radiotherapy after breastconserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 2011;378:1707–16. [4] Mukesh MB, Harris E, Collette S, et al. Normal tissue complication probability (NTCP) parameters for breast fibrosis: pooled results from two randomised trials. Radiother Oncol 2013;108:293–8. [5] National Comprehensive Cancer Network. NCCN Invasive breast cancer. [Online] Version 1.2016. http://www.nccn.org/professionals/physician_gls/ pdf/breast.pdf. Last accessed November 26, 2015. [6] Senkus E, Kyriakides S, Ohno S. On behalf of the ESMO Guidelines Committee. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2015;26:v8–v30. [7] Sanghani M, Truong PT, Raad RA, et al. Validation of a web-based predictive nomogram for ipsilateral breast tumor recurrence after breast conserving therapy. J Clin Oncol 2010;28:718–22. [8] Bartelink H, Maingon P, Poortmans P, et al. Whole-breast irradiation with or without a boost for patients treated with breast-conserving surgery for early breast cancer: 20-year follow-up of a randomised phase 3 trial. Lancet Oncol 2015;16:47–56. [9] Van Werkhoven E, Hart G, Hv Tinteren, et al. Nomogram to predict ipsilateral breast relapse based on pathology review from the EORTC 22881–10882 boost versus no boost trial. Radiother Oncol 2011;100:101–7.
7
[10] Verhoeven K, Kindts I, Laenen A, et al. A comparison of three different radiotherapy boost techniques after breast conserving therapy for breast cancer. Breast 2015;24:391–6. [11] Gonen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika 2005:965–70. [12] Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trial 1996;17:343–6. [13] Royston P, Altman DG. External validation of a cox prognostic model: principles and methods. BMC Med Res Methodol 2013;13:1–15. [14] Miles RC, Gullerud RE, Lohse CM, Jakub JW, Degnim AC, Boughey JC. Local recurrence after breast-conserving surgery: multivariable analysis of risk factors and the impact of young age. Ann Surg Oncol 2012;19:1153–9. [15] van Laar C, van der Sangen MJ, Poortmans PM, et al. Local recurrence following breast-conserving treatment in women aged 40 years or younger: trends in risk and the impact on prognosis in a population-based cohort of 1143 patients. Eur J Cancer 2013;49:3093–101. [16] Canavan J, Truong PT, Smith SL, Lu L, Lesperance M, Olivotto IA. Local recurrence in women with stage I breast cancer: declining rates over time in a large, population-based cohort. Int J Radiat Oncol Biol Phys 2014;88:80–6. [17] Botteri E, Bagnardi V, Rotmensz N, et al. Analysis of local and regional recurrences in breast cancer after conservative surgery. Ann Oncol 2010;21:723–8. [18] Arvold ND, Taghian AG, Niemierko A, et al. Age, breast cancer subtype approximation, and local recurrence after breast-conserving therapy. J Clin Oncol 2011;29:3885–91. [19] Houssami N, Macaskill P, Marinovich ML, et al. Meta-analysis of the impact of surgical margins on local recurrence in women with early-stage invasive breast cancer treated with breast-conserving therapy. Eur J Cancer 2010;46:3219–32. [20] Kunkler IH, Kerr GR, Thomas JS, et al. Impact of screening and risk factors for local recurrence and survival after conservative surgery and radiotherapy for early breast cancer: results from a large series with long-term follow-up. Int J Radiat Oncol Biol Phys 2012;83:829–38. [21] EBCTCG. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of randomised trials. Lancet 2005;365:1687–717. [22] Antonini N, Jones H, Horiot JC, et al. Effect of age and radiation dose on local control after breast conserving treatment: EORTC trial 22881–10882. Radiother Oncol 2007;82:265–71. [23] Bartelink H, Bourgier C, Elkhuizen P. Has partial breast by IORT or brachytherapy been prematurely introduced into the clinic? Radiother Oncol 2012;104:139–42. [24] Poortmans P, Aznar M, Bartelink H. Quality indicators for breast cancer: revisiting historical evidence in the context of technology changes. Semin Radiat Oncol 2012;22:29–39. [25] Buchholz TA, Tucker SL, Erwin J, et al. Impact of systemic treatment on local control for patients with lymph node-negative breast cancer treated with breast-conservation therapy. J Clin Oncol 2001;19:2240–6. [26] Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005;353:1673–84. [27] Mamounas EP, Tang G, Fisher B, et al. Association between the 21-gene recurrence score assay and risk of locoregional recurrence in node-negative, estrogen receptor-positive breast cancer: results from NSABP B-14 and NSABP B-20. J Clin Oncol 2010;28:1677–83. [28] Moran MS. Radiation therapy in the locoregional treatment of triple-negative breast cancer. Lancet Oncol 2015;16. e113-22. [29] Collette S, Collette L, Budiharto T, et al. Predictors of the risk of fibrosis at 10 years after breast conserving therapy for early breast cancer: a study based on the EORTC trial 22881–10882 ’boost versus no boost’. Eur J Cancer 2008;44:2587–99.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
Contents lists available at ScienceDirect
Radiotherapy and Oncology journal homepage: www.thegreenjournal.com
Original article
Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy Isabelle Kindts a,b, Annouschka Laenen c, Stephanie Peeters a,b, Hilde Janssen a,b, Tom Depuydt a,b, Patrick Neven a,d, Erik Van Limbergen a,b, Caroline Weltens a,b,⇑ a KU Leuven – University of Leuven, Department of Oncology; b University Hospitals Leuven, Department of Radiation Oncology; c Leuven Biostatistics and Statistical Bioinformatics Centre (L-Biostat), KU Leuven University; and d University Hospitals Leuven, Department of Obstetrics and Gynaecology, Belgium
a r t i c l e
i n f o
Article history: Received 26 November 2015 Received in revised form 18 January 2016 Accepted 19 January 2016 Available online xxxx Keywords: Radiotherapy Breast cancer Breast-conserving therapy Nomogram Decision making Personalised medicine
a b s t r a c t Background and purpose: A nomogram to predict for the 10-year ipsilateral breast relapse (IBR) after breast-conserving therapy (BCT) for breast cancer (BC) was developed based on the ‘boost-no-boost’-trial with a concordance probability estimate (CPE) of 0.68. The aim of our study was to validate that algorithm. Material and methods: We retrospectively identified 1787 BC cases, treated with BCT and radiotherapy at the University Hospitals Leuven from 2000 to 2007, without missing data of the nomogram variables. Clinicopathologic factors were assessed. Validity of the prediction model was tested in terms of discrimination and calibration. Results: Median follow-up time was 10.75 years. The validation cohort differed with respect to the administration of a radiation boost, chemo- or hormonal therapy, age, tumour diameter or grade, ductal carcinoma in situ and hormone receptor positivity. On multivariable analysis, the omission of the boost was a significant prognosticator of IBR (p < 0.01). The 10-year IBR-rate was 1.4%. The nomogram demonstrated suboptimal discrimination (CPE 0.54) and calibration, with an overestimation of the IBR-risk in general. Conclusions: The predictive model for IBR in BC is imperfect in this more recent study population. Ó 2016 Elsevier Ireland Ltd. All rights reserved. Radiotherapy and Oncology xxx (2016) xxx–xxx
With the worldwide development of mammographic screening and age-increase, the incidence of early-stage breast cancer has increased. Since randomised controlled trials have shown that local control rates and survival are comparable to those of mastectomy, breast-conserving therapy (BCT) – including breast-conserving surgery (BCS) followed by whole breast irradiation (WBI) and optionally a boost to the tumour bed – is the standard therapeutic option [1,2]. Therapeutic failures leading to local or distant recurrences are a major concern, especially since the meta-analysis of the Early Breast Cancer Trialists’ Collaborative Group confirmed the relationship between local control and overall survival. By adding radiotherapy to BCS, the 10-year any first-recurrence rate decreases from 35.0% to 19.3% and breast cancer survival gains 3.8% at 15 years. The prevention of 4 recurrences at 10 years avoids one breast cancer death at 15 years [3].
⇑ Corresponding author at: University Hospitals Leuven, Department of Radiation Oncology, Herestraat 49, B-3000 Leuven, Belgium. E-mail addresses: [email protected] (I. Kindts), caroline.weltens@ uzleuven.be (C. Weltens).
Although boosting the tumour bed after WBI helps to further increase the local control rates, no consensus on its use has been reached because it increases the risk of fibrosis and might worsen cosmetic outcome [4]. The latest National Comprehensive Cancer Network guidelines recommend a boost in patients at higher risk for recurrence; whereas European guidelines advise a boost in the case of at least one of the following risk factors: age <50 years old, grade 3 tumours, vascular invasion, extensive ductal carcinoma in situ (DCIS) and (focally – otherwise further surgery should be advocated) non-radical tumour excision [5,6]. Several predictive algorithms have been developed to assist with the therapy decision making in breast cancer treatment. The main goal of adjuvant radiotherapy after BCS is to decrease local recurrences and to permit breast conservation with low treatment-induced sequels. Sanghani et al. constructed a nomogram that estimated the 10-year risk of ipsilateral breast relapse (IBR), with and without WBI after BCS [7]. The European Organisation for Research and Treatment of Cancer (EORTC) 22881-10882 (boost versus no boost) trial randomised 5318 patients between no boost and a 16 Gray (Gy) boost dose (or interstitial equivalent) after WBI [8]. Pathology slides from the early years of the accrual period (1989–1996) from
http://dx.doi.org/10.1016/j.radonc.2016.01.023 0167-8140/Ó 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
2
Evaluation IBR nomogram
brachytherapy was 15 Gy in low dose rate or pulse dose rate and 8.5 Gy in high dose rate, prescribed at the 85% isodose. Patients in whom no boost was administered, were also included in the analysis. Conform the original article, patients with another boost dose regimen (i.e. lower or higher) were excluded [7,9]. Section margins were considered positive in the case of transection, free if the margin was P2 mm and dubious in other cases. In case of re-excision, section margins thereby were taken into account. Chemotherapy was given according to standard protocol and involved 5 fluorouracil, epirubicin, cyclophosphamide, methotrexate and taxanes. In patients with bilateral breast cancer, data of both sides were included independently. Validation of the EORTC nomogram was performed only on patients who had no missing data of nomogram variables.
Fig. 1. Nomogram developed by van Werkhoven et al. [9]
Ipsilateral breast relapse
1616 patients were collected and reviewed by a single pathologist. A Cox model was then developed based on the clinical and pathological data of 1603 patients to estimate the 10-year IBR risk after BCT (Fig. 1). The nomogram includes 7 factors: histologic grade, DCIS, tumour diameter, age, tamoxifen, chemotherapy and boost, and was internally validated using the bootstrap procedure with a concordance probability estimate (CPE) of 0.68 [9]. The estimated 10-year IBR risk can be calculated online (http://research.nki.nl/ ibr/ibr/index.html). An objective and thorough validation of any predictive algorithm is of critical importance before its widespread implementation as a useful clinical tool. The aim of our study was to evaluate the nomogram by using a large, external and independent cancer centre database.
The event of interest was IBR. Patients without IBR were censored at the time of metastasis, death or end of follow-up. Two definitions of IBR were considered to deal with simultaneous regional or distant recurrence: firstly, patients with simultaneous regional or distant recurrence occurring within 4 months after IBR were censored. This definition is in agreement with the approach in van Werkhoven et al. [7,9]. In the second commonly used definition, patients with simultaneous regional or distant recurrence occurring within 4 months after IBR were considered as local relapse (event). The prognostic value of patient, tumour and treatment characteristics was evaluated in univariable and multivariable analysis. For binary and categorical variables, the same reference category was chosen as in van Werkhoven et al. [9]. The multivariable model included the same set of variables as the final model [9]. These analyses were based on the first definition of local relapse.
Material and methods
Validation
Patient selection and data collection A large database, set up in January 2000 and now containing prospectively obtained data of around 12,200 patient files was used for patient selection. The database includes data of all patients diagnosed with breast cancer and having at least one of the following treatments, i.e. surgery and/or radiotherapy and/or systemic therapy, at the University Hospitals of Leuven (UZL), Belgium. The patient cohort used for validation of the prediction model included patients diagnosed with a non-metastasised invasive breast cancer between January 1, 2000 and December 31, 2007. All radiation treatments had to be administered at UZL. This study was approved by the Clinical Trial Centre and the Ethics Committee of our institution. Treatment Whole breast irradiation was performed with two tangential photon beams with the dose specified at the intersection of the beam axes in the central plane as recommended by the ICRU report 50. The dose given for WBI was 50 Gy in 25 fractions of 2 Gy in all but one patient who received 42.56 Gy in 16 fractions of 2.66 Gy. For the selection of the boost technique, an in-house developed flowchart based on the depth of the tumour bed was used. For a tumour bed lying more than 28 mm beneath the skin, an interstitial or photon boost is chosen over an electron boost because of skin doses and for cosmetic reasons [10]. The standard external boost dose was 16 Gy in 8 fractions. The standard dose with
The validation was performed separately for the two definitions of local relapse. There are two aspects in the evaluation of model performance: discrimination and calibration. Discrimination concerns the relative positioning of patients as the extent to which patients predicted to be at higher risk exhibit higher event rates than those deemed at lower risk. Calibration concerns the absolute risk estimation, or absence of over- or underestimation of the actual risk. The EORTC nomogram was constructed based on a dataset with patients’ age range 27–76 years and tumour size 0–50 mm. When applying the nomogram, no predictions are provided for patients with age or tumour size beyond these ranges. We performed the validation for all data, including patients with values beyond these ranges, and for a restricted dataset, including only patients with values within both ranges. To assess discrimination, the CPE was determined based on a Cox model with time to IBR as outcome and the EORTC nomogram 10-year IBR-free probability as the only covariate [11,12]. For two patients, one of whom had a local relapse and the other did not by a certain time, the CPE estimates the probability that the model will give higher risk to the one patient compared to the other. A model with a perfect discrimination would have a CPE of 1, whereas a value of 0.5 indicates that a coin toss would provide information as accurate as that given by the model. A calibration plot was drawn showing predicted 10-year IBRfree probabilities against observed Kaplan–Meier estimates, grouped into five intervals of equal size. All analyses have been performed using SAS software, version 9.4 of the SAS System for Windows.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
3
I. Kindts et al. / Radiotherapy and Oncology xxx (2016) xxx–xxx
Results Patient, tumour and treatment characteristics Of the 1866 patients without missing nomogram variables, 111 patients were excluded because the radiation boost dose differed from the standard dose described in the Section ‘Treatment’: 4 patients received an insufficient dose, and 107 patients received a higher dose due to involved section margins. 1755 patients were included in this study, accounting for 1787 cases. The median follow-up time was 10.75 years (Q1 8.87–Q3 12.86). Table 1 lists the comparisons between the EORTC and the UZL cohorts, including patient, tumour, and treatment variables. Overall, in the UZL cohort, patients were somewhat older (58 years old vs 54 years old), had a slightly larger tumour diameter (18 mm vs 15 mm) and were more likely to have received chemotherapy
(29.7% vs 15.7%), to have a high grade disease (37.0% vs 23.5%) and to have a DCIS (69.8% vs 57.8%). Twenty-three percent of the patients received tamoxifen in the EORTC group, whereas 81.6% received hormonal therapy in the UZL group. Almost all patients (99.7%) in the UZL group received a boost versus 50.4% in the EORTC cohort. Noteworthy on the variables not included in the nomogram, is that patients in the UZL cohort had a higher percentage of oestrogen (ER) and progesterone receptor (PR) positivity (86.4% vs 71.7% and 75.9% vs 64.3%, respectively) and that 10.2% of the UZL cohort had HER2 overexpression/amplification (Table 1). Ipsilateral breast relapse Among the 1787 cases, 34 (1.9%) developed IBR according to the first definition. Forty-five patients (2.5%) developed IBR
Table 1 Comparison of patient, tumour and treatment characteristics in the EORTC and UZL cohorts. Patients in the EORTC Cohort Characteristic
Patients in the UZL Cohort
No. (n = 1603)
Percentage (%)
No. (n = 1787)
Percentage (%)
Chemotherapy
No Yes
1351 252
84.3 15.7
1257 530
70.3 29.7
Hormonal therapy
No Yes
1234 369
77.0 23.0
328 1459
18.4 81.6
Radiotherapeutic boost
No boost 16 Gy or equivalent Boost 16 Gy or equivalent
795 808
49.6 50.4
5 1782
0.3 99.7
Low Intermediate High
74 missing 778 392 359
50.9 25.6 23.5
343 783 661
19.2 43.8 37.0
No Yes
38 missing 660 905
42.2 57.8
540 1247
30.2 69.8
Age (years)
Median Range
54 27–76
Age (binary) (years)
650 >50
622 981
Diameter (mm)
Median Range
15 0–50
18 0–122
Follow-up time (years)
Median IQ range
11.5
10.75 8.87–12.86
Histologic grade
DCIS
Lymphovascular invasion
58 22–90 38.8 61.2
None Present Doubtful
43 missing 1179 222 159
75.6 14.2 10.2
Close Free Involved
120 missing 304 1130 49
Invasive ductal carcinoma Invasive lobular carcinoma Mixed invasive pattern Other
531 1256
29.7 70.3
623 missing 967 197
83.1 16.9
20.5 76.2 3.3
4 missing 250 1480 53
14.0 83.0 3.0
34 missing 1116 109 163 181
71.1 6.9 10.4 11.5
424 missing 1141 111 3 108
83.7 8.1 0.2 7.9
Node-negative Node-positive
11 missing 1236 356
77.6 22.4
32 missing 1281 474
73.0 27.0
Negative Positive
424 missing 334 845
28.3 71.7
18 missing 241 1528
13.6 86.4
Negative Positive
545 missing 378 680
35.7 64.3
23 missing 426 1338
24.1 75.9
75 missing 1537 175
89.8 10.2
Margin of invasive tumour
Histology
Nodal status
ER
PR
Her2
No data Negative Positive
Characteristics included in the nomogram are in bold.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
4
Evaluation IBR nomogram
according to the second definition (Appendix 1). The Kaplan– Meier estimated IBR-free rates at 5 and 10 years of follow-up were 99.37% and 98.63%, respectively, according to the first definition and 99.02% and 97.91%, respectively, according to the second definition. In univariable analysis, the omission of a boost dose (p < 0.01), a negative ER (p < 0.01) or PR (p = 0.03) and a positive nodal status (p = 0.02) were significantly associated with IBR. Trends towards increased risk of IBR were observed for the omission of hormonal
therapy (p = 0.12) and a younger age (p = 0.09) (Table 2). When applying all of the variables included in the initial nomogram in a multivariable model, only the omission of the boost proved to be a significant risk factor for a local recurrence (HR, 31.52; 95% CI, 3.27–303.62; p < 0.01), with a trend for age (p = 0.06) (Table 3). Given that only five patients did not receive a boost, the multivariable model was also applied excluding these five patients and excluding boost as a variable in the model; the obtained results were similar (Appendix 2).
Table 2 Results of univariable analysis of clinicopathological variable influence on IBR in the EORTC and UZL Cohorts. Patients in the EORTC Cohort Characteristic
HR (95% CI)
Chemotherapy
Patients in the UZL Cohort p
HR (95% CI)
0.77 No Yes
1.00 1.08 (0.67; 1.74)
No Yes
1.00 0.39 (0.21; 0.70)
No boost or equivalent Boost 16 Gy or equivalent
1.00 0.53 (0.36; 0.77)
Low Intermediate High
1.00 1.15 (0.71; 1.86) 1.98 (1.29; 3.03)
Low/intermediate High
1.00 1.89 (1.27; 2.81)
No Yes
1.00 1.84 (1.22; 2.77)
650 >50
1.00 0.40 (0.28; 0.57)
Per 10 mm
1.20 (0.93; 1.54)
None Present Doubtful
1.00 1.30 (0.80; 2.11) 0.51 (0.23; 1.17)
Close Free Involved
1.00 1.24 (0.76; 2.04) 0.31 (0.04; 2.31)
Invasive ductal carcinoma Invasive lobular carcinoma Mixed invasive pattern Other
1.00 0.70 (0.31; 1.60) 0.60 (0.29; 1.23) 0.80 (0.44; 1.46)
Hormonal therapy
0.25 1.00 1.51 (0.75; 3.05)
<0.01
Radiotherapeutic boost
0.12 1.00 0.56 (0.27; 1.17)
<0.01
Histologic grade
<0.01 1.00 0.06 (0.01; 0.42)
0.01
Histologic grade
0.34 1.00 1.78 (0.67; 4.78) 1.13 (0.39; 3.30)
<0.01
DCIS
0.41 1.00 0.74 (0.35; 1.54)
<0.01
Age (binary) (years)
0.98 1.00 1.01 (0.48; 2.12)
<0.01
Diameter (per 10 mm)
0.09 1.00 0.55 (0.28; 1.09)
0.16
Lymphovascular invasion
0.64 0.92 (0.64; 1.31)
0.13
Margin of invasive tumour
0.28 1.00 0.33 (0.04; 2.46)
0.24
Histology
0.17 1.00 0.57 (0.25; 1.26) 1.54 (0.33; 7.29)
0.41
Nodal status
0.13 1.00 3.14 (1.02; 9.63) *
2.54 (0.72; 8.93) 0.41
Node-negative Node-positive
1.00 0.82 (0.52; 1.31)
Negative Positive Unknown
1.00 0.71 (0.46; 1.11) 0.76 (0.46; 1.26)
Negative Positive Unknown
1.00 0.90 (0.57; 1.42) 0.95 (0.59; 1.52)
ER
0.02 1.00 2.30 (1.12; 4.69)
0.31
PR
p
<0.01 1.00 0.30 (0.15; 0.62) 0.97 (0.13; 7.55)
0.89
0.03 1.00 0.58 (0.28; 1.20) 3.09 (0.68; 13.96)
Her2
0.25 Negative Positive Unknown
1.00 1.43 (0.50; 4.11) 2.34 (0.81; 6.82)
* Note: On univariable analysis, there are only 3 cases with mixed histology (all without event), so the HR cannot be estimated. Characteristics included in the nomogram are in bold.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
5
I. Kindts et al. / Radiotherapy and Oncology xxx (2016) xxx–xxx Table 3 Results of multivariable analysis of clinicopathological variable influence on IBR in the EORTC and UZL Cohorts. Patients in the EORTC Cohort Characteristic
HR (95% CI)
Chemotherapy
p
HR (95% CI)
0.13 Yes No
1.00 1.47 (0.89; 2.43)
Yes No
1.00 1.70 (0.91; 3.19)
Boost 16 Gy or equivalent No boost or equivalent
1.00 2.02 (1.39; 2.95)
Low/intermediate High
1.00 1.21 (0.78; 1.87)
No Yes
1.00 1.96 (1.30; 2.94)
Hormonal therapy
0.80
0.23 1.00 1.60 (0.74; 3.48)
<0.01
Histologic grade
<0.01 1.00 31.52 (3.27; 303.62)
0.39
DCIS
0.33 1.00 0.68 (0.32; 1.47)
<0.01
<0.01
Diameter (per 10 mm)
0.39 1.13 (0.86; 1.47)
Table 4 CPE. *
IBR
Dataset
N cases
N events
CPE
First definition
All data Restricted data*
1787 1673
34 32
0.54 (0.51; 0.57) 0.54 (0.51; 0.57)
(95% CI)
Second definition
All data Restricted data*
1787 1673
45 43
0.53 (0.50; 0.56) 0.53 (0.51; 0.56)
Restricted data: age range 27–76 years and tumour size 0–50 mm.
Validation We evaluated the EORTC nomogram in our dataset. To assess discrimination, the CPE was calculated (Table 4). CPE values are 0.54 and 0.53, respectively for the first and the second definition of IBR, indicating poor discriminative ability of the EORTC model in the present cohort. To assess the accuracy of the EORTC nomogram, actual 10-year recurrences were plotted against the predicted 10-year probability of recurrence. Patients were ranked according to their modelpredicted risk and five subgroups of equal size and of increasing
0.98 1.00 1.01 (0.48; 2.14)
Age (per year) Per 10 mm
p
1.00 1.12 (0.47; 2.68) 0.10
Radiotherapeutic boost
*
Patients in the UZL Cohort
0.06 0.68 0.92 (0.62; 1.36)
risk were constructed. For each subgroup the average modelpredicted relapse-free rate was determined, as well as their Kaplan–Meier estimated observed rates, and both were plotted against each other. The number of 5 subgroups was motivated by a low number of events in the data (a larger number of subgroups would lead to very small numbers of event per subgroup). The plot shows that in all subgroups, and foremost in the higher risk groups, the observed relapse-free rates are higher than the modelpredicted relapse-free rates, hence that the model overestimates the risk of ipsilateral breast relapse in general (Fig. 2). Discussion Minimising the risk of local recurrences remains a clinical aim of paramount importance since it significantly correlates with overall survival [3]. This has raised the attention on the one side on more effective treatment strategies that could decrease the risk of local failure after BCT and on the other side on identifying more robust clinical, pathological, molecular and treatment-related predictors of local recurrence. A combination of predictors can
Fig. 2. Model calibration plot for first (left) and second (right) definition of local relapse. Note: For 5 subgroups of equal size, the model-predicted average relapse-free rate was plotted against the Kaplan–Meier estimated observed rates. Black represents ‘All data’, grey represents ‘Restricted data’. The dashed line corresponds to ideal calibration.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
6
Evaluation IBR nomogram
generate a prognostic model which has several critical applications. It can inform patients and their families about the treatment and clinical decision making or about the likely course of a disease; or it can create clinical risk groups by disease severity for clinical trials and risk adjustment [13]. Numerous studies have evaluated clinical, pathological and treatment-related factors that may increase the risk of local recurrence, including age, tumour size, nodal status, margin status, presence of lymphovascular invasion or DCIS, grade, hormone receptor status, Her2/neu gene expression and the administration of radio-/chemo-/hormonal therapy [7,9,10,14–21]. A nomogram to predict the 10-year IBR risk after BCT was developed based on the EORTC boost versus no boost trial [9]. The EORTC nomogram was internally validated using bootstrap re-sampling, with a concordance probability estimate of 0.68. The purpose of our study was to externally validate the predictive model to determine the performance when applied to an independent dataset [13]. From a prospectively collected database, 1787 breast cancer cases with a median follow-up time of 10.75 years at the UZL were included in the analysis. The biggest difference between the EORTC and the UZL cohorts was that almost all patients (99.7%) in the UZL group received a boost versus 50.4% in the EORTC cohort. Besides that, patients in the UZL cohort were older, had a larger tumour diameter and were more likely to have received chemotherapy, to have a high grade disease and to have DCIS. Twenty-three percent of the patients received tamoxifen in the EORTC group, whereas 81.6% received hormonal therapy in the UZL group. Patients in the UZL cohort had a higher percentage of ER and PR positivity (86.4% vs 71.7% and 75.9% vs 64.3%, respectively). Univariable analysis of our study data shows that the omission of a boost dose, the negativity of a hormone receptor and a positive nodal status are significant prognosticators for a local recurrence. We observed a trend towards increased risk of IBR in case of the omission of hormonal therapy and younger age. When applying all of the variables included in the initial nomogram in a multivariable model, only the omission of the boost proved to be a significant risk factor for IBR. This suggests the only prognosticator for an IBR in the UZL cohort is the omission of a boost. However, only five patients did not receive a boost in the UZL cohort and one of them had a local recurrence; so the value of this factor in the multivariable model is arguable (HR 31.52; 95% CI 3.27–303.62). Dropping the boost-variable from the multivariable model and exclusion of these five patients shows unchanged results regarding the other variables: none of the variables show significant association with local recurrence. Multivariable analysis however did demonstrate a trend for age. Young age in BCT remains a disputable topic, mainly since younger women often tend to have more aggressive tumour subtypes and, therefore, are expected to benefit more from systemic therapy [14,15,18,22]. The calculated CPE of 0.53–0.54 indicates that the performance of the nomogram is suboptimal. There are several explanations for the suboptimal discriminative power of the EORTC nomogram in the present dataset. Boost (no/16 Gy) is an important predictor in the EORTC model whereas nearly all patients received a boost in the validation cohort. The substandard discriminative power might thus be attributed to the reduction in variability of the data used for validation. Also with respect to adjuvant therapy there are important differences. Firstly, HER2 status was not determined in the EORTC trial, whereas 10.2% of the UZL cohort had a positive HER2 state and 55% of them targeted therapy (availability or reimbursement from 2006 on). Secondly, 30% were administered chemotherapy in the validation cohort compared to 16% in the EORTC group. Thirdly, 23% received hormonal therapy in the EORTC data versus 82% in the UZL cohort.
The calibration plot shows that in all subgroups the observed relapse-free rates are higher than the model-predicted relapsefree rates, hence that the model overestimates the risk in general. Since the EORTC boost no boost study, on which the EORTC nomogram was based, also included patients of the UZL, our validation dataset is not completely external [13]. However, the dataset used for validation is independent since it is based on patients treated more than 10 years later as those the model was constructed on. We chose to include patients from January 1, 2000 till December 31, 2007 to have on the one side an adequate follow-up time as well as an incorporation of recent treatment techniques on the other side. 10-year local recurrence free rates are 98.6% and 97.9% according to the first and the second definition of local recurrence, respectively. The EORTC prediction model was developed based on diagnoses between 1989 and 1996. The 10-year local recurrence free rate (patients were censored if they experienced regional recurrence, new tumour, distant metastasis or death within 4 months of their local recurrence, except if there was only regional recurrence) for the whole group of 5318 subjects was 91.4% (personal communication van Werkhoven). This is a clearly higher event rate compared to our data, again suggesting a different population. Although the definitions for local recurrence are different, it is indisputable that the number of local recurrences have decreased over time. The effect of the treatment era has been described in literature and can be attributed to the improvement in preoperative breast imaging and postoperative delineation of the lumpectomy cavity, greater attention to the obtainment of negative surgical margins, incorporation of the radiotherapy breast boost and the increased use of (neo-)adjuvant systemic treatment [15,23–26]. Patients in the UZL cohort received more systemic therapy. The EORTC boost no boost trial did not evaluate the HER2 status or the efficacy of the hormone therapy with respect to receptor status, partly because receptor status was unknown for many patients (424 and 545 missing data for ER and PR, respectively). Our data reveal that the hormone receptor status however might be an important prognostic factor for IBR. The treatment era effect might explain the overestimation on the calibration plot. One of the limitations of our study, like all current studies on predicting IBR, is the small number of events. This is a monoinstitutional series compared to a multi-institutional trial. However, as the recurrence rates did not significantly vary from one trial participant to another, this is probably not relevant. Moreover, the boost nomogram is based on the subgroup (of a similar size than this series) that had central pathology review, making both series even more comparable to start from. Another limitation is that we only used patients’ demographic, clinical and pathologic factors to predict the probability of IBR. More studies are needed specifying the implication of the molecular heterogeneity of breast cancer and of tumour biology, factors recognised as an independent predictor on local recurrence rates [27,28]. Based on the EORTC boost no boost trial, a second nomogram has been developed to predict the risk of fibrosis after WBI with or without a boost [29]. Unfortunately in our retrospective series, no standardised reporting of side-effects has been conducted to test this algorithm. With a currently more homogeneous dose distribution, results might also be different, however fibrosis rates are probably more comparable to the past as recurrence rates. The results of our study have demonstrated that the EORTC IBR nomogram overestimates the risk of an IBR in the current treatment era and, what is more troublesome, provides suboptimal differentiation between patients with a low or high risk. The algorithm therefore is only valid for patients treated in a similar way as in 1989–1996. A new nomogram should be developed with inclusion of factors as shown in this series: ER, PR, pN; as well as
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023
I. Kindts et al. / Radiotherapy and Oncology xxx (2016) xxx–xxx
other factors, for instance: Her2, molecular subtypes, several types of administered systemic treatment or administration of WBI. Conflict of interest There are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Acknowledgments The authors thank Erik van Werkhoven and Harry Bartelink for their cooperation. Myny Vanderpoorten foundation. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.radonc.2016.01. 023. References [1] Fisher B, Anderson S, Bryant J, et al. Twenty-year follow-up of a randomized trial comparing total mastectomy, lumpectomy, and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med 2002;347:1233–41. [2] Litiere S, Werutsky G, Fentiman IS, et al. Breast conserving therapy versus mastectomy for stage I–II breast cancer: 20 year follow-up of the EORTC 10801 phase 3 randomised trial. Lancet Oncol 2012;13:412–9. [3] Darby S, McGale P, Correa C, et al. Effect of radiotherapy after breastconserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet 2011;378:1707–16. [4] Mukesh MB, Harris E, Collette S, et al. Normal tissue complication probability (NTCP) parameters for breast fibrosis: pooled results from two randomised trials. Radiother Oncol 2013;108:293–8. [5] National Comprehensive Cancer Network. NCCN Invasive breast cancer. [Online] Version 1.2016. http://www.nccn.org/professionals/physician_gls/ pdf/breast.pdf. Last accessed November 26, 2015. [6] Senkus E, Kyriakides S, Ohno S. On behalf of the ESMO Guidelines Committee. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2015;26:v8–v30. [7] Sanghani M, Truong PT, Raad RA, et al. Validation of a web-based predictive nomogram for ipsilateral breast tumor recurrence after breast conserving therapy. J Clin Oncol 2010;28:718–22. [8] Bartelink H, Maingon P, Poortmans P, et al. Whole-breast irradiation with or without a boost for patients treated with breast-conserving surgery for early breast cancer: 20-year follow-up of a randomised phase 3 trial. Lancet Oncol 2015;16:47–56. [9] Van Werkhoven E, Hart G, Hv Tinteren, et al. Nomogram to predict ipsilateral breast relapse based on pathology review from the EORTC 22881–10882 boost versus no boost trial. Radiother Oncol 2011;100:101–7.
7
[10] Verhoeven K, Kindts I, Laenen A, et al. A comparison of three different radiotherapy boost techniques after breast conserving therapy for breast cancer. Breast 2015;24:391–6. [11] Gonen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika 2005:965–70. [12] Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control Clin Trial 1996;17:343–6. [13] Royston P, Altman DG. External validation of a cox prognostic model: principles and methods. BMC Med Res Methodol 2013;13:1–15. [14] Miles RC, Gullerud RE, Lohse CM, Jakub JW, Degnim AC, Boughey JC. Local recurrence after breast-conserving surgery: multivariable analysis of risk factors and the impact of young age. Ann Surg Oncol 2012;19:1153–9. [15] van Laar C, van der Sangen MJ, Poortmans PM, et al. Local recurrence following breast-conserving treatment in women aged 40 years or younger: trends in risk and the impact on prognosis in a population-based cohort of 1143 patients. Eur J Cancer 2013;49:3093–101. [16] Canavan J, Truong PT, Smith SL, Lu L, Lesperance M, Olivotto IA. Local recurrence in women with stage I breast cancer: declining rates over time in a large, population-based cohort. Int J Radiat Oncol Biol Phys 2014;88:80–6. [17] Botteri E, Bagnardi V, Rotmensz N, et al. Analysis of local and regional recurrences in breast cancer after conservative surgery. Ann Oncol 2010;21:723–8. [18] Arvold ND, Taghian AG, Niemierko A, et al. Age, breast cancer subtype approximation, and local recurrence after breast-conserving therapy. J Clin Oncol 2011;29:3885–91. [19] Houssami N, Macaskill P, Marinovich ML, et al. Meta-analysis of the impact of surgical margins on local recurrence in women with early-stage invasive breast cancer treated with breast-conserving therapy. Eur J Cancer 2010;46:3219–32. [20] Kunkler IH, Kerr GR, Thomas JS, et al. Impact of screening and risk factors for local recurrence and survival after conservative surgery and radiotherapy for early breast cancer: results from a large series with long-term follow-up. Int J Radiat Oncol Biol Phys 2012;83:829–38. [21] EBCTCG. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of randomised trials. Lancet 2005;365:1687–717. [22] Antonini N, Jones H, Horiot JC, et al. Effect of age and radiation dose on local control after breast conserving treatment: EORTC trial 22881–10882. Radiother Oncol 2007;82:265–71. [23] Bartelink H, Bourgier C, Elkhuizen P. Has partial breast by IORT or brachytherapy been prematurely introduced into the clinic? Radiother Oncol 2012;104:139–42. [24] Poortmans P, Aznar M, Bartelink H. Quality indicators for breast cancer: revisiting historical evidence in the context of technology changes. Semin Radiat Oncol 2012;22:29–39. [25] Buchholz TA, Tucker SL, Erwin J, et al. Impact of systemic treatment on local control for patients with lymph node-negative breast cancer treated with breast-conservation therapy. J Clin Oncol 2001;19:2240–6. [26] Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005;353:1673–84. [27] Mamounas EP, Tang G, Fisher B, et al. Association between the 21-gene recurrence score assay and risk of locoregional recurrence in node-negative, estrogen receptor-positive breast cancer: results from NSABP B-14 and NSABP B-20. J Clin Oncol 2010;28:1677–83. [28] Moran MS. Radiation therapy in the locoregional treatment of triple-negative breast cancer. Lancet Oncol 2015;16. e113-22. [29] Collette S, Collette L, Budiharto T, et al. Predictors of the risk of fibrosis at 10 years after breast conserving therapy for early breast cancer: a study based on the EORTC trial 22881–10882 ’boost versus no boost’. Eur J Cancer 2008;44:2587–99.
Please cite this article in press as: Kindts I et al. Evaluation of a breast cancer nomogram to predict ipsilateral breast relapse after breast-conserving therapy. Radiother Oncol (2016), http://dx.doi.org/10.1016/j.radonc.2016.01.023