- Email: [email protected]
The value of diffusion weighted imaging and apparent diffusion coefficient in primary Osteogenic and Ewing sarcomas for the monitoring of response to treatment: Initial experience
European Journal of Radiology 124 (2020) 108855
Contents lists available at ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Research article
The value of diffusion weighted imaging and apparent diffusion coefficient in primary Osteogenic and Ewing sarcomas for the monitoring of response to treatment: Initial experience
T
Karl Asmara, Charbel Saadeb, Rida Salmana, Raya Saabc, Nabil J. Khourya, Miguel Abboudc, Hani Tamimd, Maha Makkid, Lena Naffaaa,* a
Department of Radiology, American University of Beirut Medical Center, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon Faculty of Health Sciences, American University of Beirut, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon Department of Pediatrics and Adolescent Medicine, American University of Beirut Medical Center, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon d Department of Internal Medicine, American University of Beirut Medical Center, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Magnetic Resonance Imaging (MRI) Osteosarcoma Ewing sarcoma Diffusion Weighted Imaging (DWI) Apparent Diffusion Coefficient (ADC) Post-treatment
Purpose: To assess the value of using Apparent Diffusion Coefficient (ADC) mapping in Diffusion Weighted Imaging (DWI) when monitoring treatment response in pediatric Osteogenic and Ewing sarcomas and to correlate with level of necrosis on post-surgical excision pathology. Method: This retrospective study includes 7 Osteosarcoma and 8 Ewing sarcoma patients. Pre-treatment and post-treatment focal MRIs were evaluated for ADC values, tumor volumes and variability of both measurements. We also compared the measurement between Ewing and Osteosarcoma groups, as well as between good (=/ > 90 % necrosis post-excision) and poor (< 90 % necrosis post-excision) responders. Results: All measurements except Maximum ADC (p = 0.20) showed a statistically significant difference when comparing them before and after treatment. When we segregated our population according to pathologic complete response, there was no difference in ADC measurements, volumetric measurements or either variability between good (8 Patients) and poor responders (7 Patients). When comparing the before-after changes in our measurement between the Ewing sarcoma and Osteosarcoma cases, there was no significant difference in the change between pre and post treatment (Δ) Mean or Maximum ADC, or in Δtumor-volume when measured on STIR or SPIR T1 post-contrast sequences. Only the ΔMinimum-ADC showed a statistically significant difference (p < 0.02) in this group. Conclusions: ADC can potentially reflect cellular changes associated with chemotherapy use, reflecting a response to treatment. However, quantitative use of those parameters to dictate a change in management, treatment regimen or chemotherapy dose in order to target a good response (> / = 90 % necrosis post-excision) needs further investigation.
1. Introduction Primary bone malignancies are uncommon in the pediatric population, however, they rank as the leading cause of death from cancers [1]. Currently, the 5-year survival rate is 70 % due long term and lifelong risk of secondary chronic conditions [2]. The various treatments include chemotherapy and surgery; and the assessment of treatment response prior to surgery is the mainstay of management [3]. Conventional MRI using pulse sequences such as T1 Weighted
(T1W) and T2 Weighted (T2W) with and without intravenous (IV) gadolinium is the imaging test of choice for the detection and differentiation of musculoskeletal tumors. However, it lacks important information regarding tumor cellularity. Diffusion-weighted imaging (DWI), on the other hand has great potential in evaluating tumor cellularity [4], especially via quantifiable parameters like mean Apparent Diffusion Coefficient (ADC) [5]. DWI-MRI has been adopted into mainstream imaging due to its superior diagnostic performance, but it also demonstrates great promise in the assessment of treatment
⁎
Corresponding author. E-mail addresses: [email protected] (K. Asmar), [email protected] (C. Saade), [email protected] (R. Salman), [email protected] (R. Saab), [email protected] (N.J. Khoury), [email protected] (M. Abboud), [email protected] (H. Tamim), [email protected] (M. Makki), [email protected] (L. Naffaa). https://doi.org/10.1016/j.ejrad.2020.108855 Received 7 June 2019; Received in revised form 21 January 2020; Accepted 23 January 2020 0720-048X/ © 2020 Elsevier B.V. All rights reserved.
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
2.3. MRI technique
response [6]. The DWI-MRI relies on a pulse sequence that incorporates additional magnetic field gradients, which makes the intensity of the MR signal dependent on the mobility of the source of the signal. In other words, the denser the cellularity of the examined tissue, the more restricted the diffusion, the higher the MR signal on DWI and the lower the ADC map and vice versa [7]. ADC and ADC ratio measured on DWI MRI were shown in recent studies to increase in tumor necrosis (an indicator of adequate response to treatment) [8]. They were also shown to be good early indicators of prognosis [9]. The literature is however still scarce in data that compares initial ADC pre-chemotherapy to ADC post-chemotherapy especially in the pediatric population. In particular, the data regarding Ewing sarcoma is very limited. The aim of the current study is to investigate the utility of ADC mapping in DWI MRI in pediatric Osteogenic and Ewing sarcomas for the follow-up and monitoring of treatment response, when correlated with the level of necrosis on post-surgical excision pathological results.
Axial, sagittal and coronal SPIR T1 images post-IV gadolinium administration and STIR images were obtained on a 1,5 -Tesla and a 3Tesla field strength magnet (Philips Ingenia, Amsterdam, Holland), with coils adapted to the region of interest. Imaging is obtained 5 min after the intravenous injection of gadolinium. Echo-planar imaging (EPI) diffusion weighted imaging was performed on a 3-Tesla magnet (Philips Ingenia, NSA: 1–5, TR: 7000–10000, TE: 62–66, matrix: 120*80, FOV: 25−35 cm, slice thickness: 4–5.5 mm, slice gap: 0.5–1, b: 0−900 s/mm2) and on a 1.5 -Tesla magnet (Philips Ingenia, NSA: 4, TR: 7000–10000, TE: 63–80, matrix: 128*80, FOV: 25−35 cm, slice thickness: 4–5.5 mm, slice gap: 1 mm, b: 0−900 s/mm2) with coils adapted to the regions of interest.
2.4. Image analysis 2. Methods MR images were analyzed by 2 radiologists experienced in pediatric musculoskeletal imaging by consensus agreement (ABR 15 years). Axial ADC map images were obtained. The tumor was delineated at the level of the largest axial dimensions, based on the axial post-contrast SPIR T1 and linked ADC map images by a free hand region-ofinterest (ROI) to obtain the tumor area and calculated mean, maximum and minimum ADC value measurements (Figs. 2, 3 and 4 ). The ADC unit is x10-3 mm²/s. Three dimensional volumetric measurements were performed using the tumor tracking function of Philips Intellispace on STIR and SPIR T1 post-contrast images for both pre-treatment and post-treatment MRI exams. We also reported the ADC variability as well as tumor volume variability according to the formulas:
2.1. Study design This retrospective study was approved by the Institutional Review Board. We reviewed the electronic medical records of pediatric patients presenting with Ewing sarcoma and Osteosarcoma to the oncology service at our institution between 2010 and 2016. Inclusion criteria consisted of pediatric patients between 1 and 18 years of age, with a diagnosis of Ewing sarcoma or Osteosarcoma that was confirmed by imaging guided biopsy. Post-surgical excision pathology results were also required as well as MRI with diffusion weighted imaging sequences performed at initial diagnosis and post-treatment prior to surgical excision. Patients who did not meet any of these criteria were excluded (Fig. 1). All collected data was anonymized before analysis. When we segregated our population according to pathologic complete response (pCR), we considered a poor response to treatment when a patient had < 90 % necrosis in the excised tumor after surgery and a good response when a patient had a final =/ > 90 % necrosis, as reported by Hayashida et al. [10].
(ADC Post _treatment ) − (ADC Pre _treatment ) ⎤ ADC variability = ⎡ ⎢ ⎥ ADC Pre _treatment ⎣ ⎦ × 100 and
2.2. Image acquisition
Volume variability Reviewed MRI studies included standard sequences: T1WI, T2WI, Short tau inversion recovery (STIR), and Spectral Pre-saturation with Inversion Recovery (SPIR) T1 pre-and post- IV gadolinium administration, in addition to DWI and reconstructed ADC maps.
(volume Post _treatment ) − (volume Pre _treatment ) ⎤ × 100 =⎡ ⎥ ⎢ volume Pre _treatment ⎦ ⎣
Fig. 1. Diagram depicting the patient population and the reasons for exclusion or inclusion in our cohort. 2
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Fig. 2. MRI of a 12-year-old boy presenting with Ewing sarcoma of the right femur with pre-treatment (A) DWI, (B) ADC map, (C) Axial STIR and (D) Axial post contrast SPIR T1 and post-treatment (E) DWI, (F) ADC map, (G) Axial STIR and (H) Axial post contrast SPIR T1 showing the-free hand ROI selection in DWI and ADC maps. This patient was a good responder (> 90 % necrosis). Note the increase in the mean ADC value and decrease in the maximum ADC value on post-treatment ADC map (D).
(mean, maximum, and minimum) and volume (on STIR and post-contrast SPIR T1) before and after treatment was carried out by using the Wilcoxon test. P-value < 0.05 was used to indicate statistical significance.
2.5. Statistical analysis The Statistical Package for Social Sciences (SPSS, V.24, 2016, IBM) was used for data cleansing, management and analyses. Continuous data were reported as means and standard deviation (SD) and were compared between different dependent variables (type of cancer, necrosis) using the Mann-Whitney U test (for non-normal distribution and sample size less than 30). On the other hand, categorical data were reported as numbers and percentages and were compared using the Fisher test (sample size less than 30). The comparison between ADC
3. Results After reviewing the medical records of oncologic pediatric patients between 2010 and 2016, we identified 53 patients, 27 with Ewing sarcoma and 26 with Osteosarcoma. Thirty-eight cases were excluded 3
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Fig. 3. MRI of a 15-year-old boy with Osteosarcoma of the left femur with pre-treatment (A) DWI, (B) ADC map, (C) Axial STIR and (D) Axial post contrast SPIR T1 and post-treatment (E) DWI, (F) ADC map, (G) Axial STIR and (H) Axial post contrast SPIR T1 showing the free-hand ROI selection in DWI and ADC maps. This patient was a good responder (> 90 % necrosis). Note the increase in the mean ADC value and decrease in the maximum ADC value on post-treatment ADC map (F).
4
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Fig. 4. MRI of a 12-year-old boy with Ewing Sarcoma of the right humerus with pre-treatment (A) DWI, (B) ADC map, (C) reconstructed Axial STIR and (D) Axial post contrast SPIR T1 and post-treatment (E) DWI, (F) ADC map, (G) Axial STIR and (H) Axial post contrast SPIR T1 showing the free-hand ROI selection in ADC maps. This patient was a poor responder (< 90 % necrosis).
5
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Table 1 Comparison between ADC (mean, max, min) and volume (STIR, SPIR) before and after treatment. Treatment
Mean ADC Maximum ADC Minimum ADC Volume on STIR Volume on SPIR T1 Post-contrast
p-value
Before N = 15
After N = 15
1191.4 ± 361.1 2644.1 ± 402.9 133.6 ± 220.3 390.7 ± 213.2 367.6 ± 199.9
1534.7 ± 339.5 2500.0 ± 427.7 387.8 ± 349.8 172.4 ± 134.8 183.7 ± 171.2
0.03 0.20 0.02 0.01 0.02
Variability
28.65 ± 38.86 −7.10 ± 18.68 236.51 ± 371.66 −44.03 ± 63.36 −35.72 ± 78.32
ADC: apparent diffusion coefficient, STIR: Short TI Inversion Recovery, SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05.
4. Discussion
due to the unavailability of initial MRI, lack of DWI sequence on initial imaging or due to previous surgery/recurrence of disease (Fig. 1). A total of 15 cases were included. Of those cases, eight (53.3 %) were Ewing sarcoma while seven (46.7 %) were Osteosarcoma cases. The average patient age was 11.53 ± 3.52 years-old, nine cases were male and six were females. All patients received the same chemotherapy regimen as per standard of care for Ewing sarcoma and Osteosarcoma. The average time between initial MRI and follow up MRI prior to surgery was 10 weeks for Osteosarcoma and 16 weeks for Ewing sarcoma. We compared measurements before and after treatment and we found statistically significant differences in all measurements except Maximum ADC (p = 0.20), as shown in Table 1. Additionally, when we compared the ΔVolume and volume variability measured on SPIR T1 post-contrast to the ΔVolume and volume variability measured on STIR, we found no statistically significant difference between the 2 sequences (p < 0.3) (Table 2) Seven patients were poor responders and eight patients were good responders. We found no statistically significant difference in ADC measurements, volumetric measurements or either variability between good and poor responders (Table 3). Comparison between Ewing sarcoma and Osteosarcoma in terms of baseline and demographic characteristics are presented in Table 4. The average age was not significantly different between our Ewing and Osteosarcoma cases (12.38 ± 4.03 years-old and 10.57 ± 2.82 yearsold respectively). When comparing our before and after treatment measurements between Ewing sarcoma and Osteosarcoma cases, we found no statistically significant difference in the ΔMean-ADC, the ΔMaximum-ADC or between Δtumor-volume, when measured on STIR or SPIR T1 post-contrast sequences. On the other hand, the ΔMinimum-ADC showed a statistically significant difference (p < 0.02) when comparing the two groups. Additionally, the Minimum ADC measurement post-treatment was significantly higher in Osteosarcoma cases (p < 0.04). However, there were no significant differences in the variability of ADC measurements between Ewing sarcoma and Osteosarcoma in any of the parameters (mean, max or minimum). Similarly, the volume on SPIR T1 post-contrast following treatment was significantly higher in Osteosarcoma (p < 0.01). While the Δvolume on SPIR T1 post-contrast was not significantly different, the variability of tumor volume on this sequence was significantly different between Ewing and Osteosarcoma groups (p < 0.03) with a more pronounced change in Ewing sarcoma.
DWI with ADC map is sensitive in the imaging of molecular motion within tissues. After undergoing repeated cycles of chemotherapy, cellularity of tumors decreases and necrosis increases. The evaluation of response to treatment via DWI is based on the nature of the change in tumor constitution when exposed to chemotherapy. This cellular phenomenon should, in theory, be reflected by ADC mapping in DWI-MRI [7]. ADC mapping has been shown to be able to differentiate between the necrotic and viable parts of a tumor [11]. In fact, several studies have demonstrated an increase in ADC following chemotherapy, particularly in tumors of the brain and liver [12], and the relationship between cell necrosis and increased ADC values has been found to be significant in Osteosarcoma [13]. In the current study, we found results that are concordant with the literature, specifically the increase in minimum or mean ADC values with a more pronounced increase in minimum ADC (236.51 ± 71.66) than in mean ADC (28.65 ± 38.86) when comparing our population before and after treatment (Table 2). However, our data reported a decrease in maximum ADC measurement (−7.10 ± 8.68), which is not compatible with the study by Degnan et al. [14] that reported an increase in all three ADC measurements. It has been suggested that the only parameter of real significance for monitoring response to treatment is the minimum ADC which reflects the cellular component of the tumor more strongly than the mean or maximum ADC [12]. The mean or maximum ADC may be more influenced by the cystic components of the tumor and might not be as reliable as minimum ADC when assessing cellularity or necrosis of a tumor [12]. In fact, our results could be explained by a larger cystic component in our initial tumors. It could also be related to limitations concerning the region of interest (ROI) used to measure the ADC values. The ROI could include areas that affect the accuracy of ADC measurements such as perilesional edema. It also does not take into consideration the heterogeneity of the tumor. If the ROI used for measurement happens to include both necrotic and viable areas of the tumor, the changes in ADC will be reflected according to their proportions. The ADC value can be decreased if spatially varying diffusion values occur within the particular ROI selected [15]. The initial promising result of DWI and ADC measurements in the assessment of tumors after treatment was not carried to the analysis of the group according to pCR. ADC mapping did not show any significant difference when comparing the measured values of good and poor responders. We had an equal number of good and poor responders, and found very similar patterns of change in all ADC parameters. This finding is in conflict with similar studies in the literature that suggested a good correlation between ADC and response to treatment proven by pCR. Hayashida et al. [10] reported good correlation between good and poor responders and the ADC measurements in a sample of 18 patients. The sample in that study included mostly Osteosarcoma cases (only 2/ 18 were Ewing Sarcoma). Nevertheless, Bajpai et al. [16] reported in a prospective study with 31 patients with Osteosarcoma, an absence of direct correlation between ADC and pCR. An important point to be
Table 2 Comparison of Volume change before and after treatment (Δvolume) and variability between SPIR T1 Post-contrast and STIR Sequences.
ΔVolume Variability
SPIR T1 Post-contrast
STIR
p-value
−177.12 ± 257.41 −35.72 ± 78.32
−218.26 ± 228.25 −44.03 ± 63.36
0.30 0.68
STIR: Short TI Inversion Recovery. SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05. 6
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Table 3 Association between ADC (mean, max, min) before and after treatment and necrosis. Final necrosis
Mean ADC Maximum ADC Minimum ADC Volume on STIR Volume on SPIR T1 post-contrast
Age Gender, male n (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%)
P-value
< 90 N=7
≥90 N=8
11.43 ± 3.74 3 (42.9) 370.0 ± 484.3 42.15 ± 50.19 −126.2 ± 683.8 −3.41 ± 23.95 227.2 ± 225.4 256.58 ± 422.59 −103.7 ± 255.3 −14.78 ± 85.54 −95.5 ± 339.8 −6.92 ± 104.92
11.63 ± 3.58 6 (75.0) 203.6 ± 284.9 17.08 ± 24.14 −304.3 ± 387.1 −10.26 ± 13.96 239.1 ± 386.8 216.44 ± 406.85 −318.5 ± 153.9 −69.63 ± 12.33 −258.8 ± 111.9 −64.51 ± 18.64
0.78 0.32 0.25 0.32 0.57 0.67 0.89 0.83 0.08 0.25 0.11 0.48
ADC: apparent diffusion coefficient. STIR: Short TI Inversion Recovery. SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05.
evaluation and ADC parameter used (mean vs minimum vs maximum), have resulted in challenges for validating DWI and ADC for quantitative treatment response monitoring and has contributed to conflicting results in the literature [18]. It is also paramount to consider the nature of the tumor being studied. Different tumors will respond differently to chemotherapy and ADC should be most valuable in tumors that present with significant necrosis. In comparing ADC measurements within our sample between Ewing sarcoma and Osteosarcoma patients, we found that ADC measurements did not differ significantly between the two. We did find a statistical significance difference between Ewing sarcoma and Osteosarcoma when we compared ΔMinimum-ADC. In general, volume measurements correlated well with response to treatment as is reflected by variability in tumor volume (shrinkage) on both SPIR T1 post-contrast and STIR images (−35.72 ± 78.32 vs −44.03 ± 63.36, respectively), with no discernible difference between the 2 sequences used (SPIR T1 post-contrast vs STIR) (Tables 2 and 3). On the other hand, we found no statistically significant difference of
considered is the existence of a mismatch between ADC sampling and pathology necrosis evaluation. ADC measurement is based on maximal axial surface, whereas pathology necrosis evaluation reflects the entire tumor. This was somewhat reflected in the study by Bajpai et al. [16], where an indirect correlation was found between ADC measurements and pCR when a modified ADC measurement that accounts for the volume of the tumor was used. ADC as a tool and DWI as a modality cannot translate the degree of response to treatment as accurately as pathology and thus cannot direct the specifics of treatment or management. However, it can be used as an early prognostic indicator, because it can assess the presence or absence of response, and that, much earlier than tumor volume monitoring would [13]. The lack of uniform or standardized method for ADC use is another problem evident in the literature. For example, DWI can be affected by field strength: at higher strength (3 T vs 1.5 T), the diffusion gradient becomes more susceptible to artifacts which can influence the ADC measurements [17]. The absence of standard acquisition techniques/ parameters and the variability in post-image processing, timing of
Table 4 Association between ADC (mean, max, min) before and after treatment and Tumor type (Ewing and Osteosarcoma). Type of cancer
Mean ADC
Maximum ADC
Minimum ADC
Volume on STIR
Volume on SPIR T1 post-contrast
Age Gender, male n(%) > 90 % necrosis < 90 % necrosis Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%)
P-value
Ewing N=8
Osteosarcoma N=7
12.38 ± 4.03 6 (75.0) 4 (50) 4 (50) 1120.0 ± 393.4 1422.0 ± 218.1 216.1 ± 464.3 28.46 ± 51.16 2636.7 ± 543.8 2444.7 ± 413.4 −329.1 ± 561.3 −10.37 ± 19.42 131.4 ± 185.3 198.1 ± 260.2 48.0 ± 200.7 −7.26 ± 87.54 318.6 ± 204.3 114.9 ± 83.2 −203.7 ± 150.9 −66.08 ± 15.79 361.9 ± 254.9 94.93 ± 70.42 −266.1 ± 197.9 −75.78 ± 11.99
10.57 ± 2.82 3 (42.9) 4 (57.2) 3 (42.8) 1273.0 ± 330.5 1666.2 ± 425.5 355.3 ± 279.1 28.87 ± 21.98 2652.4 ± 183.0 2564.5 ± 474.0 −97.2 ± 505.1 −3.28 ± 18.78 136.1 ± 270.6 609.0 ± 321.4 450.2 ± 278.5 480.27 ± 399.24 473.1 ± 206.2 238.2 ± 157.8 −234.8 ± 307.2 −18.83 ± 87.67 373.2 ± 147.1 285.1 ± 200.2 −88.1 ± 293.2 4.35 ± 96.96
ADC: apparent diffusion coefficient. STIR: Short TI inversion Recovery. SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05. 7
0.28 0.32 0.52 0.48 0.64 0.20 0.57 0.47 0.73 0.67 0.39 0.47 1.00 0.04 0.02 0.13 0.17 0.08 0.49 0.42 0.56 0.01 0.48 0.03
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Δvolume between good and poor responders, which is probably due to our small sample size. Note that the prevalence of good responders in our small sample could affect the predictive values of measured parameters. In the study by Shin et al., 41 patients with Osteosarcoma were studied for their response to treatment using volume measurement on MRI and it showed a volume change that correlates well with pCR [19]. The nature of a tumor is an even more important factor to be considered when using tumor volume as a means of monitoring treatment response, since volume changes are highly dependent on the type of tumor studied [20]. Ewing sarcoma tends to include more soft tissue component than Osteosarcoma and is expected to have a more pronounced change in tumor volume. The intraosseous component of these tumors tends to remain almost constant while the soft tissue component is the most likely to respond [16]. Osteosarcoma is more likely to ossify than shrink when it is subjected to chemotherapy [20], In fact, we did encounter a difference between Osteosarcoma and Ewing Sarcoma when we measured volume on SPIR T1 post-contrast after treatment with Ewing showing a much smaller volume (p < 0.01) as well as a much larger variability (p < 0.03). Interestingly, neither the Δvolume nor the variability showed any significant difference on STIR when comparing those 2 groups. STIR has a high sensitivity when detecting the extent of intramedullary tumor but is not as reliable as SPIR T1 post-contrast in making the distinction between perilesional intramedullary edema and the tumor itself. In practice, radiologists will look to SPIR T1 post-contrast to make that distinction [20]. DWI/ADC can also play a role in this distinction, since abnormal T2 signal in the marrow won’t show restricted diffusion unless there is malignant involvement. Del Grande et al. [21] categorized the bone marrow lesions into three different categories based on their MRI imaging findings and showed that restricted diffusion can only be seen in case of malignancy or infection.
References [1] G. Ottaviani, N. Jaffe, The epidemiology of osteosarcoma, Cancer Treat. Res. 152 (2009) 3–13. [2] N.M. Marina, et al., Longitudinal follow‐up of adult survivors of Ewing sarcoma: a report from the Childhood Cancer Survivor Study, Cancer 123 (13) (2017) 2551–2560. [3] R.A. Durfee, M. Mohammed, H.H. Luu, Review of osteosarcoma and current management, Rheumatol. Ther. 3 (2) (2016) 221–243. [4] F.M. Costa, E.C. Ferreira, E.M. Vianna, Diffusion-weighted magnetic resonance imaging for the evaluation of musculoskeletal tumors, Magn. Reson. Imaging Clin. N. Am. 19 (1) (2011) 159–180. [5] H. Neubauer, et al., Diffusion-weighted MRI for detection and differentiation of musculoskeletal tumorous and tumor-like lesions in pediatric patients, World J. Pediatr. 8 (4) (2012) 342–349. [6] J.D. Tournier, et al., Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution, Neuroimage 23 (3) (2004) 1176–1185. [7] B.D. Ross, B.A. Moffat, T.S. Lawrence, S.K. Mukherji, S.S. Gebarski, D.J. Quint, T.D. Johnson, L. Junck, P.L. Robertson, K.M. Muraszko, Q. Dong, C.R. Meyer, P.H. Bland, P. McConville, H. Geng, A. Rehemtulla, T.L. Chenevert, Evaluation of cancer therapy using diffusion magnetic resonance imaging, Mol. Cancer Ther. 2 (6) (2003) 581–587 June 1. [8] T. Kubo, et al., Value of diffusion-weighted imaging for evaluating chemotherapy response in osteosarcoma: a meta-analysis, Mol. Clin. Oncol. 7 (1) (2017) 88–92. [9] J. Wang, et al., Correlation between apparent diffusion coefficient and histopathology subtypes of osteosarcoma after neoadjuvant chemotherapy, Acta Radiol. 58 (8) (2017) 971–976. [10] Y. Hayashida, et al., Monitoring therapeutic responses of primary bone tumors by diffusion-weighted image: initial results, Eur. Radiol. 16 (12) (2006) 2637–2643. [11] A.A.K.A. Razek, et al., Role of diffusion-weighted magnetic resonance imaging in differentiation between the viable and necrotic parts of head and neck tumors, Acta Radiol. 49 (3) (2008) 364–370. [12] K. Oka, et al., The value of diffusion-weighted imaging for monitoring the chemotherapeutic response of osteosarcoma: a comparison between average apparent diffusion coefficient and minimum apparent diffusion coefficient, Skeletal Radiol. 39 (2) (2010) 141–146. [13] M. Uhl, et al., Osteosarcoma: preliminary results of in vivo assessment of tumor necrosis after chemotherapy with diffusion- and perfusion-weighted magnetic resonance imaging, Invest. Radiol. 41 (8) (2006) 618–623. [14] A.J. Degnan, C.Y. Chung, A.J. Shah, Quantitative diffusion-weighted magnetic resonance imaging assessment of chemotherapy treatment response of pediatric osteosarcoma and Ewing sarcoma malignant bone tumors, Clin. Imaging 47 (2018) 9–13. [15] H.C. Thoeny, B.D. Ross, Predicting and monitoring cancer treatment response with diffusion-weighted MRI, J. Magn. Reson. Imaging 32 (1) (2010) 2–16. [16] J. Bajpai, et al., Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis, Pediatr. Radiol. 41 (4) (2011) 441–450. [17] S. Mohan, R. Moineddin, G.B. Chavhan, Pediatric whole-body magnetic resonance imaging: intra-individual comparison of technical quality, artifacts, and fixed structure visibility at 1.5 and 3 T, Indian J. Radiol. Imaging 25 (4) (2015) 353–358. [18] D.A. Hamstra, A. Rehemtulla, B.D. Ross, Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology, J. Clin. Oncol. 25 (26) (2007) 4104–4109. [19] K.-H. Shin, et al., Tumor volume change as a predictor of chemotherapeutic response in osteosarcoma, Clin. Orthop. Relat. Res. 376 (2000) 200–208. [20] T.D. Henry, M.E. McCarville, F.A. Hoffer, Diagnostic imaging of pediatric bone and soft tissue sarcomas, in: A. Pappo (Ed.), Pediatric Bone and Soft Tissue Sarcomas, Springer Berlin Heidelberg, Berlin, Heidelberg, 2006, pp. 35–69. [21] F. Del Grande, S.J. Farahani, J.A. Carrino, A. Chhabra, Bone marrow lesions: a systematic diagnostic approach, Indian J. Radiol. Imaging 24 (3) (2014) 279–287, https://doi.org/10.4103/0971-3026.137049.
5. Conclusion ADC can potentially reflect cellular changes associated with chemotherapy use. Similarly, volumetric changes can reflect the response to treatment. However, quantitative use of those parameters to dictate a change in management, treatment regimen or administered chemotherapy dose requires further investigation. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Transparency document The Transparency document associated with this article can be found in the online version. Declaration of Competing Interest None.
8
Contents lists available at ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Research article
The value of diffusion weighted imaging and apparent diffusion coefficient in primary Osteogenic and Ewing sarcomas for the monitoring of response to treatment: Initial experience
T
Karl Asmara, Charbel Saadeb, Rida Salmana, Raya Saabc, Nabil J. Khourya, Miguel Abboudc, Hani Tamimd, Maha Makkid, Lena Naffaaa,* a
Department of Radiology, American University of Beirut Medical Center, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon Faculty of Health Sciences, American University of Beirut, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon Department of Pediatrics and Adolescent Medicine, American University of Beirut Medical Center, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon d Department of Internal Medicine, American University of Beirut Medical Center, Riad El-Solh 1107 2020, PO Box: 11-0236, Beirut, Lebanon b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Magnetic Resonance Imaging (MRI) Osteosarcoma Ewing sarcoma Diffusion Weighted Imaging (DWI) Apparent Diffusion Coefficient (ADC) Post-treatment
Purpose: To assess the value of using Apparent Diffusion Coefficient (ADC) mapping in Diffusion Weighted Imaging (DWI) when monitoring treatment response in pediatric Osteogenic and Ewing sarcomas and to correlate with level of necrosis on post-surgical excision pathology. Method: This retrospective study includes 7 Osteosarcoma and 8 Ewing sarcoma patients. Pre-treatment and post-treatment focal MRIs were evaluated for ADC values, tumor volumes and variability of both measurements. We also compared the measurement between Ewing and Osteosarcoma groups, as well as between good (=/ > 90 % necrosis post-excision) and poor (< 90 % necrosis post-excision) responders. Results: All measurements except Maximum ADC (p = 0.20) showed a statistically significant difference when comparing them before and after treatment. When we segregated our population according to pathologic complete response, there was no difference in ADC measurements, volumetric measurements or either variability between good (8 Patients) and poor responders (7 Patients). When comparing the before-after changes in our measurement between the Ewing sarcoma and Osteosarcoma cases, there was no significant difference in the change between pre and post treatment (Δ) Mean or Maximum ADC, or in Δtumor-volume when measured on STIR or SPIR T1 post-contrast sequences. Only the ΔMinimum-ADC showed a statistically significant difference (p < 0.02) in this group. Conclusions: ADC can potentially reflect cellular changes associated with chemotherapy use, reflecting a response to treatment. However, quantitative use of those parameters to dictate a change in management, treatment regimen or chemotherapy dose in order to target a good response (> / = 90 % necrosis post-excision) needs further investigation.
1. Introduction Primary bone malignancies are uncommon in the pediatric population, however, they rank as the leading cause of death from cancers [1]. Currently, the 5-year survival rate is 70 % due long term and lifelong risk of secondary chronic conditions [2]. The various treatments include chemotherapy and surgery; and the assessment of treatment response prior to surgery is the mainstay of management [3]. Conventional MRI using pulse sequences such as T1 Weighted
(T1W) and T2 Weighted (T2W) with and without intravenous (IV) gadolinium is the imaging test of choice for the detection and differentiation of musculoskeletal tumors. However, it lacks important information regarding tumor cellularity. Diffusion-weighted imaging (DWI), on the other hand has great potential in evaluating tumor cellularity [4], especially via quantifiable parameters like mean Apparent Diffusion Coefficient (ADC) [5]. DWI-MRI has been adopted into mainstream imaging due to its superior diagnostic performance, but it also demonstrates great promise in the assessment of treatment
⁎
Corresponding author. E-mail addresses: [email protected] (K. Asmar), [email protected] (C. Saade), [email protected] (R. Salman), [email protected] (R. Saab), [email protected] (N.J. Khoury), [email protected] (M. Abboud), [email protected] (H. Tamim), [email protected] (M. Makki), [email protected] (L. Naffaa). https://doi.org/10.1016/j.ejrad.2020.108855 Received 7 June 2019; Received in revised form 21 January 2020; Accepted 23 January 2020 0720-048X/ © 2020 Elsevier B.V. All rights reserved.
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
2.3. MRI technique
response [6]. The DWI-MRI relies on a pulse sequence that incorporates additional magnetic field gradients, which makes the intensity of the MR signal dependent on the mobility of the source of the signal. In other words, the denser the cellularity of the examined tissue, the more restricted the diffusion, the higher the MR signal on DWI and the lower the ADC map and vice versa [7]. ADC and ADC ratio measured on DWI MRI were shown in recent studies to increase in tumor necrosis (an indicator of adequate response to treatment) [8]. They were also shown to be good early indicators of prognosis [9]. The literature is however still scarce in data that compares initial ADC pre-chemotherapy to ADC post-chemotherapy especially in the pediatric population. In particular, the data regarding Ewing sarcoma is very limited. The aim of the current study is to investigate the utility of ADC mapping in DWI MRI in pediatric Osteogenic and Ewing sarcomas for the follow-up and monitoring of treatment response, when correlated with the level of necrosis on post-surgical excision pathological results.
Axial, sagittal and coronal SPIR T1 images post-IV gadolinium administration and STIR images were obtained on a 1,5 -Tesla and a 3Tesla field strength magnet (Philips Ingenia, Amsterdam, Holland), with coils adapted to the region of interest. Imaging is obtained 5 min after the intravenous injection of gadolinium. Echo-planar imaging (EPI) diffusion weighted imaging was performed on a 3-Tesla magnet (Philips Ingenia, NSA: 1–5, TR: 7000–10000, TE: 62–66, matrix: 120*80, FOV: 25−35 cm, slice thickness: 4–5.5 mm, slice gap: 0.5–1, b: 0−900 s/mm2) and on a 1.5 -Tesla magnet (Philips Ingenia, NSA: 4, TR: 7000–10000, TE: 63–80, matrix: 128*80, FOV: 25−35 cm, slice thickness: 4–5.5 mm, slice gap: 1 mm, b: 0−900 s/mm2) with coils adapted to the regions of interest.
2.4. Image analysis 2. Methods MR images were analyzed by 2 radiologists experienced in pediatric musculoskeletal imaging by consensus agreement (ABR 15 years). Axial ADC map images were obtained. The tumor was delineated at the level of the largest axial dimensions, based on the axial post-contrast SPIR T1 and linked ADC map images by a free hand region-ofinterest (ROI) to obtain the tumor area and calculated mean, maximum and minimum ADC value measurements (Figs. 2, 3 and 4 ). The ADC unit is x10-3 mm²/s. Three dimensional volumetric measurements were performed using the tumor tracking function of Philips Intellispace on STIR and SPIR T1 post-contrast images for both pre-treatment and post-treatment MRI exams. We also reported the ADC variability as well as tumor volume variability according to the formulas:
2.1. Study design This retrospective study was approved by the Institutional Review Board. We reviewed the electronic medical records of pediatric patients presenting with Ewing sarcoma and Osteosarcoma to the oncology service at our institution between 2010 and 2016. Inclusion criteria consisted of pediatric patients between 1 and 18 years of age, with a diagnosis of Ewing sarcoma or Osteosarcoma that was confirmed by imaging guided biopsy. Post-surgical excision pathology results were also required as well as MRI with diffusion weighted imaging sequences performed at initial diagnosis and post-treatment prior to surgical excision. Patients who did not meet any of these criteria were excluded (Fig. 1). All collected data was anonymized before analysis. When we segregated our population according to pathologic complete response (pCR), we considered a poor response to treatment when a patient had < 90 % necrosis in the excised tumor after surgery and a good response when a patient had a final =/ > 90 % necrosis, as reported by Hayashida et al. [10].
(ADC Post _treatment ) − (ADC Pre _treatment ) ⎤ ADC variability = ⎡ ⎢ ⎥ ADC Pre _treatment ⎣ ⎦ × 100 and
2.2. Image acquisition
Volume variability Reviewed MRI studies included standard sequences: T1WI, T2WI, Short tau inversion recovery (STIR), and Spectral Pre-saturation with Inversion Recovery (SPIR) T1 pre-and post- IV gadolinium administration, in addition to DWI and reconstructed ADC maps.
(volume Post _treatment ) − (volume Pre _treatment ) ⎤ × 100 =⎡ ⎥ ⎢ volume Pre _treatment ⎦ ⎣
Fig. 1. Diagram depicting the patient population and the reasons for exclusion or inclusion in our cohort. 2
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Fig. 2. MRI of a 12-year-old boy presenting with Ewing sarcoma of the right femur with pre-treatment (A) DWI, (B) ADC map, (C) Axial STIR and (D) Axial post contrast SPIR T1 and post-treatment (E) DWI, (F) ADC map, (G) Axial STIR and (H) Axial post contrast SPIR T1 showing the-free hand ROI selection in DWI and ADC maps. This patient was a good responder (> 90 % necrosis). Note the increase in the mean ADC value and decrease in the maximum ADC value on post-treatment ADC map (D).
(mean, maximum, and minimum) and volume (on STIR and post-contrast SPIR T1) before and after treatment was carried out by using the Wilcoxon test. P-value < 0.05 was used to indicate statistical significance.
2.5. Statistical analysis The Statistical Package for Social Sciences (SPSS, V.24, 2016, IBM) was used for data cleansing, management and analyses. Continuous data were reported as means and standard deviation (SD) and were compared between different dependent variables (type of cancer, necrosis) using the Mann-Whitney U test (for non-normal distribution and sample size less than 30). On the other hand, categorical data were reported as numbers and percentages and were compared using the Fisher test (sample size less than 30). The comparison between ADC
3. Results After reviewing the medical records of oncologic pediatric patients between 2010 and 2016, we identified 53 patients, 27 with Ewing sarcoma and 26 with Osteosarcoma. Thirty-eight cases were excluded 3
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Fig. 3. MRI of a 15-year-old boy with Osteosarcoma of the left femur with pre-treatment (A) DWI, (B) ADC map, (C) Axial STIR and (D) Axial post contrast SPIR T1 and post-treatment (E) DWI, (F) ADC map, (G) Axial STIR and (H) Axial post contrast SPIR T1 showing the free-hand ROI selection in DWI and ADC maps. This patient was a good responder (> 90 % necrosis). Note the increase in the mean ADC value and decrease in the maximum ADC value on post-treatment ADC map (F).
4
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Fig. 4. MRI of a 12-year-old boy with Ewing Sarcoma of the right humerus with pre-treatment (A) DWI, (B) ADC map, (C) reconstructed Axial STIR and (D) Axial post contrast SPIR T1 and post-treatment (E) DWI, (F) ADC map, (G) Axial STIR and (H) Axial post contrast SPIR T1 showing the free-hand ROI selection in ADC maps. This patient was a poor responder (< 90 % necrosis).
5
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Table 1 Comparison between ADC (mean, max, min) and volume (STIR, SPIR) before and after treatment. Treatment
Mean ADC Maximum ADC Minimum ADC Volume on STIR Volume on SPIR T1 Post-contrast
p-value
Before N = 15
After N = 15
1191.4 ± 361.1 2644.1 ± 402.9 133.6 ± 220.3 390.7 ± 213.2 367.6 ± 199.9
1534.7 ± 339.5 2500.0 ± 427.7 387.8 ± 349.8 172.4 ± 134.8 183.7 ± 171.2
0.03 0.20 0.02 0.01 0.02
Variability
28.65 ± 38.86 −7.10 ± 18.68 236.51 ± 371.66 −44.03 ± 63.36 −35.72 ± 78.32
ADC: apparent diffusion coefficient, STIR: Short TI Inversion Recovery, SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05.
4. Discussion
due to the unavailability of initial MRI, lack of DWI sequence on initial imaging or due to previous surgery/recurrence of disease (Fig. 1). A total of 15 cases were included. Of those cases, eight (53.3 %) were Ewing sarcoma while seven (46.7 %) were Osteosarcoma cases. The average patient age was 11.53 ± 3.52 years-old, nine cases were male and six were females. All patients received the same chemotherapy regimen as per standard of care for Ewing sarcoma and Osteosarcoma. The average time between initial MRI and follow up MRI prior to surgery was 10 weeks for Osteosarcoma and 16 weeks for Ewing sarcoma. We compared measurements before and after treatment and we found statistically significant differences in all measurements except Maximum ADC (p = 0.20), as shown in Table 1. Additionally, when we compared the ΔVolume and volume variability measured on SPIR T1 post-contrast to the ΔVolume and volume variability measured on STIR, we found no statistically significant difference between the 2 sequences (p < 0.3) (Table 2) Seven patients were poor responders and eight patients were good responders. We found no statistically significant difference in ADC measurements, volumetric measurements or either variability between good and poor responders (Table 3). Comparison between Ewing sarcoma and Osteosarcoma in terms of baseline and demographic characteristics are presented in Table 4. The average age was not significantly different between our Ewing and Osteosarcoma cases (12.38 ± 4.03 years-old and 10.57 ± 2.82 yearsold respectively). When comparing our before and after treatment measurements between Ewing sarcoma and Osteosarcoma cases, we found no statistically significant difference in the ΔMean-ADC, the ΔMaximum-ADC or between Δtumor-volume, when measured on STIR or SPIR T1 post-contrast sequences. On the other hand, the ΔMinimum-ADC showed a statistically significant difference (p < 0.02) when comparing the two groups. Additionally, the Minimum ADC measurement post-treatment was significantly higher in Osteosarcoma cases (p < 0.04). However, there were no significant differences in the variability of ADC measurements between Ewing sarcoma and Osteosarcoma in any of the parameters (mean, max or minimum). Similarly, the volume on SPIR T1 post-contrast following treatment was significantly higher in Osteosarcoma (p < 0.01). While the Δvolume on SPIR T1 post-contrast was not significantly different, the variability of tumor volume on this sequence was significantly different between Ewing and Osteosarcoma groups (p < 0.03) with a more pronounced change in Ewing sarcoma.
DWI with ADC map is sensitive in the imaging of molecular motion within tissues. After undergoing repeated cycles of chemotherapy, cellularity of tumors decreases and necrosis increases. The evaluation of response to treatment via DWI is based on the nature of the change in tumor constitution when exposed to chemotherapy. This cellular phenomenon should, in theory, be reflected by ADC mapping in DWI-MRI [7]. ADC mapping has been shown to be able to differentiate between the necrotic and viable parts of a tumor [11]. In fact, several studies have demonstrated an increase in ADC following chemotherapy, particularly in tumors of the brain and liver [12], and the relationship between cell necrosis and increased ADC values has been found to be significant in Osteosarcoma [13]. In the current study, we found results that are concordant with the literature, specifically the increase in minimum or mean ADC values with a more pronounced increase in minimum ADC (236.51 ± 71.66) than in mean ADC (28.65 ± 38.86) when comparing our population before and after treatment (Table 2). However, our data reported a decrease in maximum ADC measurement (−7.10 ± 8.68), which is not compatible with the study by Degnan et al. [14] that reported an increase in all three ADC measurements. It has been suggested that the only parameter of real significance for monitoring response to treatment is the minimum ADC which reflects the cellular component of the tumor more strongly than the mean or maximum ADC [12]. The mean or maximum ADC may be more influenced by the cystic components of the tumor and might not be as reliable as minimum ADC when assessing cellularity or necrosis of a tumor [12]. In fact, our results could be explained by a larger cystic component in our initial tumors. It could also be related to limitations concerning the region of interest (ROI) used to measure the ADC values. The ROI could include areas that affect the accuracy of ADC measurements such as perilesional edema. It also does not take into consideration the heterogeneity of the tumor. If the ROI used for measurement happens to include both necrotic and viable areas of the tumor, the changes in ADC will be reflected according to their proportions. The ADC value can be decreased if spatially varying diffusion values occur within the particular ROI selected [15]. The initial promising result of DWI and ADC measurements in the assessment of tumors after treatment was not carried to the analysis of the group according to pCR. ADC mapping did not show any significant difference when comparing the measured values of good and poor responders. We had an equal number of good and poor responders, and found very similar patterns of change in all ADC parameters. This finding is in conflict with similar studies in the literature that suggested a good correlation between ADC and response to treatment proven by pCR. Hayashida et al. [10] reported good correlation between good and poor responders and the ADC measurements in a sample of 18 patients. The sample in that study included mostly Osteosarcoma cases (only 2/ 18 were Ewing Sarcoma). Nevertheless, Bajpai et al. [16] reported in a prospective study with 31 patients with Osteosarcoma, an absence of direct correlation between ADC and pCR. An important point to be
Table 2 Comparison of Volume change before and after treatment (Δvolume) and variability between SPIR T1 Post-contrast and STIR Sequences.
ΔVolume Variability
SPIR T1 Post-contrast
STIR
p-value
−177.12 ± 257.41 −35.72 ± 78.32
−218.26 ± 228.25 −44.03 ± 63.36
0.30 0.68
STIR: Short TI Inversion Recovery. SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05. 6
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Table 3 Association between ADC (mean, max, min) before and after treatment and necrosis. Final necrosis
Mean ADC Maximum ADC Minimum ADC Volume on STIR Volume on SPIR T1 post-contrast
Age Gender, male n (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%) Difference (Δ) Variability (%)
P-value
< 90 N=7
≥90 N=8
11.43 ± 3.74 3 (42.9) 370.0 ± 484.3 42.15 ± 50.19 −126.2 ± 683.8 −3.41 ± 23.95 227.2 ± 225.4 256.58 ± 422.59 −103.7 ± 255.3 −14.78 ± 85.54 −95.5 ± 339.8 −6.92 ± 104.92
11.63 ± 3.58 6 (75.0) 203.6 ± 284.9 17.08 ± 24.14 −304.3 ± 387.1 −10.26 ± 13.96 239.1 ± 386.8 216.44 ± 406.85 −318.5 ± 153.9 −69.63 ± 12.33 −258.8 ± 111.9 −64.51 ± 18.64
0.78 0.32 0.25 0.32 0.57 0.67 0.89 0.83 0.08 0.25 0.11 0.48
ADC: apparent diffusion coefficient. STIR: Short TI Inversion Recovery. SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05.
evaluation and ADC parameter used (mean vs minimum vs maximum), have resulted in challenges for validating DWI and ADC for quantitative treatment response monitoring and has contributed to conflicting results in the literature [18]. It is also paramount to consider the nature of the tumor being studied. Different tumors will respond differently to chemotherapy and ADC should be most valuable in tumors that present with significant necrosis. In comparing ADC measurements within our sample between Ewing sarcoma and Osteosarcoma patients, we found that ADC measurements did not differ significantly between the two. We did find a statistical significance difference between Ewing sarcoma and Osteosarcoma when we compared ΔMinimum-ADC. In general, volume measurements correlated well with response to treatment as is reflected by variability in tumor volume (shrinkage) on both SPIR T1 post-contrast and STIR images (−35.72 ± 78.32 vs −44.03 ± 63.36, respectively), with no discernible difference between the 2 sequences used (SPIR T1 post-contrast vs STIR) (Tables 2 and 3). On the other hand, we found no statistically significant difference of
considered is the existence of a mismatch between ADC sampling and pathology necrosis evaluation. ADC measurement is based on maximal axial surface, whereas pathology necrosis evaluation reflects the entire tumor. This was somewhat reflected in the study by Bajpai et al. [16], where an indirect correlation was found between ADC measurements and pCR when a modified ADC measurement that accounts for the volume of the tumor was used. ADC as a tool and DWI as a modality cannot translate the degree of response to treatment as accurately as pathology and thus cannot direct the specifics of treatment or management. However, it can be used as an early prognostic indicator, because it can assess the presence or absence of response, and that, much earlier than tumor volume monitoring would [13]. The lack of uniform or standardized method for ADC use is another problem evident in the literature. For example, DWI can be affected by field strength: at higher strength (3 T vs 1.5 T), the diffusion gradient becomes more susceptible to artifacts which can influence the ADC measurements [17]. The absence of standard acquisition techniques/ parameters and the variability in post-image processing, timing of
Table 4 Association between ADC (mean, max, min) before and after treatment and Tumor type (Ewing and Osteosarcoma). Type of cancer
Mean ADC
Maximum ADC
Minimum ADC
Volume on STIR
Volume on SPIR T1 post-contrast
Age Gender, male n(%) > 90 % necrosis < 90 % necrosis Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%) Pre-treatment Post-treatment Difference (Δ) Variability (%)
P-value
Ewing N=8
Osteosarcoma N=7
12.38 ± 4.03 6 (75.0) 4 (50) 4 (50) 1120.0 ± 393.4 1422.0 ± 218.1 216.1 ± 464.3 28.46 ± 51.16 2636.7 ± 543.8 2444.7 ± 413.4 −329.1 ± 561.3 −10.37 ± 19.42 131.4 ± 185.3 198.1 ± 260.2 48.0 ± 200.7 −7.26 ± 87.54 318.6 ± 204.3 114.9 ± 83.2 −203.7 ± 150.9 −66.08 ± 15.79 361.9 ± 254.9 94.93 ± 70.42 −266.1 ± 197.9 −75.78 ± 11.99
10.57 ± 2.82 3 (42.9) 4 (57.2) 3 (42.8) 1273.0 ± 330.5 1666.2 ± 425.5 355.3 ± 279.1 28.87 ± 21.98 2652.4 ± 183.0 2564.5 ± 474.0 −97.2 ± 505.1 −3.28 ± 18.78 136.1 ± 270.6 609.0 ± 321.4 450.2 ± 278.5 480.27 ± 399.24 473.1 ± 206.2 238.2 ± 157.8 −234.8 ± 307.2 −18.83 ± 87.67 373.2 ± 147.1 285.1 ± 200.2 −88.1 ± 293.2 4.35 ± 96.96
ADC: apparent diffusion coefficient. STIR: Short TI inversion Recovery. SPIR: Spectral Presaturation with Inversion Recovery. p-value < 0.05. 7
0.28 0.32 0.52 0.48 0.64 0.20 0.57 0.47 0.73 0.67 0.39 0.47 1.00 0.04 0.02 0.13 0.17 0.08 0.49 0.42 0.56 0.01 0.48 0.03
European Journal of Radiology 124 (2020) 108855
K. Asmar, et al.
Δvolume between good and poor responders, which is probably due to our small sample size. Note that the prevalence of good responders in our small sample could affect the predictive values of measured parameters. In the study by Shin et al., 41 patients with Osteosarcoma were studied for their response to treatment using volume measurement on MRI and it showed a volume change that correlates well with pCR [19]. The nature of a tumor is an even more important factor to be considered when using tumor volume as a means of monitoring treatment response, since volume changes are highly dependent on the type of tumor studied [20]. Ewing sarcoma tends to include more soft tissue component than Osteosarcoma and is expected to have a more pronounced change in tumor volume. The intraosseous component of these tumors tends to remain almost constant while the soft tissue component is the most likely to respond [16]. Osteosarcoma is more likely to ossify than shrink when it is subjected to chemotherapy [20], In fact, we did encounter a difference between Osteosarcoma and Ewing Sarcoma when we measured volume on SPIR T1 post-contrast after treatment with Ewing showing a much smaller volume (p < 0.01) as well as a much larger variability (p < 0.03). Interestingly, neither the Δvolume nor the variability showed any significant difference on STIR when comparing those 2 groups. STIR has a high sensitivity when detecting the extent of intramedullary tumor but is not as reliable as SPIR T1 post-contrast in making the distinction between perilesional intramedullary edema and the tumor itself. In practice, radiologists will look to SPIR T1 post-contrast to make that distinction [20]. DWI/ADC can also play a role in this distinction, since abnormal T2 signal in the marrow won’t show restricted diffusion unless there is malignant involvement. Del Grande et al. [21] categorized the bone marrow lesions into three different categories based on their MRI imaging findings and showed that restricted diffusion can only be seen in case of malignancy or infection.
References [1] G. Ottaviani, N. Jaffe, The epidemiology of osteosarcoma, Cancer Treat. Res. 152 (2009) 3–13. [2] N.M. Marina, et al., Longitudinal follow‐up of adult survivors of Ewing sarcoma: a report from the Childhood Cancer Survivor Study, Cancer 123 (13) (2017) 2551–2560. [3] R.A. Durfee, M. Mohammed, H.H. Luu, Review of osteosarcoma and current management, Rheumatol. Ther. 3 (2) (2016) 221–243. [4] F.M. Costa, E.C. Ferreira, E.M. Vianna, Diffusion-weighted magnetic resonance imaging for the evaluation of musculoskeletal tumors, Magn. Reson. Imaging Clin. N. Am. 19 (1) (2011) 159–180. [5] H. Neubauer, et al., Diffusion-weighted MRI for detection and differentiation of musculoskeletal tumorous and tumor-like lesions in pediatric patients, World J. Pediatr. 8 (4) (2012) 342–349. [6] J.D. Tournier, et al., Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution, Neuroimage 23 (3) (2004) 1176–1185. [7] B.D. Ross, B.A. Moffat, T.S. Lawrence, S.K. Mukherji, S.S. Gebarski, D.J. Quint, T.D. Johnson, L. Junck, P.L. Robertson, K.M. Muraszko, Q. Dong, C.R. Meyer, P.H. Bland, P. McConville, H. Geng, A. Rehemtulla, T.L. Chenevert, Evaluation of cancer therapy using diffusion magnetic resonance imaging, Mol. Cancer Ther. 2 (6) (2003) 581–587 June 1. [8] T. Kubo, et al., Value of diffusion-weighted imaging for evaluating chemotherapy response in osteosarcoma: a meta-analysis, Mol. Clin. Oncol. 7 (1) (2017) 88–92. [9] J. Wang, et al., Correlation between apparent diffusion coefficient and histopathology subtypes of osteosarcoma after neoadjuvant chemotherapy, Acta Radiol. 58 (8) (2017) 971–976. [10] Y. Hayashida, et al., Monitoring therapeutic responses of primary bone tumors by diffusion-weighted image: initial results, Eur. Radiol. 16 (12) (2006) 2637–2643. [11] A.A.K.A. Razek, et al., Role of diffusion-weighted magnetic resonance imaging in differentiation between the viable and necrotic parts of head and neck tumors, Acta Radiol. 49 (3) (2008) 364–370. [12] K. Oka, et al., The value of diffusion-weighted imaging for monitoring the chemotherapeutic response of osteosarcoma: a comparison between average apparent diffusion coefficient and minimum apparent diffusion coefficient, Skeletal Radiol. 39 (2) (2010) 141–146. [13] M. Uhl, et al., Osteosarcoma: preliminary results of in vivo assessment of tumor necrosis after chemotherapy with diffusion- and perfusion-weighted magnetic resonance imaging, Invest. Radiol. 41 (8) (2006) 618–623. [14] A.J. Degnan, C.Y. Chung, A.J. Shah, Quantitative diffusion-weighted magnetic resonance imaging assessment of chemotherapy treatment response of pediatric osteosarcoma and Ewing sarcoma malignant bone tumors, Clin. Imaging 47 (2018) 9–13. [15] H.C. Thoeny, B.D. Ross, Predicting and monitoring cancer treatment response with diffusion-weighted MRI, J. Magn. Reson. Imaging 32 (1) (2010) 2–16. [16] J. Bajpai, et al., Role of MRI in osteosarcoma for evaluation and prediction of chemotherapy response: correlation with histological necrosis, Pediatr. Radiol. 41 (4) (2011) 441–450. [17] S. Mohan, R. Moineddin, G.B. Chavhan, Pediatric whole-body magnetic resonance imaging: intra-individual comparison of technical quality, artifacts, and fixed structure visibility at 1.5 and 3 T, Indian J. Radiol. Imaging 25 (4) (2015) 353–358. [18] D.A. Hamstra, A. Rehemtulla, B.D. Ross, Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology, J. Clin. Oncol. 25 (26) (2007) 4104–4109. [19] K.-H. Shin, et al., Tumor volume change as a predictor of chemotherapeutic response in osteosarcoma, Clin. Orthop. Relat. Res. 376 (2000) 200–208. [20] T.D. Henry, M.E. McCarville, F.A. Hoffer, Diagnostic imaging of pediatric bone and soft tissue sarcomas, in: A. Pappo (Ed.), Pediatric Bone and Soft Tissue Sarcomas, Springer Berlin Heidelberg, Berlin, Heidelberg, 2006, pp. 35–69. [21] F. Del Grande, S.J. Farahani, J.A. Carrino, A. Chhabra, Bone marrow lesions: a systematic diagnostic approach, Indian J. Radiol. Imaging 24 (3) (2014) 279–287, https://doi.org/10.4103/0971-3026.137049.
5. Conclusion ADC can potentially reflect cellular changes associated with chemotherapy use. Similarly, volumetric changes can reflect the response to treatment. However, quantitative use of those parameters to dictate a change in management, treatment regimen or administered chemotherapy dose requires further investigation. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Transparency document The Transparency document associated with this article can be found in the online version. Declaration of Competing Interest None.
8