Imaging response evaluation after neoadjuvant treatment in soft tissue sarcomas: Where do we stand?

Soft tissue sarcomas (STS) represent a broad family of rare tumours for which surgery with radiotherapy represents first-line treatment. Recently, neoadjuvant chemo-radiotherapy has been increasingly used in high-risk patients in an effort to reduce surgical morbidity and improve clinical outcomes. An adequate understanding of the efficacy of neoadjuvant therapies would optimise patient care, allowing a tailored approach. Although response evaluation criteria in solid tumours (RECIST) is the most common imaging method to assess tumour response, Choi criteria and functional and molecular imaging (DWI, DCE-MRI and ¹⁸F-FDG-PET) seem to outperform it in the discrimination between responders and non-responders. Moreover, the radiologic-pathology correlation of treatment-related changes remains poorly understood. In this review, we provide an overview of the imaging assessment of tumour response in STS undergoing neoadjuvant treatment, including conventional imaging (CT, MRI, PET) and advanced imaging analysis. Future directions will be presented to shed light on potential advances in pre-surgical imaging assessments that have clinical implications for sarcoma patients.

Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system process

PURPOSE

Accuracy and consistency in RECIST (Response evaluation criteria in solid tumors) measurements are crucial for treatment planning. Manual RECIST measurement is tedious, prone-to-error and operator-subjective. Objective was to develop a fully automated system for tumor segmentation and RECIST score estimation with reasonable accuracy, consistency and speed.

METHODS

Diffusion weight images (DWI) of forty patients (N = 40; Male:Female = 30:10; Age = 17.7 ± 5.9years) with Osteosarcoma was acquired using 1.5 T MRI scanner before (baseline) and after neoadjuvant chemotherapy (follow-up). 3D tumor volume was segmented applying Simple-linear-iterative-clustering Superpixels (SLIC-S) and Fuzzy-c-means-clustering (FCM) separately. Connected-component-analysis was performed to identify image-slice with maximum tumor-burden (Max-burden-sliceno) and measure tumor-sizes (Tumor-diameter(cm) & Tumor-volume(cc)). Relative-percentage-changes in tumor-sizes across time-points were scored using RECIST1.1 and Volumetric-response criterion. Segmentation accuracy was estimated by Dice-coefficient (DC), Jaccard-Index (JI), Precision (P) and Recall (R). Evaluated Apparent-diffusion-coefficient (ADC), Tumor-diameter, Max-burden-sliceno and Tumor-volume in segmented tumor-mask and ground-truth tumor-mask were compared using paired-t-test (p < 0.05), Pearson-correlation-coefficient(PCC) and Bland-Altman plots. Misclassification-error-rate (MER) was evaluated for automated RECIST1.1 and Volumetric-response scoring methods.

RESULTS

Automated SLIC-S and FCM produced satisfactory tumor segmentation (DC:∼70-83%;JI:∼55-72%;P:∼64-85%;R:∼73-83%) and showed excellent correlation with ground-truth measurements in estimating ADC (p > 0.05; PCC=0.84-0.89), Tumor-diameters (p > 0.05; PCC=0.90-0.95; bias=0.3-2.41), Max-burden-sliceno (p > 0.05; PCC=0.87-0.96) and Tumor-volumes (p > 0.05; PCC=0.89-0.94; bias=15.19-131.81) at baseline and follow-up. MER for SLIC-S and FCM were comparable for RECIST1.1 (15-18 %) and Volumetric-response (18-20 %) scores and assessment times were 2-3s and 4-6s per patient respectively.

CONCLUSIONS

Proposed method produced promising segmentation and RECIST score measurements in current bone tumor dataset and might be useful as decision-support-tool for response evaluation in other tumors.

Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities

Background and purpose

In this work, we validate a texture-based model computed from positron emission tomography (PET) and magnetic resonance imaging (MRI) for the prediction of lung metastases in soft-tissue sarcomas (STS). We explore functional imaging at different treatment time points and evaluate the feasibility of radiotherapy dose painting as a potential treatment strategy for patients with higher metastatic risk.

Materials and methods

We acquired fluorodeoxyglucose (FDG)-PET, fluoromisonidazole (FMISO)-PET, diffusion weighting (DW)-MRI and dynamic contrast enhanced (DCE)-MRI data for 18 patients with extremity STS before, during, and after pre-operative radiotherapy. We tested the lung metastases prediction model using pre-treatment images. We evaluated the feasibility of dose painting using volumetric arc therapy (VMAT) via treatment re-planning with a prescription of 50 Gy to the planning target volume (PTV_50Gy) and boost doses of 60 Gy to the FDG hypermetabolic gross tumour volume (GTV_60Gy) and 65 Gy to the low-perfusion DCE-MRI hypoxic GTV contained within the GTV_60Gy (GTV_65Gy).

Results

The texture-based model for lung metastases prediction reached an area under the curve (AUC), sensitivity, specificity and accuracy of 0.71, 0.75, 0.85 and 0.82, respectively. Dose painting resulted in adequate coverage and homogeneity in the re-planned treatments: D_95% to the PTV_50Gy, GTV_60Gy and GTV_65Gy were 50.0 Gy, 60.3 Gy and 65.4 Gy, respectively.

Conclusions

Textural biomarkers extracted from FDG-PET and MRI could be useful to identify STS patients that might benefit from dose escalation. The feasibility of treatment planning with double boost levels to intratumoural GTV functional sub-volumes was established.