MONC - 2019

Section: New Results

T2-based MRI Delta-Radiomics Improve Response Prediction in Soft-Tissue Sarcomas Treated by Neoadjuvant Chemotherapy

Authors: Amandine Crombé, Cynthia Perier, Michèle Kind, Baudouin Denis de Senneville, Francois Le Loarer, Antoine Italiano, Xavier Buy, Olivier Saut. Published in Journal of Magnetic Reasonance Imaging https://hal.inria.fr/hal-01929807v2

Background: Standard of care for patients with high-grade soft-tissue sarcoma (STS) are being redefined since neoadjuvant chemotherapy (NAC) has demonstrated a positive effect on patients’ outcome. Yet, response evaluation in clinical trials still remains on RECIST criteria.

Purpose: To investigate the added value of a Delta-radiomics approach for early response prediction in patients with STS undergoing NAC Study type: Retrospective Population: 65 adult patients with newly-diagnosed, locally-advanced, histologically proven high-grade STS of trunk and extremities. All were treated by anthracycline-based NAC followed by surgery and had available MRI at baseline and after 2 cycles. Field strength/Sequence: Pre- and post-contrast enhanced T1-weighted imaging (T1-WI), turbo spin echo T2-WI at 1.5T.

Assessment: A threshold of <10% viable cells on surgical specimen defined good response (Good-HR). Two senior radiologists performed a semantic analysis of the MRI. After 3D manual segmentation of tumors at baseline and early evaluation, and standardization of voxelsizes and intensities, absolute changes in 33 texture and shape features were calculated. Statistical tests: Classification models based on logistic regression, support vector machine, k-nearest neighbors and random forests were elaborated using cross-validation (training and validation) on 50 patients (‘training cohort’) and was validated on 15 other patients (‘test cohort’).

Results: 16 patients were good-HR. Neither RECIST status, nor semantic radiological variables were associated with response except an edema decrease (p=0.003) although 14 shape and texture features were (range of p-values: 0.002-0.037). On the training cohort, the highest diagnostic performances were obtained with random forests built on 3 features, which provided: AUROC=0.86, accuracy=88.1%, sensitivity=94.1%, specificity=66.3%. On the test cohort, this model provided an accuracy of 74.6% but 3/5 good-HR were systematically ill-classified.

Data conclusions: A T2-based Delta-Radiomics approach can improve early response prediction in STS patients with a limited number of features.