ESTRO 2022 - Abstract Book

S1113

Abstract book

ESTRO 2022

Conclusion Radiomics based patients stratification may be of help in prediction DSS in anal canal cancer patients, prioritizing treatments personalization and follow up programs. Thanks to the positive obtained results, studies on external independent cohorts are ongoing to validate the identified biomarker and additionally, studies combining radiomics and clinical features are needed.

PO-1318 MRI based radiomics as imaging biomarker for response to Neoadjuvant Chemoradiation in Rectal Cancer

A. PUPPALWAR 1 , R. Engineer 2 , S. Kumar 3 , J.S. Goda 4 , P. Nayak 4 , J. Agarwal 4

1 Tata Memorial Hospital , Radiation Oncology, Mumbai, India; 2 Tata Memorial Hospital , Radiation Oncology , Mumbai, India; 3 Tata Memorial Hospital, Radiodiagnosis, Mumbai, India; 4 Tata Memorial Hospital, Radiation Oncology, Mumbai, India Purpose or Objective To study magnetic resonance imaging (MRI) based radiomic features for predicting tumor response to neoadjuvant chemoradiation (NACTRT) in patients of rectal cancers. Materials and Methods We retrospectively studied 100 patients (male:76, female:24) of rectal cancer, who underwent baseline and post-treatment MRI scans 6 weeks after therapy. The treatment protocol consists of NACTRT with concurrent capecitabine. The study was conducted after due approval of institutional ethics committee. Region of interest (ROI) was delineated along the tumor outline in all cross sections of baseline and post-treatment axial T2W MRI imaging. . First order texture (radiomic) features were extracted were extracted and filtered across various spatial scale filters (SSF 0-6) for quantification of histogram derived parameters, namely mean, standard deviation, entropy, mean positive pixel (MPP), skewness, and kurtosis. After NACTRT, 74 patients underwent complete surgical resection and their pathological specimen served as the gold standard for assessing pathological response Remaining 26 patients did not undergo surgery of which 5 were observed as part of wait and watch protocol and 21 were deemed unresectable due to local progression or distant metastasis. Receiving operating characteristic (ROC) curves were generated to distinguish between Complete response (CR) and partial response (PR) + no response (NR) with respect to values of individual texture features. Area under curve (AUC) and metrics such as sensitivity and specificity were used as measures of diagnostic accuracy. Results In the entire cohort of 100 patients, 9 patients achieved radiological CR, whereas 79 had PR and 12 showed NR. Pathologically 22 (29%) achieved CR, 45 (60%) had PR and 7 (9%) showed NR. Among radiomic features of pretreatment scans that could best predict radiological response for the entire cohort, Skewness (SSF-3) (AUC-0.821, 55.5% sensitivity, 96.7% specificity, 93% accuracy) was most predictive of response. On post-treatment scans, MPP (SSF 5) was the best