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Abstract #3417

Comparison between machine learning and radiologists’ readings for prediction of chemoradiation therapy response in rectal cancer using MRI

Yang Zhang1,2, Liming Shi3, Weiwen Zhou3, Xiaonan Sun3, Salma Jabbour1, Ning Yue1, Min-Ying Su2, and Ke Nie1
1Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States, 2Department of Radiological Sciences, University of California, Irvine, CA, United States, 3Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China


A radiomics model with pre-treatment MRI for pCR prediction and compared with two oncologists’ readings. Their performance with and without the model assistances was cross-compared to see the potential role of radiomics model in assisting clinical decision. A total of 203 patients receiving neoadjuvant CRT followed by total mesorectal excision (TME) were enrolled. For training set, AUC from radiomics model is 0.85 and AUC from clinical model is 0.74. For testing set, AUC from radiomics model is 0.82 and AUC from clinical model is 0.69. For oncologist’s reading, with the assistance of radiomics model, positive prediction value (PPV) and specificity increased significantly for both experienced and inexperienced oncologist.

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