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Abstract #0829

Machine Learning for Prediction of Chemoradiation Therapy Response in Patients with Locally-Advanced Rectal Cancer (LARC) Using Pre- and Early-Treatment Follow-up Multiparametric MRI

Yang Zhang1, Liming Shi2, Xiaonan Sun2, Tianye Niu2, Ning Yue3, Tiffany Kwong1,3, Peter Chang4, Melissa Khy1, Daniel Chow1, Min-Ying Su1, and Ke Nie3

1Department of Radiological Sciences, University of California, Irvine, CA, United States, 2Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China, 3Department of Radiation Oncology, Rutgers-The State University of New Jersey, New Brunswick, NJ, United States, 4Department of Radiology, University of California, San Francisco, CA, United States

A convolutional neural network (CNN) was implemented to predict the response of LARC patients receiving neoadjuvant chemoradiation therapy. The pre-treatment MRI, and the early-treatment follow-up MRI done at 2-3 weeks after the initiation of radiation were used. The MRI protocol included T2, DWI and DCE. A total of 41 patients were studied, with 8 pCR, 27 Tumor Regression Grade 1, and 9 TRG 2+3. The prediction accuracy was 0.71-0.89 for pCR vs. non-pCR; 0.70-0.77 for TRG(0+1) vs. TRG(2+3), not very good due to the limitations of a relatively small dataset. Using manually extracted tumor features in conjunction with neural network classifiers may achieve a higher accuracy.

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