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Abstract #0101

Prediction of Neoadjuvant Chemoradiation Therapy Response in Rectal Cancer Using Radiomics Compared to Deep Learning Based on Pre-Treatment and mid-RT MRI

Yang Zhang1, Liming Shi2, Ke Nie3, Xiaonan Sun2, Tianye Niu2, Ning Yue3, Tiffany Kwong1,3, Peter Chang1, Daniel Chow1, Jeon-Hor Chen1,4, and Min-Ying Lydia Su1

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, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan

The capability to predict patients’ response to neoadjuvant chemoradiation therapy is important for improving their management. The multi-parametric MRI (T2, DWI, DCE) performed before treatment and after 3-4 weeks of radiation were analyzed to predict final pathological response. Quantitative radiomics was performed using GLCM texture and histogram parameters, and also ROI and deep learning using convolutional neural network (CNN) were performed. Combining quantitative radiomics features with tumor volume and diffusion coefficient could achieve accuracy of 0.86 for pCR vs. non-pCR and 0.93 for GR vs. non-GR, and adding follow-up to pre-treatment MRI could improve accuracy, especially for CNN analysis.

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