Meeting Banner
Abstract #2326

A deep neural network based model for treatment response prediction using longitudinal diffusion MRI

Yu Gao1,2, Vahid Ghodrati1,2, Anusha Kalbasi3, Jie Fu2,3, Dan Ruan2,3, Minsong Cao2,3, Chenyang Wang3, Fritz C. Eilber4, Nicholas Bernthal5, Susan Bukata5, Sarah M. Dry6, Scott D. Nelson6, Mitchell Kamrava7, John Lewis2,3, Daniel A. Low2,3, Michael Steinberg3, Peng Hu1,2, and Yingli Yang2,3

1Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA, United States, 2Physics and Biology in Medicine IDP, University of California, Los Angeles, Los Angeles, CA, United States, 3Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States, 4Division of Surgical Oncology, Department of Surgery, University of California, Los Angeles, Los Angeles, CA, United States, 5Department of Orthopaedic Surgery, University of California, Los Angeles, Los Angeles, CA, United States, 6Department of Pathology, University of California, Los Angeles, Los Angeles, CA, United States, 7Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

A deep neural network based model was proposed to predict post-radiotherapy treatment effect score for localized soft tissue sarcoma patient using longitudinal diffusion MRI. Diffusion images were acquired three times throughout patient’s hypofractionated radiotherapy treatment. A convolutional neural network was constructed to learn the most useful spatial features from the tumor ADC maps at each time point, which is then fed into a recurrent neural network to exploit the temporal information between the extracted features. Excellent prediction performance of 97.4% accuracy on slice-based classification, and 95% accuracy on patient-based classification were achieved on independent test sets.

This abstract and the presentation materials are available to members only; a login is required.

Join Here