Abstract #4480
Predicting breast cancer treatment response using a hybrid deep learning network on multislice SyntheticMR images
Jong Bum Son1, Ken-Pin Hwang1, David E Rauch1, Ju Hee Ahn2, Jiyoung Lee3, Zijian Zhou1, Bikash Panthi4, Beatriz Adrada4, Rosalind P Candelaria4, Jason B White5, Mary Guirguis4, Rania M Mohamed6, Elizabeth E Ravenberg7, Clinton Yam7, Debasish Tripathy7, and Jingfei Ma1
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2Radiology Department, GangNam Radiology Clinic, Busan, Korea, Republic of, 3College of Medicine, Chung-Ang University, Seongnam-si, Korea, Republic of, 4Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 5Department of Moon Shots Operations, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 6Department of Breast Imaging Research, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 7Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
Synopsis
We developed a hybrid deep learning network combining convolutional neural (CNN) and long short-term memory (LTSM) networks to predict slice-to-slice consistent responses to neoadjuvant systematic therapy (NAST) in triple negative breast cancer (TNBC) patients using multislice quantitative SyntheticMR images. We demonstrated that neural networks originally developed for video feature classification can be adapted to predict treatment response of cancer patients using MR images. Our hybrid network was able to overcome the slice-to-slice inconsistency that would have resulted if a 2D network is applied directly, therefore providing higher prediction accuracy.
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