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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