Response Estimation Through Spatially Oriented Neural Network and Texture Ensemble (RESONATE)

Abstract

Neoadjuvant chemotherapy (NAC) is considered to be the standard treatment for locally advanced breast cancer, but less than half of all recipients achieve pathological complete response (pCR), necessitating a way to predict pCR prior to NAC. Previous work has shown that pCR prediction is viable via either radiomic or deep learning classification methods when applied to the tumoral region on breast MRI. Others have shown that analysis within the peritumoral region directly outside of the tumor can contribute unique value to pCR prediction. In this work we present Response Estimation through Spatially Oriented Neural Network and Texture Ensemble (RESONATE): an approach to spatially invoke different types of analytic representations in different tumor compartments to create a multi-representation based prediction of response to NAC in breast cancer. A total of 114 NAC recipients with pre-treatment MRI were retrospectively analyzed, with 80 of the patients used for training and 34 held out as an independent testing set. Deep learning and radiomic classifiers were trained separately within the tumor and the peritumoral region, with separate classifier predictions then being fused together via a logistic regression classifier. In the testing set, individual radiomics and deep learning classifiers achieved area under the curve (AUC) values of 0.69 and 0.75 within the tumor, respectively, and 0.69 and 0.66 within the peritumoral region. A weighted fusion of these four classifiers, however, best predicted pCR with an AUC of 0.79. This approach also outperformed fusions incorporating radiomic (AUC = 0.77) or deep learning (AUC = 0.75) only, as well as combinations of representations only within (AUC = 0.78) or outside (AUC = 0.70) the tumor.

J. E. Eben and N. Braman—Equal contribution.

Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers 1F31CA221383-01, 1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01 CA216579-01A1, R01 CA220581-01A1, 1U01 CA239055-01, National Center for Research Resources under award number 1 C06 RR12463-01, VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service, the DOD Prostate Cancer Idea Development Award (W81XWH-15-1-0558), the DOD Lung Cancer Investigator-Initiated Translational Research Award (W81XWH-18-1-0440), the DOD Peer Reviewed Cancer Research Program (W81XWH-16-1-0329), the Ohio Third Frontier Technology Validation Fund, the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, the Department of Defense, or the United States Government.