Multiparametric MRI–based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer
Rania M. Mohamed, Bikash Panthi, Beatriz E. Adrada, Medine Böge, Rosalind P. Candelaria, Huiqin Chen, Mary S. Guirguis, Kelly K. Hunt, Lei Huo, Ken‐Pin Hwang, Anil Korkut, Jennifer K. Litton, Tanya W. Moseley, Sanaz Pashapoor, Miral Patel, Brandy Reed, Marion E. Scoggins, Jong Bum Son, Alastair M. Thompson, Debu Tripathy, Vicente Valero, Peng Wei, Jason White, Gary J. Whitman, Zhan Xu, Wei Yang, Clinton Yam, Jingfei Ma, Gaiane M. Rauch
Abstract
Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.