Multiparametric MRI-Based Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

Authors

Rania M Mohamed
Bikash Panthi
Beatriz E Adrada
Medine Boge
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 M Patel
Brandy Reed
Marion E Scoggins
Jong Bum Son
Alastair Thompson
Debu Tripathy
Vicente Valero
Peng Wei
Jason White
Gary J Whitman
Zhan Xu
Wei Yang
Clinton Yam
Jingfei Ma
Gaiane M Rauch

Publication Date

7-12-2024

Journal

Scientific Reports

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.

Keywords

Humans, Triple Negative Breast Neoplasms, Female, Neoadjuvant Therapy, Middle Aged, Multiparametric Magnetic Resonance Imaging, Adult, Aged, Treatment Outcome, ROC Curve, Magnetic Resonance Imaging, Radiomics

Comments

Supplementary Materials

PMID: 38992094