Scientific Reports (Jul 2024)
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 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
Affiliations
- Rania M. Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Bikash Panthi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Beatriz E. Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Rosalind P. Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Huiqin Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
- Mary S. Guirguis
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Kelly K. Hunt
- Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center
- Lei Huo
- Department of Pathology, The University of Texas MD Anderson Cancer Center
- Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center
- Jennifer K. Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center
- Tanya W. Moseley
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Sanaz Pashapoor
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Miral M. Patel
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Brandy Reed
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Marion E. Scoggins
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Alastair Thompson
- Department of Surgery, Baylor College of Medicine
- Debu Tripathy
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center
- Vicente Valero
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center
- Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
- Jason White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center
- Gary J. Whitman
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Zhan Xu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Wei Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- Clinton Yam
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center
- Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
- Gaiane M. Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center
- DOI
- https://doi.org/10.1038/s41598-024-66220-9
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 1 – 10
Abstract
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
- Triple-negative breast cancer
- Dynamic contrast-enhanced breast MRI
- Diffusion-weighted imaging
- Neoadjuvant systemic therapy
- Treatment response
- Radiomic features
