MRI-Based Prediction of Immunotherapy Response in Breast Cancer: A Systematic Review
- 1Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10461, USA
- 2School of Medicine, Meharry Medical College, Nashville, TN 37208, USA
Abstract
Background: Immune checkpoint inhibitors (ICIs) have been shown to improve long term outcomes and pathological complete response (pCR) rates in triple-negative breast cancer (TNBC). However, treatment response remains heterogeneous. Magnetic resonance imaging (MRI) has emerged as a promising noninvasive biomarker for predicting response to immunotherapy-containing treatment regimens. Purpose: To systematically review studies evaluating MRI biomarkers for predicting response to immunotherapy-containing treatment regimens in breast cancer. Methods: A PRISMA-guided systematic review of PubMed was performed for studies published between June 2019 and June 2026 evaluating MRI-based prediction of pCR or immunotherapy response in breast cancer. Data regarding study design, MRI techniques, predictive performance, and study characteristics were extracted and qualitatively synthesized. Results: Across the ten included studies, conventional MRI generally demonstrated moderate predictive performance (AUC 0.65–0.75), whereas multiparametric radiomics models achieved the highest predictive performance (AUC up to 0.88). Several consistent themes emerged. Studies evaluating neoadjuvant chemoimmunotherapy (NACI) reported increased MRI-pathology discordance and reduced reliability of enhancement-based assessment alone, suggesting that conventional MRI response assessment may require refinement in this treatment setting. Functional MRI biomarkers, including diffusion-weighted imaging and dynamic enhancement metrics, identified treatment response earlier than baseline morphologic imaging, while radiomics models integrating MRI with clinical or molecular biomarkers consistently demonstrated the strongest predictive performance. However, evidence remained limited by heterogeneous imaging protocols, small study populations, and limited external validation. Conclusions: MRI, particularly multiparametric and radiomics-enhanced approaches, is evolving beyond conventional morphologic assessment to provide complementary information regarding tumor biology and treatment response in patients undergoing NACI. However, standardized imaging protocols and prospective multicenter validation are needed before MRI-derived biomarkers can be incorporated into routine clinical decision-making.
Related
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- Machine learning model for the prediction of pathological complete response after neoadjuvant chemotherapy in breast cancer.
BibTeX
@article{mehta2026mribased,
title = {{MRI-Based Prediction of Immunotherapy Response in Breast Cancer: A Systematic Review}},
author = {Juhi Mehta and Cassidy Adams and Christian Sellers and Kevin E. Duong and Takouhie Maldjian},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18203267},
url = {https://www.mdpi.com/2072-6694/18/20/3267},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - MRI-Based Prediction of Immunotherapy Response in Breast Cancer: A Systematic Review
AU - Juhi Mehta
AU - Cassidy Adams
AU - Christian Sellers
AU - Kevin E. Duong
AU - Takouhie Maldjian
PY - 2026
DA - 2026/10/01
JO - Cancers
DO - 10.3390/cancers18203267
UR - https://www.mdpi.com/2072-6694/18/20/3267
AB - Background: Immune checkpoint inhibitors (ICIs) have been shown to improve long term outcomes and pathological complete response (pCR) rates in triple-negative breast cancer (TNBC). However, treatment response remains heterogeneous. Magnetic resonance imaging (MRI) has emerged as a promising noninvasive biomarker for predicting response to immunotherapy-containing treatment regimens. Purpose: To systematically review studies evaluating MRI biomarkers for predicting response to immunotherapy-containing treatment regimens in breast cancer. Methods: A PRISMA-guided systematic review of PubMed was performed for studies published between June 2019 and June 2026 evaluating MRI-based prediction of pCR or immunotherapy response in breast cancer. Data regarding study design, MRI techniques, predictive performance, and study characteristics were extracted and qualitatively synthesized. Results: Across the ten included studies, conventional MRI generally demonstrated moderate predictive performance (AUC 0.65–0.75), whereas multiparametric radiomics models achieved the highest predictive performance (AUC up to 0.88). Several consistent themes emerged. Studies evaluating neoadjuvant chemoimmunotherapy (NACI) reported increased MRI-pathology discordance and reduced reliability of enhancement-based assessment alone, suggesting that conventional MRI response assessment may require refinement in this treatment setting. Functional MRI biomarkers, including diffusion-weighted imaging and dynamic enhancement metrics, identified treatment response earlier than baseline morphologic imaging, while radiomics models integrating MRI with clinical or molecular biomarkers consistently demonstrated the strongest predictive performance. However, evidence remained limited by heterogeneous imaging protocols, small study populations, and limited external validation. Conclusions: MRI, particularly multiparametric and radiomics-enhanced approaches, is evolving beyond conventional morphologic assessment to provide complementary information regarding tumor biology and treatment response in patients undergoing NACI. However, standardized imaging protocols and prospective multicenter validation are needed before MRI-derived biomarkers can be incorporated into routine clinical decision-making.
SN - 2072-6694
ER -