Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer

Authors

Casey E Stowers
Chengyue Wu
Zhan Xu
Sidharth Kumar
Clinton Yam
Jong Bum Son
Jingfei Ma
Jonathan I Tamir
Gaiane M Rauch
Thomas E Yankeelov

Language

English

Publication Date

1-1-2025

Journal

Radiology: Artificial Intelligence

DOI

10.1148/ryai.240124

PMID

39503605

PMCID

PMC11791743

PubMedCentral® Posted Date

2-26-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with limited targeted treatment options, heterogeneous treatment response, and high risk of early recurrence. Artificial intelligence (AI) has rapidly emerged as a powerful tool to address key clinical challenges in TNBC across diagnosis, treatment response assessment, and prognosis. Diagnostic and staging challenges persist due to variable imaging features in TNBC and limitations in conventional modalities, increasing the risk of delayed detection. Predicting response to neoadjuvant systemic therapy remains difficult, as patient responses are heterogeneous, and existing clinical markers provide limited early predictive value. Prognostication in TNBC is similarly constrained by the absence of widely used genomic tools and reliance on clinicopathologic factors that incompletely reflect tumor biology. This review summarizes recent advances in AI applications for TNBC across diagnosis, tumor characterization and staging, treatment response prediction, and prognosis, highlighting both emerging opportunities and current limitations in clinical translation.

Keywords

Adult, Aged, Female, Humans, Middle Aged, Chemotherapy, Adjuvant, Deep Learning, Magnetic Resonance Imaging, Neoadjuvant Therapy, Neural Networks, Computer, Retrospective Studies, Treatment Outcome, Triple Negative Breast Neoplasms, Clinical Trials as Topic, triple negative breast cancer, artificial intelligence, MR imaging, diagnosis, staging, neoadjuvant therapy

Published Open-Access

yes

Recommended Citation

Stowers, Casey E; Wu, Chengyue; Xu, Zhan; et al., "Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer" (2025). Faculty, Staff and Student Publications. 6228.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6228