Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis

Fu, Ziyu; Huo, Xiaofei; Jing, Andrew B.; Ma, Jingfei; Rauch, Gaiane M.

doi:10.3390/diagnostics16050671

Open AccessReview

Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis

by

Ziyu Fu

¹

,

Xiaofei Huo

²,

Andrew B. Jing

²,

Jingfei Ma

¹

and

Gaiane M. Rauch

^2,*

¹

Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

²

Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA

^*

Author to whom correspondence should be addressed.

Diagnostics 2026, 16(5), 671; https://doi.org/10.3390/diagnostics16050671

Submission received: 31 January 2026 / Revised: 20 February 2026 / Accepted: 24 February 2026 / Published: 26 February 2026

(This article belongs to the Special Issue Advances in Breast Diagnostics)

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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: triple negative breast cancer; artificial intelligence; MR imaging; diagnosis; staging; neoadjuvant therapy

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MDPI and ACS Style

Fu, Z.; Huo, X.; Jing, A.B.; Ma, J.; Rauch, G.M. Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis. Diagnostics 2026, 16, 671. https://doi.org/10.3390/diagnostics16050671

AMA Style

Fu Z, Huo X, Jing AB, Ma J, Rauch GM. Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis. Diagnostics. 2026; 16(5):671. https://doi.org/10.3390/diagnostics16050671

Chicago/Turabian Style

Fu, Ziyu, Xiaofei Huo, Andrew B. Jing, Jingfei Ma, and Gaiane M. Rauch. 2026. "Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis" Diagnostics 16, no. 5: 671. https://doi.org/10.3390/diagnostics16050671

APA Style

Fu, Z., Huo, X., Jing, A. B., Ma, J., & Rauch, G. M. (2026). Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis. Diagnostics, 16(5), 671. https://doi.org/10.3390/diagnostics16050671

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and Prognosis

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