Artificial Intelligence in Triple-Negative Breast Cancer: Applications in Diagnosis, Treatment Response, and
Ziyu Fu1, Xiaofei Huo2, Andrew B Jing2
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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.
Insights
Artificial intelligence (AI) offers new solutions for triple-negative breast cancer (TNBC) challenges in diagnosis, treatment response, and prognosis. This review highlights AI
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Triple-negative breast cancer (TNBC) is an aggressive subtype with limited treatment options and high recurrence rates.
- Current diagnostic and prognostic tools for TNBC have limitations, impacting patient outcomes.
- Predicting treatment response in TNBC is challenging due to patient heterogeneity.
Purpose of the Study:
- To review recent advancements in artificial intelligence (AI) applications for triple-negative breast cancer (TNBC).
- To explore AI's role in improving TNBC diagnosis, staging, treatment response assessment, and prognosis.
- To identify opportunities and limitations for clinical translation of AI in TNBC management.
Main Methods:
- Literature review of recent studies on AI in TNBC.
- Synthesis of findings across AI applications in diagnosis, characterization, staging, treatment response prediction, and prognosis.
- Analysis of current challenges and future directions for AI in clinical practice.
Main Results:
- AI demonstrates potential in enhancing TNBC diagnosis and staging by overcoming limitations of conventional imaging.
- AI tools show promise in predicting response to neoadjuvant systemic therapy, addressing patient heterogeneity.
- AI applications are emerging for improved prognostication in TNBC, complementing existing clinicopathologic factors.
Conclusions:
- AI is a rapidly evolving tool with significant potential to address critical challenges in triple-negative breast cancer.
- Further research and validation are needed to overcome limitations and facilitate the clinical translation of AI in TNBC.
- AI integration may lead to more personalized and effective management strategies for TNBC patients.
