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<p>Breast cancer (BC) is the world’s second most frequent malignancy, affecting approximately 2.3 million women and causing over 600,000 deaths globally. Integrating artificial intelligence (AI) into tumor-infiltrating lymphocytes (TILs) assessment for BC treatment is important in diagnostics and treatment planning. TILs are essential parts of the tumor microenvironment (TME) with established roles as predictive and prognostic biomarkers, especially in aggressive breast cancer subtypes such as triple-negative and HER2-positive breast cancer. The current manual TILs assessment is tedious, subjective, and susceptible to interobserver variability, emphasizing the need for standardized, automated approaches. This narrative review evaluated 27 eligible articles published from January 2020 to January 2024 retrieved from PubMed, Web of Science, ScienceDirect, and Scopus. The review summarizes recent advances in AI-driven TIL assessment, its validation strategies, and potential for integration into clinical workflows. Findings suggest that AI models can enhance diagnostic accuracy, improve reproducibility, and support personalized treatment planning, although challenges remain regarding dataset variability and regulatory approval. In conclusion, with further advancements and validation, AI technology has the potential to revolutionize cancer diagnostics and treatment, leading to improved patient outcomes and more precise oncology care.</p>

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Artificial intelligence for tumor-infiltrating lymphocytes (TILs) assessment in breast cancer: a narrative review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

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  • Nurkhairul Bariyah Baharun,
  • Mohamed Afiq Hidayat Zailani,
  • Afzan Adam,
  • Nasir M. Rajpoot,
  • Qiaoyi XU,
  • Reena Rahayu Md Zin

摘要

Breast cancer (BC) is the world’s second most frequent malignancy, affecting approximately 2.3 million women and causing over 600,000 deaths globally. Integrating artificial intelligence (AI) into tumor-infiltrating lymphocytes (TILs) assessment for BC treatment is important in diagnostics and treatment planning. TILs are essential parts of the tumor microenvironment (TME) with established roles as predictive and prognostic biomarkers, especially in aggressive breast cancer subtypes such as triple-negative and HER2-positive breast cancer. The current manual TILs assessment is tedious, subjective, and susceptible to interobserver variability, emphasizing the need for standardized, automated approaches. This narrative review evaluated 27 eligible articles published from January 2020 to January 2024 retrieved from PubMed, Web of Science, ScienceDirect, and Scopus. The review summarizes recent advances in AI-driven TIL assessment, its validation strategies, and potential for integration into clinical workflows. Findings suggest that AI models can enhance diagnostic accuracy, improve reproducibility, and support personalized treatment planning, although challenges remain regarding dataset variability and regulatory approval. In conclusion, with further advancements and validation, AI technology has the potential to revolutionize cancer diagnostics and treatment, leading to improved patient outcomes and more precise oncology care.