Front Oncol. 2026 ;16
1908349
Gauhar Dunenova,
Aidos Sarsembayev,
Ainash Oshibayeva,
Saltanat Kyrykbayeva,
Zinat Abdrakhmanova,
Balnur Omarova,
Alexandr Ivankov,
Aliya Ualiyeva,
Balzhan Zhakypova,
Zhanar Zhagiparova,
Gulnaz Nuskabayeva,
Meruyert Suleimenova,
Natalya Glushkova.
Background: Machine learning-based assessment of tumor-infiltrating lymphocytes (TILs) has been proposed to improve the reproducibility and clinical utility of immune-contexture evaluation in breast cancer histopathology. However, existing studies differ substantially in task definition, annotation strategy, model architecture, validation design, and clinical endpoint. This systematic review and evidence-gap analysis aimed to map the translational readiness of TIL-related machine learning studies in breast cancer histopathology and to identify cluster-specific methodological gaps.
Methods: A systematic search of PubMed, Web of Science, Scopus, ScienceDirect, and Google Scholar was conducted up to 20 April 2026, supplemented by backward citation tracing and a targeted Google search of the 30 most relevant additional results. Eligible studies included breast cancer histopathology-based machine learning, deep learning, artificial intelligence, or computational image-analysis studies evaluating TILs or TIL-related immune-cell features. Studies were grouped by principal application into detection/classification, outcome prognostication, and treatment-response prediction. Extracted variables included data source, specimen type, model family, annotation strategy, validation design, clinical comparator use, calibration, decision-curve analysis, incremental value, reproducibility, and methodological challenges. Because of heterogeneity in endpoints and performance metrics, synthesis was descriptive and evidence-map based.
Results: The primary evidence map included 51 histopathology-image studies: 19 detection/classification, 18 outcome prognostication, and 14 treatment-response prediction studies. External validation was reported in 31.6% of detection/classification studies, 61.1% of prognostication studies, and 57.1% of treatment-response prediction studies. Clinical comparator reporting increased across the same continuum, from 63.2% to 94.4% and 92.9%, respectively. However, calibration was reported in only one prognostication study (5.6%) and in no detection/classification or treatment-response prediction studies. Decision-curve analysis was similarly limited to one prognostication study. Class imbalance was near-universal (94.7-100% across clusters). Scanner/stain robustness was addressed in a majority of studies (63.2-83.3%), but fairness or subgroup analyses remained inconsistently reported (27.8-57.1%).
Conclusions: TIL machine learning in breast cancer is technically active but clinically under-validated. Future studies should move beyond model development toward externally validated, calibrated, clinically benchmarked, reproducible, and decision-relevant evidence, using cluster-specific planning standards for detection, prognostication, and treatment-response prediction.
Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024574566, identifier CRD42024574566.
Keywords: artificial intelligence; breast cancer; computational pathology; evidence-gap analysis; machine learning; systematic review; translational readiness; tumor-infiltrating lymphocytes