{
  "abstract": "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.\n\nMethods: 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.\n\nResults: 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%).\n\nConclusions: 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.\n\nSystematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024574566 , identifier CRD42024574566.",
  "abstract_shown": true,
  "affiliations": [
    "Khoja Akhmet Yassawi International Kazakh-Turkish University",
    "School of Digital Technologies, Almaty Management University",
    "Office of the Vice Rector for Science and Strategic Development, Khoja Akhmet Yassawi International Kazakh-Turkish University",
    "Center for Strategic Development, Ranking, and Quality, Khoja Akhmet Yassawi International Kazakh-Turkish University",
    "Health Research Institute, Al-Farabi Kazakh National University",
    "Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University",
    "Department of Epidemiology, Biostatistics and Evidence-Based Medicine, Al-Farabi Kazakh National University",
    "Faculty of Medicine and Health Care, Al-Farabi Kazakh National University",
    "Postgraduate Faculty of Medicine, Khoja Akhmet Yassawi International Kazakh-Turkish University"
  ],
  "article_type": null,
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  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Gauhar Dunenova"
    },
    {
      "affs": [
        1,
        0
      ],
      "name": "Aidos Sarsembayev"
    },
    {
      "affs": [
        2
      ],
      "name": "Ainash Oshibayeva"
    },
    {
      "affs": [
        3
      ],
      "name": "Saltanat Kyrykbayeva"
    },
    {
      "affs": [
        4
      ],
      "name": "Zinat Abdrakhmanova"
    },
    {
      "affs": [
        5
      ],
      "name": "Balnur Omarova"
    },
    {
      "affs": [
        0
      ],
      "name": "Alexandr Ivankov"
    },
    {
      "affs": [
        0,
        6
      ],
      "name": "Aliya Ualiyeva"
    },
    {
      "affs": [
        7
      ],
      "name": "Balzhan Zhakypova"
    },
    {
      "affs": [
        8
      ],
      "name": "Zhanar Zhagiparova"
    },
    {
      "affs": [
        5
      ],
      "name": "Gulnaz Nuskabayeva"
    },
    {
      "affs": [
        4
      ],
      "name": "Meruyert Suleimenova"
    },
    {
      "affs": [
        4,
        0
      ],
      "name": "Natalya Glushkova"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9t4ydf1heah4z.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1908349",
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  "id": "9t4ydf1heah4z",
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    "2234-943X"
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  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC254-282"
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    [
      "openalex",
      "1702|AI in cancer detection"
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  "pdf_url": "https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2026.1908349/pdf",
  "pmcid": null,
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  "published": "2026-10-26",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/9t4ydf1heah4z.ris",
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  "subjects": [
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      "field": "medicine",
      "origin": "lcc:RC254-282",
      "subfield": "oncology"
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  "title": "Machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer histopathology: a systematic review and evidence-gap analysis of translational readiness",
  "title_text": "Machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer histopathology: a systematic review and evidence-gap analysis of translational readiness",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:26Z",
  "url": "https://research.draconitic.com/paper/9t4ydf1heah4z",
  "venue": "Frontiers in Oncology",
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