{
  "abstract": "The reliability of emergent histopathology foundation models (Histo-FMs) for real-world diagnostic tasks remains unproven. Given the complexity of diagnostic tasks and the challenges inherent in real-world data, we utilized Histo-FMs to investigate their utility for morphological subtyping of Barrett's esophagus (BE) and detecting esophageal adenocarcinoma (EAC) across the BE progression spectrum. We benchmarked Histo-FMs on the BarrettNet cohort, spanning diagnostic categories ranging from normal to EAC (N2EAC). The dataset comprised 3528 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) from 790 patients. A multi-magnification, multi-backbone aggregation of the five most expert-consistent single-scale models yielded incremental performance gains (AUROC of 0.907, F1-score of 0.696, accuracy of 0.795, and κ of 0.651 for morphological subtyping; AUROC of 0.909, F1-score of 0.836, accuracy of 0.959, and κ of 0.673 for EAC detection), with non-uniform gains across single-model comparisons and limited benefit for dysplasia. The findings support the capability of Histo-FMs for BE morphological subtyping and EAC detection in a multicenter clinical cohort, while highlighting persistent challenges in dysplasia assessment and the need for external validation. This study provides a foundation for future clinical validation.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany. azar.kazemi@tum.de.",
    "Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. azar.kazemi@tum.de.",
    "Munich Center for Machine Learning, Munich, Germany. azar.kazemi@tum.de.",
    "TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.",
    "AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Munich, Germany.",
    "Institute of Pathology, School of Medicine and Health, Technical University of Munich, Munich, Germany.",
    "Munich Center for Machine Learning, Munich, Germany.",
    "Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany.",
    "Klinikum Rechts Der Isar, Medical Clinic and Polyclinic II, Technical University of Munich, Munich, Germany.",
    "Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.",
    "Pharmaceutical Sciences Research Center, Institute of Pharmaceutical Technology, Mashhad University of Medical Sciences, Mashhad, Iran.",
    "Department of Medical Informatics, University of Amsterdam, Amsterdam, the Netherlands.",
    "Department of Medicine II, University Medical Center Freiburg, Freiburg, Germany.",
    "Department of Nephrology, School of Medicine, Technical University of Munich, Munich, Germany.",
    "Pathologie Starnberg MVZ GmbH, Starnberg, Bayern, Germany.",
    "Munich Data Science Institute, Munich, Germany."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Azar Kazemi"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Camillo Saueressig"
    },
    {
      "affs": [
        5,
        6,
        3
      ],
      "name": "Jingsong Liu"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Han Li",
      "orcid": "0000-0002-8029-1498"
    },
    {
      "affs": [
        5
      ],
      "name": "Julius Shakhtour"
    },
    {
      "affs": [
        5
      ],
      "name": "Bianca Grosser"
    },
    {
      "affs": [
        5
      ],
      "name": "Fabian Stögbauer",
      "orcid": "0000-0002-8897-5165"
    },
    {
      "affs": [
        8
      ],
      "name": "Julia Horstmann"
    },
    {
      "affs": [
        6,
        7
      ],
      "name": "Nassir Navab"
    },
    {
      "affs": [
        9,
        10,
        11
      ],
      "name": "Saeid Eslami"
    },
    {
      "affs": [
        12
      ],
      "name": "Michael Quante",
      "orcid": "0000-0002-8497-582X"
    },
    {
      "affs": [
        5,
        13,
        14
      ],
      "name": "Julia Slotta-Huspenina"
    },
    {
      "affs": [
        5,
        6,
        3,
        15
      ],
      "name": "Peter J Schüffler",
      "orcid": "0000-0002-1353-8921"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6znxevexn7pt2.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-03329-3",
  "fulltext_url": "https://europepmc.org/articles/PMC13694699",
  "id": "6znxevexn7pt2",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://europepmc.org/article/MED/42919834",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R858-859.7"
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      "openalex",
      "1702|AI in cancer detection"
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  "openalex": "W7223105170",
  "pdf_url": "https://europepmc.org/articles/PMC13694699?pdf=render",
  "pmcid": "PMC13694699",
  "pmid": "42919834",
  "published": "2026-10-21",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/6znxevexn7pt2.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42919834",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42919834"
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    {
      "first_seen": "2026-10-30T13:46:23Z",
      "native_id": "8017dbbabab44d9488fdb5eaa634e200",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R858-859.7",
      "subfield": "general"
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  ],
  "title": "Towards reliable morphological subtyping and cancer detection on Barrett's esophagus pathology through ensembling foundation models.",
  "title_text": "Towards reliable morphological subtyping and cancer detection on Barrett's esophagus pathology through ensembling foundation models.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:23Z",
  "url": "https://research.draconitic.com/paper/6znxevexn7pt2",
  "venue": "NPJ digital medicine",
  "versions": []
}