{
  "abstract": "Of all melanoma patients who develop distant metastasis over time, 41% were initially diagnosed with stage I/II. More accurate prognostic models are needed to select patients for potential (neo)adjuvant therapy. We hypothesise that histopathological features in whole slide images (WSIs) from primary early-stage melanomas provide additional information beyond American Joint Committee on Cancer (AJCC) stage. We utilised data from the population-based Dutch Early-Stage Melanoma (D-ESMEL) study. This included a discovery set (n=328) of case-control sets matched on follow-up time and known prognostic features (i.e. Breslow thickness, ulceration, age, and sex) and a validation cohort based on >5,500 patients following a nested case-control (NCC) design with stage-matched controls (n=266). We developed an end-to-end deep learning workflow to automatically extract relevant patches from WSIs and predict patient-level metastatic risk based on histopathological features independent of AJCC stage. The discovery set served as a training set for the workflow using 10-fold cross-validation. The prognostic performance of this model and its independence from clinical risk factors was evaluated in the validation cohort using odds ratios (OR) and the weighted C-index. We achieved a balanced accuracy of 0.88 (mean: 0.81; 95% CI: 0.77-0.85) in the best validation fold of the discovery set. In the validation cohort, metastatic risk predictions from the deep learning model were an independent prognostic factor (adjusted OR: 1.97; p<0.01). Combining the deep learning predictions (C-index: 0.86) with AJCC stage (C-index: 0.80) yielded the highest C-index of 0.87, significantly improving the C-index compared to AJCC stage alone (p<0.001). Our histopathology-based deep learning model was rigorously evaluated in a population-based cohort and demonstrated added prognostic value beyond AJCC stage. Further investigations of the underlying biological features could improve clinical decision making and patient outcomes.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pathology and Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "J. Ouwerkerk"
    },
    {
      "name": "F. Akram"
    },
    {
      "name": "L. Hollestein"
    },
    {
      "name": "T. Kerkour"
    },
    {
      "name": "C. Zhou"
    },
    {
      "name": "A. Stubbs"
    },
    {
      "name": "A. Mooyaart"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7vvrbqpq16c03.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.29.26366599",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.29.26366599v1.full",
  "id": "7vvrbqpq16c03",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.29.26366599v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.29.26366599v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7vvrbqpq16c03.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:43Z",
      "native_id": "10.64898/2026.10.29.26366599",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.29.26366599v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Deep learning reveals prognostic information beyond AJCC stage in routine histopathology of early-stage melanoma: the population-based D-ESMEL study",
  "title_text": "Deep learning reveals prognostic information beyond AJCC stage in routine histopathology of early-stage melanoma: the population-based D-ESMEL study",
  "type": "preprint",
  "updated_at": "2026-11-01T00:20:33Z",
  "url": "https://research.draconitic.com/paper/7vvrbqpq16c03",
  "venue": "medRxiv",
  "versions": []
}