{
  "abstract": "Background. Estrogen receptor (ER), progesterone receptor (PR) and HER2 status guide systemic treatment in breast cancer. Multimodal models are rarely benchmarked against key biomarker molecular baselines or evaluated for cross-cohort calibration. We assessed incremental value, dependence on label-proximal HER2 transcripts, and transport of discrimination and calibration. Methods. A model combining whole-slide images, transcriptomes, age and stage was developed in TCGA-BRCA (1051 patients; ER/PR/HER2 analysis sets: 1001/998/1006) and evaluated in CPTAC-BRCA (120 patients; 118/113/95). CPTAC had been observed during earlier development and included 13 HER2-positive cases. Analyses included paired model comparisons, transcript removal, alternative loss functions and recalibration. Internal discrimination used pooled out-of-fold predictions; external discrimination used fold-mean estimates, with ensemble calibration reported separately. Internal calibration and macro-AUC uncertainty were interpreted as exploratory. Results. Internal AUCs were 0.943 for ER, 0.901 for PR, and 0.860 for HER2; external AUCs were 0.952, 0.937, and 0.918, respectively. External macro-AUC was 0.936 for the multimodal model and 0.957 for logistic regression using ESR1, PGR, and ERBB2 (difference -0.021). Removal of the extended 29-transcript 17q12-q21 region changed HER2 AUC by -0.035 [-0.058 to -0.013] (95% CI) internally and by -0.042 [-0.089 to -0.007] externally relative to the baseline multimodal model. For external 15-model ensemble predictions, recalibrated HER2 expected calibration error (ECE) was 0.236, exceeding the ECE reference ceiling of 0.125, while calibration-in-the-large was -1.657 [-2.373 to -1.023] (profile-likelihood 95% CI), indicating systematic overprediction. ER and PR also showed overprediction. In paired fold-mean comparisons, HER2 ECE and Brier score favoured the three-transcript model (ESR1, PGR, and ERBB2). Conclusions. A multimodal deep-learning model achieved high discrimination across cohorts but did not demonstrate clear incremental value over a parsimonious three-transcript baseline, while cross-cohort calibration remained a substantial challenge. These findings highlight the importance of evaluating incremental value, calibration, and clinical utility alongside discrimination when assessing AI-based prediction models for medical use.",
  "abstract_shown": true,
  "affiliations": [
    "Hanoi Medical University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "B. M. Tran"
    },
    {
      "affs": [
        0
      ],
      "name": "V. A. Nguyen"
    },
    {
      "name": "Q. A. Dang"
    },
    {
      "affs": [
        0
      ],
      "name": "N. M. H. Nguyen"
    },
    {
      "affs": [
        0
      ],
      "name": "Q. A. Nguyen"
    },
    {
      "name": "H. N. Tran"
    },
    {
      "name": "D. H. Tran"
    },
    {
      "affs": [
        0
      ],
      "name": "T. B. V. Nguyen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dq8c7a4vgf1xk.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.01.761587",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.761587v1.full",
  "id": "dq8c7a4vgf1xk",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.761587v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.761587v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dq8c7a4vgf1xk.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T00:21:42Z",
      "native_id": "10.64898/2026.11.01.761587",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.01.761587v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Development and cross-cohort evaluation of a multimodal deep-learning model for ER, PR and HER2 status classification in breast cancer",
  "title_text": "Development and cross-cohort evaluation of a multimodal deep-learning model for ER, PR and HER2 status classification in breast cancer",
  "type": "preprint",
  "updated_at": "2026-11-05T00:21:10Z",
  "url": "https://research.draconitic.com/paper/dq8c7a4vgf1xk",
  "venue": "bioRxiv",
  "versions": []
}