{
  "abstract": "Cross-cohort evaluation of breast cancer subtype classifiers is unreliable when source and target cohorts use different annotation standards: METABRIC labels six classes, PAM50 defines five, and a claudin-low prediction cannot be correct under a five-class standard. A classifier transferred from 1,369 METABRIC patients (train n=958) to TCGA-BRCA (n=987) and GSE21653 (n=266) separates three evaluation operations routinely conflated in cross-site comparisons. (1) Label-space restriction - an evaluation-rule change, not a retraining gain - is the dominant correction (73.76%->79.74% on TCGA-BRCA; 63.16%->68.42% on GSE21653); (2) EM prior correction on the reconciled space is an optional calibration step (Brier fidelity up, macro F1 down; to 80.65% and 74.06%); and (3) the target label space is not recoverable from unlabelled predictions (EM retains prior 0.0751 on the absent class; calibrated detectors fail), so compliance must come from cohort documentation. The protocol exposes why internal validation overstates cross-cohort value: multimodal fusion gains do not transfer (no configuration beats mRNA alone, n=694; McNemar p=0.13-1.00), and the genomic panel adds nothing to pathological complete response prediction over clinical variables (DeltaAUC = +0.0045). Clinically, without label-space compliance, cross-site accuracy comparisons misattribute annotation-standard differences to model skill; Luminal A/B and claudin-low boundary errors map to distinct treatment decisions; and subtype accuracy is a weaker portable endpoint than survival discrimination (external C-index 0.577-0.584). Reporting the cohort's allowed label set and applying label-space compliance are therefore prerequisites before any cross-cohort performance claim.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.24962",
  "author_total": 4,
  "authors": [
    {
      "name": "Suxing Liu"
    },
    {
      "name": "Byungwon Min"
    },
    {
      "name": "Zhijian Zheng"
    },
    {
      "name": "Jiahao Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8ydnqcnacw9xn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.24962",
  "fulltext_url": null,
  "id": "8ydnqcnacw9xn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.24962",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ],
    [
      "openalex",
      "1306|Breast Cancer Treatment Studies"
    ]
  ],
  "openalex": "W7223021368",
  "pdf_url": "https://arxiv.org/pdf/2610.24962",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-18",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8ydnqcnacw9xn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:31:01Z",
      "native_id": "2610.24962",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.24962"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Label-Space Mismatch Distorts Cross-Cohort Evaluation in Breast Cancer Molecular Subtyping",
  "title_text": "Label-Space Mismatch Distorts Cross-Cohort Evaluation in Breast Cancer Molecular Subtyping",
  "type": "preprint",
  "updated_at": "2026-10-30T12:33:50Z",
  "url": "https://research.draconitic.com/paper/8ydnqcnacw9xn",
  "venue": "arXiv",
  "versions": []
}