{
  "abstract": "BackgroundReported performance for multi-modal cancer prognostic models is frequently obtained from a single train-test partition and without comparison against a simple clinical baseline. We evaluated an integrated framework combining language-model representations of structured clinical records with high-dimensional multi-omics data under a leakage-controlled protocol.\n\nMethodsClinical variables from 589 TCGA-BRCA patients were converted to natural language sentences and encoded with BioBERT; DNA methylation, RNA, miRNA and protein data were reduced by Sparse principal component analysis. Clinical-only, multi-omics-only and integrated feature sets were evaluated with Cox proportional hazards, XGBoost and random survival forest (RSF) using five repetitions of stratified five-fold cross-validation with pooled out-of-fold predictions. Models were compared against a marginal Kaplan-Meier null and a conventional age-and-stage Cox model, with differences assessed by paired bootstrap.\n\nResultsAmong 589 patients with 88 deaths (14.9%; median follow-up 33.7 months), the integrated RSF model performed best (Harrells C 0.685, 95% CI 0.620-0.754; Unos C 0.748), exceeding the conventional clinical Cox model (0.637, 0.559-0.707). The ordering integrated > clinical-only > multi-omics-only held in all nine feature-set by algorithm comparisons and at all three component settings tested. The integrated model significantly outperformed every multi-omics-only model (P = 0.008-0.044) but not clinical-only models ({Delta}C = 0.037, P = 0.128). Brier scores approached a null reference at 2 and 3 years and improved modestly at 5 years.\n\nConclusionIntegration produced the highest discrimination and exceeded a conventional clinical model, but the gain over clinical features within the same pipeline was not statistically resolvable at this event count. Reporting simple baselines, null-model calibration and cross-validated estimates should be standard in multi-modal prognostic modelling.",
  "abstract_shown": true,
  "affiliations": [
    "University of Central Florida"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. M. H. Bhuiyan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/21fpqfrh29rce.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.31.26361875",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361875v1.full",
  "id": "21fpqfrh29rce",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361875v1",
  "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": [
    [
      "medrxiv",
      "oncology"
    ],
    [
      "openalex",
      "1702|Machine Learning in Healthcare"
    ]
  ],
  "openalex": "W7211958348",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361875v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/21fpqfrh29rce.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:57Z",
      "native_id": "10.64898/2026.08.31.26361875",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.31.26361875v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "BioBERT-Derived Clinical TextRepresentations and Sparse PrincipalComponents of Multi-Omics Data for BreastCancer Survival Prediction: ALeakage-Controlled Cross-ValidatedBenchmark",
  "title_text": "BioBERT-Derived Clinical TextRepresentations and Sparse PrincipalComponents of Multi-Omics Data for BreastCancer Survival Prediction: ALeakage-Controlled Cross-ValidatedBenchmark",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:57Z",
  "url": "https://research.draconitic.com/paper/21fpqfrh29rce",
  "venue": "medRxiv",
  "versions": []
}