{
  "abstract": "PurposeDigital-twin frameworks can potentially offer a pathway toward individualized treatment but remain largely untested in radiation oncology. We present a causal digital-twin model for head-and-neck (H&N) cancer that estimates patient-specific survival benefit from chemoradiotherapy (CRT) versus radiotherapy (RT) alone, stratified by HPV status.\n\nMethodsUsing the public RADCURE cohort (n = 3,346), we developed a causal digital-twin framework in which patients were divided into HPV-positive and HPV-negative groups and assigned to training (RT start year [≤] 2007) and test sets (RT start year > 2007). Bayesian-network structure learning identified each cohorts Markov Blanket around the 2-year survival endpoint (SVy2), yielding the minimal causally relevant feature set. A causal-forest model (double machine learning, DML framework) estimated the individualized treatment effect{tau} = P(Svy2 | CRT,X) - P(Svy2 | RT,X), with [Formula] denoting the model-estimated effect. Overlap restrictions were applied to mitigate extrapolation. Predictive discrimination (AUC, C-index), average treatment effects (ATE), individualized effects (ITE), and decision-curve analyses were computed.\n\nResultsFor HPV-negative disease, internal evaluation demonstrated strong discriminative performance (test AUC = 0.72; C-index = 0.81, p < 0.01). The estimated test-set ATE favored CRT with a modest but positive improvement in SVy2 (mean [Formula]). Patterns in [Formula] were clinically coherent, including increased benefit in node-positive and good-performance patients and reduced benefit in high-burden or poor-performance presentations. Decision-curve analysis showed that a [Formula]-guided selective-CRT strategy provided higher net benefit than uniform treatment policies. HPV-positive disease exhibited substantially greater but less clinically structured treatment-effect heterogeneity, accompanied by reduced overlap and greater uncertainty.\n\nConclusionThis study demonstrates a proof-of-concept causal digital-twin framework for interpretable, patient-specific survival simulation in H&N cancer. The model captures clinically plausible treatment-effect heterogeneity and establishes a foundation for multi-cohort validation and future integration into individualized treatment decision support.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "K. D. Shah"
    },
    {
      "name": "C.-W. Chang"
    },
    {
      "name": "J. Zhou"
    },
    {
      "name": "J. Ding"
    },
    {
      "name": "A. Dhabaan"
    },
    {
      "name": "H. A. Al-Hallaq"
    },
    {
      "name": "J. Bates"
    },
    {
      "name": "W. Stokes"
    },
    {
      "name": "D. S. Yu"
    },
    {
      "name": "X. Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0rrrhjbb5f67k.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.30.26363910",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26363910v1.full",
  "id": "0rrrhjbb5f67k",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26363910v1",
  "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",
      "oncology"
    ],
    [
      "openalex",
      "2733|Head and Neck Cancer Studies"
    ]
  ],
  "openalex": "W7216400614",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26363910v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0rrrhjbb5f67k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:59Z",
      "native_id": "10.64898/2026.09.30.26363910",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.30.26363910v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "HPV-Stratified Causal Digital Twins for Personalized Survival Benefit Estimation in Head and Neck Cancer",
  "title_text": "HPV-Stratified Causal Digital Twins for Personalized Survival Benefit Estimation in Head and Neck Cancer",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:59Z",
  "url": "https://research.draconitic.com/paper/0rrrhjbb5f67k",
  "venue": "medRxiv",
  "versions": []
}