{
  "abstract": "Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present an uncertainty-guided digital twin (UGDT) framework that forecasts treatment-day anatomy before treatment and evaluate whether it generates online adaptive proton therapy (APT) plans of clinical quality. Approach: A library of 302 longitudinal deformations from 88 previously treated HN patients was transported onto each new patient's treatment planning CT (TPCT) using two-step multi-atlas deformable image registration (DIR) built on a pretrained CT foundation model, generating about 284 predicted CTs (pdCTs) with contours per patient. Dispersion of propagated clinical target volume (CTV) contours defined a patient-specific robust margin. In ten patients, the quality assurance CT (QACT) triggering a replan represented treatment-day anatomy, and the physician-approved replan was the baseline. The pdCT most similar to the QACT (pdCT-H) and one from the lowest quartile (pdCT-L) were planned to within about 5% of baseline plan quality, forward-calculated on the QACT, and reoptimized to generate online APT plans. Main results: pdCT plans scored within -0.7% (pdCT-H) and -1.0% (pdCT-L) of baseline. Forward calculation on QACT reduced high-dose CTV D98% to 88.3% and 85.5%. After online reoptimization, D98% recovered to 98.3 +/- 0.3% and 98.2 +/- 0.3%, versus 98.5 +/- 0.4% at baseline. Spinal cord and brainstem doses remained below tolerance, and plan quality scores were within -1.1% (p = 0.19) and -1.7% (p = 0.01) of baseline. Significance: UGDT generated online APT plans comparable in quality to physician-approved offline replans using anatomy forecast before treatment, enabling a transition from reactive offline replanning toward anticipatory online adaptation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.39010",
  "author_total": 15,
  "authors": [
    {
      "name": "Yizhou Wu"
    },
    {
      "name": "Ryan J. Sanford"
    },
    {
      "name": "Huiqiao Xie"
    },
    {
      "name": "Jie Ding"
    },
    {
      "name": "Shupeng Chen"
    },
    {
      "name": "Tung-Ho Wu"
    },
    {
      "name": "Ping-Hsiu Wu"
    },
    {
      "name": "Justin Roper"
    },
    {
      "name": "Jun Zhou"
    },
    {
      "name": "Minglei Kang"
    },
    {
      "name": "Bill Stokes"
    },
    {
      "name": "Sibo Tian"
    },
    {
      "name": "David S. Yu"
    },
    {
      "name": "Xiaofeng Yang"
    },
    {
      "name": "Chih-Wei Chang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/096xtwjnkqfpv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.39010",
  "fulltext_url": null,
  "id": "096xtwjnkqfpv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.39010",
  "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",
      "physics.med-ph"
    ],
    [
      "openalex",
      "3108|Advanced Radiotherapy Techniques"
    ]
  ],
  "openalex": "W7215033074",
  "pdf_url": "https://arxiv.org/pdf/2609.39010",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/096xtwjnkqfpv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.39010",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.39010"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "An Uncertainty-Guided Digital Twin Framework for Online Adaptive Proton Therapy in Head and Neck Cancer: A Feasibility Study",
  "title_text": "An Uncertainty-Guided Digital Twin Framework for Online Adaptive Proton Therapy in Head and Neck Cancer: A Feasibility Study",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/096xtwjnkqfpv",
  "venue": "arXiv",
  "versions": []
}