{
  "abstract": "IntroductionIndependent proton dose verification requires a calculation pathway that is accurate enough to identify clinically relevant discrepancies while remaining practical to commission and operate. We use a pre-release version of the commercial RadMonteCarlo, a multithreaded CPU-based proton Monte Carlo engine built on a dose planning method (DPM) framework for independent treatment-plan recalculation. In this study, we evaluate the sensitivity of RadMonteCarlo to the conversion of CT Hounsfield units (HU) to relative stopping power (RSP).MethodsRadMonteCarlo was commissioned from integrated depth-dose (IDD) curves, spot profiles, and absolute-output data for a pencil beam scanning proton system. Three HU-to-RSP assignments were evaluated: a density-derived parameterization, a Bethe–Bloch-based calculation, and a Schneider stoichiometric tissue model. Four anonymized clinical plans (prostate, bladder, lung, and brain) were recalculated. RadMonteCarlo dose was compared with the RayStation Monte Carlo dose, using three-dimensional gamma criteria of 3%/3 mm and 2%/ 2 mm. RadMonteCarlo distributions were also compared directly to isolate RSP sensitivity from TPS agreement.ResultsThe commissioned fits achieved a mean R² of 0.9998, and re-simulated R80 values agreed with the measurements within 1 mm (mean difference +0.39 mm). All 12 clinical plan–RSP comparisons met the specified 95% criterion at 3%/3 mm. In direct RSP model comparisons, target mean-dose differences ranged from−1.15% to +1.20%, distal R80 shifts along the beam axes ranged from−0.3 to +1.9 mm, and the 95th-percentile absolute voxelwise dose difference ranged from 1.8% to 4.1%.DiscussionThese controlled comparisons demonstrate that HU-to-RSP selection produces localized, anatomy- and beam-path- dependent changes in the individual cases examined. RadMonteCarlo therefore provides a stable independent recalculation framework in which calibration-driven effects can be identified and interpreted separately from transport or source-model differences.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Original Research",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Aimee L. McNamara"
    },
    {
      "name": "Daniel O’Brien"
    },
    {
      "name": "Alan Nelson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fsh4csc0bs5j5.bib",
  "cited_by": 0,
  "doi": "10.3389/fonc.2026.1963150",
  "fulltext_url": null,
  "id": "fsh4csc0bs5j5",
  "issns": [
    "2234-943X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1963150",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "feed-journal",
      "medicine/oncology"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/fsh4csc0bs5j5.ris",
  "sources": [
    {
      "first_seen": "2026-11-06T12:20:48Z",
      "native_id": "10.3389/fonc.2026.1963150",
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      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1963150"
    },
    {
      "first_seen": "2026-11-06T12:21:22Z",
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      "url": "https://www.frontiersin.org/articles/10.3389/fonc.2026.1963150/full"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "feed-journal:medicine/oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Proton treatment-plan dose verification with a DPM-based Monte Carlo engine",
  "title_text": "Proton treatment-plan dose verification with a DPM-based Monte Carlo engine",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T00:23:06Z",
  "url": "https://research.draconitic.com/paper/fsh4csc0bs5j5",
  "venue": "Frontiers in Oncology",
  "versions": []
}