{
  "abstract": "Purpose\n\nClassical nomograms for high-dose-rate (HDR) prostate brachytherapy quality assurance (QA) evaluate agreement between planned and expected total dwell time but do not directly assess the three-dimensional treated volume. This study's aim was to develop a deep learning-based 3D nomogram to predict institution-specific clinical 100% isodose volumes from anatomy and dwell positions for post-plan QA.Methods\n\nA total of 2394 single-fraction 15 Gy HDR prostate brachytherapy plans were included. Input label maps encoded the 3D prostate, urethra, rectum, and dwell positions. The prediction target was the clinical 100% isodose volume. A five-fold cross-validation strategy was used for model development, with 2155 plans used for training and validation and 239 plans held out for testing. A 3D U-Net was trained using combined Dice and binary cross-entropy loss. Agreement was evaluated using prostate V100%, 100% isodose volume, Dice similarity coefficient, and average symmetric surface distance (ASSD). Validation-derived thresholds were used for outlier screening and targeted review.Results\n\nThe model predicted prostate V100% with a mean error of 0.73 percentage points. Predicted and clinical 100% isodose volumes showed strong agreement, with R² = 0.990, slope = 0.99, and intercept = 0.77 cm³. Median Dice similarity coefficient was 0.960, and median ASSD was 0.48 mm. A 95% threshold flagged 6.3% of plans as atypical relative to validation data.Conclusions\n\nA deep learning-based nomogram predicted an institution-specific 100% isodose volume with low coverage error and high spatial agreement. This geometry aware post-plan QA tool can complement equation-based nomograms by directly evaluating the spatial treated volume.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.",
    "Medical Physics Unit, Department of Oncology, McGill University, Montreal, Quebec, Canada.",
    "Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Joel Beaudry"
    },
    {
      "affs": [
        0
      ],
      "name": "Tonghe Wang"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Shirin A Enger"
    },
    {
      "affs": [
        0
      ],
      "name": "Antonio L Damato"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dxpkx33hpvg0s.bib",
  "cited_by": 0,
  "doi": "10.1016/j.phro.2026.101100",
  "fulltext_url": "https://europepmc.org/articles/PMC13679370",
  "id": "dxpkx33hpvg0s",
  "issns": [
    "2405-6316"
  ],
  "landing_url": "https://europepmc.org/article/MED/42898876",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3108|Advanced Radiotherapy Techniques"
    ]
  ],
  "openalex": "W7215848243",
  "pdf_url": "https://europepmc.org/articles/PMC13679370?pdf=render",
  "pmcid": "PMC13679370",
  "pmid": "42898876",
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dxpkx33hpvg0s.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:41Z",
      "native_id": "MED:42898876",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42898876"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "openalex:3108|Advanced Radiotherapy Techniques",
      "subfield": "applied-instrumentation"
    }
  ],
  "title": "Three-dimensional deep learning nomogram for post-plan quality assurance in high-dose-rate prostate brachytherapy.",
  "title_text": "Three-dimensional deep learning nomogram for post-plan quality assurance in high-dose-rate prostate brachytherapy.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:41Z",
  "url": "https://research.draconitic.com/paper/dxpkx33hpvg0s",
  "venue": "Physics and imaging in radiation oncology",
  "versions": []
}