{
  "abstract": "Severe radiation-induced lymphopenia (RIL), specifically Grade 4 RIL (G4RIL), is a frequent immune toxicity and a strong predictor of poor survival for esophageal cancer patients undergoing concurrent chemoradiation therapy. This study developed an uncertainty-incorporated ensemble machine learning model to predict Absolute Lymphocyte Count (ALC) nadir using baseline clinical and dosimetric variables, supporting treatment decisions such as proton therapy selection and dose distribution optimization. A cohort of 1,491 esophageal cancer patients treated with photon or proton therapy between July 2001 and April 2022 was analyzed. A weighted ensemble model was constructed using clinical features and dosimetric variables, including the composite dosimetric score (CDS) derived from radiation dose distributions. To quantify individual-level predictive uncertainty, Cross-Residual Conformal Prediction (CRCP) was developed and evaluated against standard conformal prediction methods. Proton therapy patients maintained a significantly higher mean ALC nadir than photon patients (0.33 vs. 0.25 K/uL). The ensemble model achieved a mean absolute error of 0.0926 K/uL for ALC nadir prediction and an ROC-AUC of 0.78 for G4RIL classification. Baseline ALC, Planning Target Volume, and CDS were the most influential predictors. CRCP achieved competitive prediction interval widths with mathematically guaranteed coverage, offering a favorable balance between interval width and coverage accuracy. This framework enables risk-aware, personalized clinical decision-making in radiation oncology.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.25139",
  "author_total": 13,
  "authors": [
    {
      "name": "Baode Gao"
    },
    {
      "name": "Yan Chu"
    },
    {
      "name": "Zongsheng Hu"
    },
    {
      "name": "Madison E. Grayson"
    },
    {
      "name": "Peter S. N. van Rossum"
    },
    {
      "name": "Clemens Grassberger"
    },
    {
      "name": "Heiko Enderling"
    },
    {
      "name": "Harald Paganetti"
    },
    {
      "name": "Ibrahim Chamseddine"
    },
    {
      "name": "Steven H. Lin"
    },
    {
      "name": "Brian P. Hobbs"
    },
    {
      "name": "Radhe Mohan"
    },
    {
      "name": "Yiqing Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6tr83rx0crttb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.25139",
  "fulltext_url": null,
  "id": "6tr83rx0crttb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.25139",
  "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",
      "q-bio.QM"
    ],
    [
      "openalex",
      "2746|Esophageal Cancer Research and Treatment"
    ]
  ],
  "openalex": "W7214122306",
  "pdf_url": "https://arxiv.org/pdf/2609.25139",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6tr83rx0crttb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:44Z",
      "native_id": "2609.25139",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.25139"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Uncertainty Quantification-Incorporated Ensemble Model for Personalized Prediction of Severe Radiotherapy-Induced Immunosuppression Among Esophageal Cancer Patients",
  "title_text": "Uncertainty Quantification-Incorporated Ensemble Model for Personalized Prediction of Severe Radiotherapy-Induced Immunosuppression Among Esophageal Cancer Patients",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:44Z",
  "url": "https://research.draconitic.com/paper/6tr83rx0crttb",
  "venue": "arXiv",
  "versions": []
}