{
  "abstract": "Background/Aim: This study aimed to develop a classification tree model to predict clinical complete response (cCR) after definitive hypofractionated RT in elderly patients with non-melanoma (basal and squamous cell carcinoma) skin cancer based on pre-treatment clinical parameters.\n\nPatients and Methods: Data from elderly patients (aged ≥ 70 years) with histologically proven non-melanoma skin cancer lesion(s) were reviewed. All lesions were treated with definitive hypofractionated RT. A decision tree methodology was applied to define significant pre-treatment parameters influencing the cCR rate. The data set included seven predictor variables (location, histology, T stage, diameter, bleeding, pain, previous treatment) and a dependent variable (cCR). The analysis was conducted using open-source R software. Important explanatory variables were selected using the random forest approach. A classification tree that optimally partitioned patients according to cCR was then built.\n\nResults: A total of 121 patients with 145 non-melanoma skin cancer lesions were identified between January 2015 and December 2023. The cCR rate after treatment was 78.6%. The three most important variables identified were T stage, location and pain at diagnosis. Based on these explanatory variables, patients were partitioned in five groups. Of these groups, three experienced cCR rates ranging from 69% to 88% and two experienced cCR rates lower than 15%.\n\nConclusion: The proposed classification tree could serve as a valuable tool to guide future research in the field of non-melanoma skin cancer. The decision tree method may be an effective approach to partition elderly patients with non-melanoma skin cancer based on key clinical parameters.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiotherapy, Policlinico Umberto I, Department of Radiological, Oncological and Pathological Sciences, “Sapienza” University of Rome, Rome, Italy",
    "Radiotherapy Department S.Croce & Carle Teaching Hospital, Cuneo, Italy",
    "Department of Radiation Oncology, Ospedale dell’Angelo, Venice, Italy",
    "Department of Radiotherapy Santa Maria Addolorata Hospital, Rome, Italy"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "FRANCESCA DE FELICE"
    },
    {
      "affs": [
        1
      ],
      "name": "LAVINIA SPINELLI"
    },
    {
      "affs": [
        1
      ],
      "name": "PAOLA CRITELLI"
    },
    {
      "affs": [
        2
      ],
      "name": "MELISSA SCRICCIOLO"
    },
    {
      "affs": [
        3
      ],
      "name": "DANIELA MUSIO"
    },
    {
      "affs": [
        0
      ],
      "name": "ANNALISA MAGNANTE"
    },
    {
      "affs": [
        1
      ],
      "name": "STEFANIA MARTINI"
    },
    {
      "affs": [
        0
      ],
      "name": "GIUSEPPE MINNITI"
    },
    {
      "affs": [
        2
      ],
      "name": "ANNA MERLOTTI"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bjgmf1a1964ar.bib",
  "cited_by": 0,
  "doi": "10.21873/invivo.14507",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13723980/",
  "id": "bjgmf1a1964ar",
  "issns": [
    "0258-851X",
    "1791-7549"
  ],
  "landing_url": "https://doi.org/10.21873/invivo.14507",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research Article"
    ],
    [
      "openalex",
      "2713|Nonmelanoma Skin Cancer Studies"
    ]
  ],
  "openalex": "W7224175478",
  "pdf_url": null,
  "pmcid": "PMC13723980",
  "pmid": "42957261",
  "published": "2026-10-29",
  "publisher": "International Institute of Anticancer Research",
  "ris": "https://research.draconitic.com/paper/bjgmf1a1964ar.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:22:31Z",
      "native_id": "PMC13723980",
      "source": "pmc",
      "url": "https://doi.org/10.21873/invivo.14507"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Nonmelanoma Skin Cancer Studies",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Definitive Hypofractionated Radiotherapy in Elderly Patients With Non-melanoma Skin Cancer: Predicting Complete Response Using a Machine Learning-based Approach",
  "title_text": "Definitive Hypofractionated Radiotherapy in Elderly Patients With Non-melanoma Skin Cancer: Predicting Complete Response Using a Machine Learning-based Approach",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T12:20:42Z",
  "url": "https://research.draconitic.com/paper/bjgmf1a1964ar",
  "venue": "In Vivo",
  "versions": []
}