{
  "abstract": "Objective: Accurately assessing residual disease after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) remains challenging. Residual extramural venous invasion (EMVI) and perirectal lymph node (PLN) metastasis indicate adverse outcomes, but treatment-related changes obscure their detection on post-treatment MRI. This study developed MRI-based radiomics models to predict residual EMVI and PLN metastasis using post-nCRT restaging MRI. Materials and methods: In this retrospective study, 219 patients with LARC who completed nCRT and underwent post-treatment MRI for restaging prior to surgery were included. Radiomic features were extracted from manually segmented regions of interest encompassing the primary tumor and mesorectal fat on high-resolution T2-weighted images using PyRadiomics. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models were developed to predict pathological EMVI and PLN status. Model performance was assessed using repeated 5-fold cross-validation, with the area under the receiver operating characteristic curve (AUC) as the primary evaluation metric. Differences in model performance were compared using DeLong test. Results: For EMVI prediction, the combined tumor and mesorectal fat radiomics model achieved the highest AUC of 0.797 ± 0.073 using the LR model. For PLN prediction, the combined model also demonstrated superior performance, achieving an AUC of 0.824 ± 0.073. Models incorporating both tumor and mesorectal fat features consistently outperformed single-region models. Conclusion: MRI-based radiomics models using post-nCRT restaging images could predict residual EMVI and PLN metastasis in LARC. Incorporating mesorectal fat features improved model performance, suggesting that information from the surrounding mesorectal compartment may be useful for post-treatment risk assessment.",
  "abstract_shown": true,
  "affiliations": [
    "Medical Physics Program, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand",
    "Chulalongkorn Biomedical Imaging Group, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand",
    "Department of Radiological Technology and Medical Physics, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Thailand",
    "Division of Diagnostic Radiology, Department of Radiology, King Chulalongkorn Memorial Hospital, Bangkok, Thailand",
    "Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand",
    "Colorectal Division, Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand",
    "Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand",
    "Center for Artificial Intelligence in Medicine, Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand",
    "Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand",
    "Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yaniga Swaengdee"
    },
    {
      "affs": [
        1
      ],
      "name": "Sararas Khongwirotphan"
    },
    {
      "affs": [
        2
      ],
      "name": "Jaravee Lasode"
    },
    {
      "affs": [
        3
      ],
      "name": "Phakakarn Kuecharoen"
    },
    {
      "affs": [
        4
      ],
      "name": "Phathayphout Phetvilay"
    },
    {
      "affs": [
        5
      ],
      "name": "Thitithep Limvorapitak"
    },
    {
      "affs": [
        6
      ],
      "name": "Anapat Sanpavat"
    },
    {
      "affs": [
        7
      ],
      "name": "Sira Sriswasdi"
    },
    {
      "affs": [
        8
      ],
      "name": "Piyaporn Boonsirikamchai"
    },
    {
      "affs": [
        9
      ],
      "name": "Yothin Rakvongthai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/29xcmxq9azt0r.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0357268",
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  "id": "29xcmxq9azt0r",
  "issns": [
    "1932-6203"
  ],
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  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ],
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      "plos",
      "/Biology and life sciences/Biochemistry/Lipids/Fats"
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      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Colorectal cancer"
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      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Malignant tumors"
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      "/Medicine and health sciences/Oncology/Metastasis"
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      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Magnetic resonance imaging"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357268&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/29xcmxq9azt0r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pone.0357268",
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      "url": "https://doi.org/10.1371/journal.pone.0357268"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:diagnostic radiology",
      "subfield": "imaging"
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    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
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  ],
  "title": "MR-based radiomics of mesorectal fat for improved prediction of perirectal lymph node metastasis and extramural venous invasion in locally advanced rectal cancer",
  "title_text": "MR-based radiomics of mesorectal fat for improved prediction of perirectal lymph node metastasis and extramural venous invasion in locally advanced rectal cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/29xcmxq9azt0r",
  "venue": "PLOS One",
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