{
  "abstract": "Background Non-small cell lung cancer (NSCLC) staging relies on accurate assessment of mediastinal lymph nodes. This study investigates the utility of radiomic features derived from contrast-enhanced thoracic CT scans in predicting malignancy in clinically positive (cN+) mediastinal lymph nodes. Methods A retrospective cohort of 110 NSCLC patients with cN + who underwent surgical resection was analyzed. 3D segmentations of up to three lymph nodes per patient were performed. Radiomic features, encompassing heterogeneity measures, were extracted. A radiomics model was constructed using a random forest and XGboost algorithm. To ensure robustness and minimize bias, 100 iterations of data splitting were conducted to create distinct training and test sets for reproducibility and statistical reliability. Results The radiomics model achieved an area under the curve (AUC) of 0.703 for Forest model and 063 for XGboost model. Three recurrent features in the radiomic signatures, “RootMeanSquared”, “Grey Level Co-Occurrence Matrix Imc2”, and “Grey Level Run Length Matrix RunEntropy”, highlighted the importance of nodal heterogeneity features in the model. Conclusion Radiomic features extracted from contrast-enhanced thoracic CT scans could predict malignancy in cN+ mediastinal lymph nodes of NSCLC patients (AUC = 0.703). Three features of heterogeneity were recurrent in radiomic signature, emphasizing the potential importance of incorporating nodal heterogeneity criteria in addition to size assessment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "Judith Legrand"
    },
    {
      "name": "Antoine Decoux"
    },
    {
      "name": "Loïc Duron"
    },
    {
      "name": "Cécile Di-Rocco"
    },
    {
      "name": "Manuel Gargiulo"
    },
    {
      "name": "Pascal Thomas"
    },
    {
      "name": "Armelle Arnoux"
    },
    {
      "name": "Kathia Chaumoitre"
    },
    {
      "name": "Jean-Yves Gaubert"
    },
    {
      "name": "Laure Fournier"
    },
    {
      "name": "Paul Habert"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c9549myx50qd8.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0355650",
  "fulltext_url": null,
  "id": "c9549myx50qd8",
  "issns": [
    "1932-6203",
    "1932-6203"
  ],
  "landing_url": "https://amu.hal.science/hal-05737884v1",
  "language": "en",
  "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": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.sdv.ib"
    ],
    [
      "hal",
      "1.sdv.mhep"
    ],
    [
      "hal",
      "2.sdv.ib.ima"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ],
    [
      "plos",
      "/Biology and life sciences/Anatomy/Lymphatic system/Lymph nodes"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neuroimaging/Computed axial tomography"
    ],
    [
      "plos",
      "/Medicine and health sciences/Anatomy/Lymphatic system/Lymph nodes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Tomography/Computed axial tomography"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Basic cancer research/Metastasis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors/Non-small cell lung cancer"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Malignant tumors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Metastasis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Tomography/Computed axial tomography"
    ],
    [
      "plos",
      "/Medicine and health sciences/Surgical and invasive medical procedures"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Tomography/Computed axial tomography"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Neuroimaging/Computed axial tomography"
    ]
  ],
  "openalex": "W7204821724",
  "pdf_url": "https://amu.hal.science/hal-05737884/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/c9549myx50qd8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:27Z",
      "native_id": "hal-05737884",
      "source": "hal",
      "url": "https://amu.hal.science/hal-05737884v1"
    },
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pone.0355650",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0355650"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.ib",
      "subfield": "systems-synthetic"
    },
    {
      "field": "medicine",
      "origin": "plos:diagnostic radiology",
      "subfield": "imaging"
    },
    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "CT radiomics-histopathological correlation for mediastinal lymph node staging in non-small cell lung cancer",
  "title_text": "CT radiomics-histopathological correlation for mediastinal lymph node staging in non-small cell lung cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/c9549myx50qd8",
  "venue": "PLoS ONE",
  "versions": []
}