{
  "abstract": "Preoperative histologic characterisation of lung adenocarcinoma is typically available only after resection. We evaluated whether fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics could predict histologic patterns and pathologic risk features using a hierarchical modelling strategy. This retrospective cohort study included patients from three imaging facilities within a single academic health system. Radiomic features were extracted from preoperative PET/CT and CT segmentations; preprocessing included feature filtering, standardisation, redundancy reduction, and ComBat harmonisation. Penalised generalised linear model (GLMNET), random forest, and XGBoost models were trained with stratified cross-validation and evaluated in independent test sets. Outcomes included mucinous phenotype, non-mucinous histologic subtype, histologic grade, spread through air spaces (STAS), vascular invasion, and visceral pleural invasion (VPI). Eligible patients had primary lung adenocarcinoma treated by curative-intent resection, with preoperative PET/CT or thin-section CT and histopathologic review available; patients receiving neoadjuvant therapy, non-adenocarcinoma histology, or rare adenocarcinoma variants were excluded. The histologic reference standard was central review by a single thoracic pathologist per the 2021 WHO/IASLC (World Health Organization/International Association for the Study of Lung Cancer) classification, blinded to imaging and radiomic results. From 300 candidate radiomic variables (147 PET-derived, 153 CT-derived), models were developed using a 75%/25% stratified train-test split within the same single-institution cohort; the test set therefore represents internal, not external, validation. Among 254 patients, 255 tumours were analysed. Binary identification of poorly differentiated tumours (grade 3 vs. grade 1-2) achieved area under the receiver operating characteristic curve (ROC-AUC) 0.767 [95% confidence interval (CI): 0.624-0.897], area under the precision-recall curve (PR-AUC) 0.854 (95% CI: 0.723-0.952), and satisfactory calibration [Brier Score 0.170; Brier Skill Score (BSS) 0.23]. VPI yielded a moderate global signal (ROC-AUC 0.691, 95% CI: 0.518-0.841). Non-mucinous subtype classification showed preserved probabilistic ranking despite limited categorical accuracy (balanced accuracy 0.614). Models for mucinous phenotype, STAS, and vascular invasion showed limited discrimination; VPI in the small-tumour subgroup was constrained by insufficient positive events. PET/CT radiomics supported probabilistic preoperative risk stratification of lung adenocarcinoma, particularly for identifying poorly differentiated tumours. These results support radiomics as a complementary preoperative tool pending external validation.",
  "abstract_shown": true,
  "affiliations": [
    "Thoracic Surgery Division, Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brasil.",
    "Pathology Department, Instituto do Cancer do Estado de Sao Paulo, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brasil.",
    "Division of Nuclear Medicine, Department of Radiology and Oncology, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brasil.",
    "Thoracic Surgery Division, Instituto do Coracao, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brasil."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Fabio E N Minamoto",
      "orcid": "0000-0001-9012-854X"
    },
    {
      "affs": [
        0
      ],
      "name": "Leticia L Lauricella",
      "orcid": "0000-0002-8378-7704"
    },
    {
      "affs": [
        1
      ],
      "name": "Luiz G C A Lima",
      "orcid": "0000-0002-3718-358X"
    },
    {
      "affs": [
        2
      ],
      "name": "Paulo S Duarte",
      "orcid": "0000-0003-4517-4410"
    },
    {
      "affs": [
        3
      ],
      "name": "Paulo M P Fernandes",
      "orcid": "0000-0001-7243-5343"
    },
    {
      "affs": [
        0
      ],
      "name": "Ricardo Mingarini Terra",
      "orcid": "0000-0001-8577-8708"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/18zw0bapekqb1.bib",
  "cited_by": 0,
  "doi": "10.21037/tlcr-2026-0899",
  "fulltext_url": "https://europepmc.org/articles/PMC13743451",
  "id": "18zw0bapekqb1",
  "issns": [
    "2218-6751",
    "2226-4477"
  ],
  "landing_url": "https://europepmc.org/article/MED/42999626",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7224015167",
  "pdf_url": "https://europepmc.org/articles/PMC13743451?pdf=render",
  "pmcid": "PMC13743451",
  "pmid": "42999626",
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/18zw0bapekqb1.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T15:20:34Z",
      "native_id": "MED:42999626",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42999626"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "PET/CT radiomics for noninvasive histologic risk stratification in resected lung adenocarcinoma: a single-system development and internal validation study.",
  "title_text": "PET/CT radiomics for noninvasive histologic risk stratification in resected lung adenocarcinoma: a single-system development and internal validation study.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T15:20:34Z",
  "url": "https://research.draconitic.com/paper/18zw0bapekqb1",
  "venue": "Translational lung cancer research",
  "versions": []
}