ArticleEuropean radiology2025
Deciphering the intratumoral histologic heterogeneity of lung adenocarcinoma using radiomics.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- From the Thoracoscope to the Robot: The Evolving Landscape of Minimally Invasive Pulmonary Surgery.Cancers · 2026Review
- Comprehensive analysis ofJournal of thoracic disease · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Radiomics to understand pre-treatment tumor biology for resectable non-small cell lung cancer.Frontiers in oncology · 2026Review
- Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- [CT radiomicsZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study.Frontiers in oncology · 2025Article
- Predicting Ki-67 expression levels in non-small cell lung cancer using an explainable CT-based deep learning radiomics model.Frontiers in oncology · 2025Article
- Stacking Model-Based Preoperative Prediction of Lymphovascular Invasion in Lung Adenocarcinoma by Integrating Intratumoral Heterogeneity Score and Clinicoradiological Features: A Multicenter Study.Technology in cancer research & treatmentArticle
- Robustness and Accuracy of Radiomics Models for Classifying IASLC Grading in Lung Adenocarcinomas: A Comprehensive Analysis of a Large Multicenter CT Database.Technology in cancer research & treatmentArticle
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11 authors.
Funding
Abstract
objectiveTo discern highly aggressive intratumoral areas among lung adenocarcinoma (LUAD) and its impact on occult nodal metastases and the recurrence rate with radiomic analysis.
methodsThis prospective dual-institution study analyzed clinical information and high-resolution preoperative CT of 528 patients from institution A and 249 patients from institution B. We extracted radiomic features and performed pathologic evaluations for resected tumors, based on the 2020 International Association for the Study of Lung Cancer (IASLC) classification. Prediction models were developed to discern micropapillary and solid patterns within LUAD using clinical and radiomic features from institution A through logistic analysis.
resultsSix selected CT radiomic features, sex, CTR (consolidation-to-tumor ratio), and solid diameter were selected to develop the prediction models. A composite model of radiomic and clinical characteristics outperformed radiomics-only and clinical-only models (AUC, 95% CI; the composite model: 0.84 [0.81-0.87]; the radiomics model: 0.82 [0.78-0.87]; the clinical model: 0.80 [0.76-0.83]) in institution A. External validation was performed with institution B cohort, showing even better results (AUC, 95% CI; the composite model: 0.91 [0.87-0.94]; the radiomics model: 0.89 [0.84-0.94]; the clinical model: 0.88 [0.84-0.92]).
conclusionsOur study underscores the potential of radiomics to preoperatively predict aggressive histologic patterns in LUAD, enabling precise treatment planning and prognosis estimation. KEY POINTS: Question Can any adjuvant methods address the limitations of core needle biopsies, which are invasive and may not capture the full heterogeneity of lung adenocarcinoma? Findings In a prospective study of 528 patients with cT1N0M0 lung adenocarcinoma, a composite model of clinical characteristics, conventional CT findings, and radiomics features predicted high-grade cancers. Clinical relevance Preoperative non-invasive diagnosis of histologically high-grade tumors using radiomics analysis offers crucial information for the treatment of lung adenocarcinoma with respect to occult lymph node metastasis and recurrence rate.
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