ArticleTechnology in cancer research & treatment
CT Radiomic Nomogram Using Optimal Volume of Interest for Preoperatively Predicting Invasive Mucinous Adenocarcinomas in Patients with Incidental Pulmonary Nodules: A Multicenter, Large-Scale Study.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Amino acid transporters in hepatocellular carcinoma: Gatekeepers of metabolic reprogramming.Molecular biology reports · 2026Review
- Integrated tumor habitat and metabolic hot spot displacement analysis on [¹⁸F]FDG PET/CT for predicting treatment response and progression in unresectable locally advanced NSCLC: a two-center competing risk study.European journal of nuclear medicine and molecular imaging · 2026Article
- CT-based Intratumoral Heterogeneity Score for Preoperative Prediction of Lymphovascular Invasion and Prognosis in Lung Adenocarcinoma: A Multicenter Validation and Radiogenomic Interpretation.Annals of surgical oncology · 2026Article
- Clinical characteristics, prognosis, and nomograms for lung invasive mucinous adenocarcinoma with distant metastasis: a population-based study.Translational cancer research · 2026Article
- The value of an integrated multi-omics model in the diagnosis of benign and malignant pulmonary nodules.Translational cancer research · 2026Article
- 3D intratumoral heterogeneity-based quantitative score from chest CT for preoperative prediction of visceral pleural invasion in lung adenocarcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Bilateral pulmonary cavitary mucinous adenocarcinoma in an immunocompetent patient: a case report and literature review.BMC pulmonary medicine · 2025Review
- 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
- Article
- 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
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Abstract
introductionThis study evaluated the efficacy of radiomic analysis with optimal volumes of interest (VOIs) on computed tomography images to preoperatively differentiate invasive mucinous adenocarcinoma (IMA) from non-mucinous adenocarcinoma (non-IMA) in patients with incidental pulmonary nodules (IPNs).
methodsThis multicenter, large-scale retrospective study included 1383 patients with IPNs, 110 (8%) of whom were pathologically diagnosed with IMA postoperatively. Radiomic features were extracted from multi-scale VOI subgroups (VOI
resultsBubble lucency and lower lobe predominance were significant in establishing an image-finding classifier to differentiate between IMA and non-IMA in IPNs, achieving an area under the curve (AUC) value of 0.684 (0.568-0.801). Across all radiomic models, IMA had a higher Radscore than did non-IMA. Specifically, the VOI + 2 mm-based radiomic model exhibited the highest performance, with an AUC of 0.832 (0.753-0.911). The combined nomogram outperformed the recognized image-finding classifier and radiomic models, achieving an AUC of 0.850 (0.776-0.925).
conclusionA nomogram that combines a recognized image-finding classifier with an optimal VOI-based radiomic model effectively predicts IMA in IPNs, aiding physicians in developing comprehensive treatment strategies.
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