ArticleEJNMMI research2022
Development of a PET/CT molecular radiomics-clinical model to predict thoracic lymph node metastasis of invasive lung adenocarcinoma ≤ 3 cm in diameter.
Article in EJNMMI research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.BMC medical imaging · 2026Pooled it
- Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.BMC pulmonary medicine · 2024Pooled it
- AptoDetect™-Lung Assay as a Blood-Based Predictor of Advanced-Stage Lung Cancer in Patients with Lung-RADS 3-4 Pulmonary Nodules: A Multicenter Prospective Cohort Study.Biomedicines · 2026Article
- Article
- Clinical study ofPeerJ · 2026Article
- Development of anQuantitative imaging in medicine and surgery · 2025Article
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- Accurate categorization and rapid pathological diagnosis correction with Micro-Raman technique in human lung adenocarcinoma infiltration level.Translational lung cancer research · 2024Article
- Machine Learning and Texture Analysis of [Biomedicines · 2024Article
- An [European radiology · 2024Article
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Authors and funding
16 authors.
Funding
Abstract
backgroundTo investigate the value of
methodsA total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA).
resultsAccording to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93-0.97) in the training group and 0.94 (95% CI: 0.89-0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM.
conclusionsThe integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.
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