ArticleChinese journal of cancer research = Chung-kuo yen cheng yen chiu2025
Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study.
Article in Chinese journal of cancer research = Chung-kuo yen cheng yen chiu, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Application value of high-resolution CT imaging features combined with texture analysis in patients with solitary pulmonary nodules.Frontiers in oncology · 2026Article
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13 authors.
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Abstract
Objective: This study aimed to construct a model that predicts invasive lung cancer using longitudinal radiological features from multiple low-dose computed tomography (LDCT) scans, thereby addressing overdiagnosis in lung cancer screening. Methods: In this retrospective study, 628 patients with pulmonary nodules who underwent three LDCT scans followed by surgical resection were categorized into invasive carcinoma (n=155) and non-invasive nodule (n=473) groups on the basis of pathological diagnosis. This derivation aimed to identify risk factors and construct a multivariate logistic model. The predictive performance was externally validated in two independent cohorts (retrospectively designed, n=252; prospectively designed, n=269). The discrimination and calibration of the model were evaluated using area under the curve (AUC), and calibration plots. Decision curve analysis (DCA) was further performed to evaluate the net benefit in practical clinical scenarios. Results: The model, termed multiple CTs-invasive lung cancer (MCT-ILC), incorporated eleven factors encompassing nodule features at baseline and feature variability during follow-up. The standard deviation of diameter variability (SD Conclusions: The MCT-ILC model could assess pulmonary nodule invasiveness, potentially mitigating overdiagnosis in lung cancer screening.
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