ArticleQuantitative imaging in medicine and surgery2024
The value of predicting the invasiveness and degree of infiltration of pulmonary ground-glass nodules based on computed tomography features and enhanced quantitative analysis.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ACR Appropriateness Criteria® Indolent Lung Cancer.Journal of the American College of Radiology : JACR · 2026Guideline
- Evaluation of high-grade growth patterns in invasive adenocarcinomas manifesting as ground-glass lesions via clinical and qualitative and quantitative computed tomography features.Quantitative imaging in medicine and surgery · 2026Article
- Assessing Invasiveness of Ground-Glass Nodules Using Ternary-Class CT Radiomics Models: A Multi-Center Study with SHAP Explanations.Cancer management and research · 2026Article
- Exploring the risk factors for the growth of pure ground-glass nodules in a 3-year follow-up period.Quantitative imaging in medicine and surgery · 2025Article
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6 authors.
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Abstract
Background: The incidence and mortality rate of lung cancer are the highest in the world among all malignant tumors. Accurate assessment of ground-glass nodules (GGNs) is crucial in reducing lung cancer mortality. This study aimed to explore the value of computed tomography (CT) features and quantitative parameters in predicting the invasiveness and degree of infiltration of GGNs. Methods: Lesions were classified into three groups based on pathological types: the precursor glandular lesion (PGL) group, including atypical adenomatoid hyperplasia and adenocarcinoma Results: There were significant differences in lesion size, morphology, nodule type, bronchial abnormality, internal vascular sign and pleural retraction among the three groups (P<0.05). There were significant differences in all CT quantitative parameters (CT attenuation value in the plain phase, CT attenuation value in the arterial phase, CT attenuation value in the venous phase, arterial phase enhancement difference, venous phase enhancement difference, arterial phase enhancement index and venous phase enhancement index) among the three groups (P<0.001). The ROC curve analysis showed that the CT attenuation value in the plain phase, CT attenuation value in each enhanced phase, enhancement difference and enhancement index had good discriminatory power. Binary logistic regression analysis revealed that nodule type and internal vascular sign were independent risk factors for GGN invasiveness. Conclusions: CT features combined with enhanced scanning and quantitative analysis have important value in predicting the invasiveness of GGNs. The type of pulmonary nodule detected on CT (pure GGN or mixed GGN) and the presence of internal vascular signs are independent risk factors for GGN invasiveness.
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