Journal of computer assisted tomography · 2025
Predicting Visceral Pleural Invasion in Part-Solid and Solid Nodules Using CT Features: A Systematic Review, Meta-Analysis, and Independent Cohort Validation.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод в очереди
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Long Yu, Li Yong, Lin Libo, He ChangJiu, Qing HaoMiao, Liu JieKe, Zhou Peng
Аннотация
OBJECTIVE: To identify risk factors predicting visceral pleural invasion (VPI) in part-solid and solid nodules through meta-analysis, and to develop a predictive model in an independent cohort.
METHODS: The PubMed, Embase, and Web of Science databases were systematically searched to identify studies on pleural-related semantic, nodule semantic, and quantitative computed tomography (CT) features to predict VPI. The pooled odds ratios (ORs) for semantic features and standardized mean differences (SMDs) for quantitative features were calculated to develop a predictive model. A total of 203 patients (147 VPI-negative and 56 VPI-positive) were enrolled in the validation cohort between January and December 2024. The diagnostic performance of the model was assessed using the area under the receiver operating characteristic curve (AUC).
RESULTS: Thirteen studies with 3999 patients were included in this meta-analysis. Several key risk factors were identified to construct the predictive model, including pleural indentation (OR: 3.428, 95% CI: 2.559-4.593), nodule type (OR: 4.867, 95% CI: 3.915-6.051), spiculation (OR: 2.581, 95% CI: 1.640-4.062), lobulation (OR: 1.855, 95% CI: 1.148-2.997), vessel convergence sign (OR: 3.606, 95% CI: 1.698-7.656), and the maximum solid diameter (SMD: 0.894, 95% CI: 0.600-1.188). This model yielded an AUC of 0.892 (95% CI: 0.840-0.931) in the validation cohort.
CONCLUSIONS: This meta-analysis involved the construction of an effective model for predicting VPI by integrating pleural indentation, nodule type, spiculation, lobulation, vessel convergence sign, and maximum solid diameter, which could inform preoperative clinical decision-making for subpleural part-solid and solid nodules.
METHODS: The PubMed, Embase, and Web of Science databases were systematically searched to identify studies on pleural-related semantic, nodule semantic, and quantitative computed tomography (CT) features to predict VPI. The pooled odds ratios (ORs) for semantic features and standardized mean differences (SMDs) for quantitative features were calculated to develop a predictive model. A total of 203 patients (147 VPI-negative and 56 VPI-positive) were enrolled in the validation cohort between January and December 2024. The diagnostic performance of the model was assessed using the area under the receiver operating characteristic curve (AUC).
RESULTS: Thirteen studies with 3999 patients were included in this meta-analysis. Several key risk factors were identified to construct the predictive model, including pleural indentation (OR: 3.428, 95% CI: 2.559-4.593), nodule type (OR: 4.867, 95% CI: 3.915-6.051), spiculation (OR: 2.581, 95% CI: 1.640-4.062), lobulation (OR: 1.855, 95% CI: 1.148-2.997), vessel convergence sign (OR: 3.606, 95% CI: 1.698-7.656), and the maximum solid diameter (SMD: 0.894, 95% CI: 0.600-1.188). This model yielded an AUC of 0.892 (95% CI: 0.840-0.931) in the validation cohort.
CONCLUSIONS: This meta-analysis involved the construction of an effective model for predicting VPI by integrating pleural indentation, nodule type, spiculation, lobulation, vessel convergence sign, and maximum solid diameter, which could inform preoperative clinical decision-making for subpleural part-solid and solid nodules.