ArticleJournal of thoracic disease2024
Baseline computed tomography imaging findings could assist in early diagnosis of visceral pleural invasion for newly discovered early subpleural non-small cell lung cancer: T1 or T2.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- CT-based nomogram for estimating visceral pleural invasion in solid pulmonary adenocarcinoma nodules with pleural contact: a retrospective single-center study.BMC medical imaging · 2026Article
- Contrast-enhanced CT-based radiomics for predicting visceral pleural invasion in early-stage non-small cell lung cancer.Insights into imaging · 2026Article
- Development of anQuantitative imaging in medicine and surgery · 2025Article
- Advances in the clinical diagnosis of lung cancer using contrast-enhanced ultrasound.Frontiers in medicine · 2025Review
- Prognostic significance of visceral pleural changes in stage IA lung adenocarcinoma: a retrospective study.Frontiers in oncology · 2025Article
- CT imaging-based nomogram for predicting pleural invasion in non-small cell lung cancer.American journal of translational research · 2025Article
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6 authors.
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Abstract
Background: Preoperative accurate visceral pleural infiltration (VPI) diagnosis for T1-size non-small cell lung cancer (NSCLC) is significant for clinical decision-making. The study aimed to explore the diagnostic efficacy of computed tomography (CT) imaging features and serum biomarkers in diagnosing VPI in newly discovered subpleural NSCLC ≤3 cm. Methods: There were 447 patients with NSCLC ≤3 cm retrospectively enrolled and assigned to the VPI group (n=81) and the non-VPI group (n=366) based on elastic fiber staining results. The serum biomarkers and CT imaging features were obtained for each subject. Univariate and multivariate analyses were used to identify the independent predictors for VPI. Area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the diagnostic performance of each independent predictor and combined predictors in predicting VPI, with performance compared using the DeLong test. Results: For tumor biomarkers, the VPI group had a significantly higher percentage of cases with abnormal carcino-embryonic antigen (CEA) level, cytokeratin 19 fragment (CYFRA21-1) level, and pro-gastrin-releasing peptide (ProGRP) level than that of the non-VPI group (P<0.001, P=0.003, P=0.004). However, in multivariate analysis, only the lesion-pleura relationship patterns type Ia [odds ratio (OR) =20.689; 95% confidence interval (CI): 5.058-84.622; P<0.001], type Ib (OR =5.155; 95% CI: 1.178-22.552; P=0.03), type II (OR =7.154; 95% CI: 1.733-29.53; P=0.007) with type III as reference, solid lesion density (OR =9.954; 95% CI: 4.976-19.911; P<0.001) with part-solid density as reference were identified as the independent predictors for VPI. In predicting VPI, the combined model (AUC =0.885) significantly outperformed models based on lesion density (AUC =0.833) and lesion-pleura relationship patterns (AUC =0.655) (all P<0.001). Conclusions: The CT predictors for VPI in patients with subpleural NSCLC (≤3 cm) were lesion density and lesion-pleura relationship patterns (pleural attachment and indentation), but not serum tumor biomarkers.
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