ArticleJournal of thoracic disease2025
Clinicopathological-CT model for predicting PD-L1 expression in resectable early-stage non-small cell lung cancer.
Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Construction and validation of a CT-based radiomics-deep learning signature for non-invasive prediction of PD-L1 expression and immunotherapy outcomes in non-small cell lung cancer.Translational lung cancer research · 2026Article
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8 authors.
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Abstract
Background: The expression of programmed death-ligand 1 (PD-L1) has an impact on survival outcomes in non-small cell lung cancer (NSCLC) patients, but preoperative diagnosis is challenging. This study aimed to construct and validate a non-invasive model for predicting PD-L1 expression in early-stage resected NSCLC based on computed tomography (CT) features and clinicopathological characteristics. Methods: In this retrospective study, the clinical, pathological, and CT data were obtained from consecutive NSCLC patients who had undergone resection from January 2016 to March 2018. The clinicopathologic, CT, and clinicopathologic-CT models were constructed after univariate and multivariate logistic regression analyses. The Kaplan-Meier analysis and log-rank test were used for survival analysis. Results: A total of 679 consecutive patients with 695 early-stage NSCLC nodules were included, and there were 243 {median age 57 [interquartile range (IQR), 48-63] years; 152 females} in the positive PD-L1 group and 452 [median age 58 (IQR, 50-65) years; 315 females] in the negative PD-L1 group. Smoking history, spread through air spaces (STAS), average CT value, lobulation, and cytokeratin 7 (CK7) were independent predictors of positive PD-L1 NSCLC. In validation set, the area under the curve (AUC) value of clinicopathologic model, CT model and clinicopathologic-CT model were 0.630 [95% confidence interval (CI): 0.621-0.702; sensitivity =0.701; specificity =0.542], 0.629 (95% CI: 0.574-0.638; sensitivity =0.624; specificity =0.615), and 0.819 (95% CI: 0.740-0.837; sensitivity =0.763; specificity =0.760), respectively. The clinicopathologic-CT model had higher predictive performance than the other two models by DeLong test, both in the training and validation sets. Conclusions: Smoking history, STAS, average CT value, lobulation, and CK7 might be helpful in the diagnosis of PD-L1 expression in patients with early-stage NSCLC. The clinicopathologic-CT model had higher predictive performance than the clinicopathologic and CT models.
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