ArticleTranslational lung cancer research2025
A Bayesian deep learning model with consolidation-to-tumor ratio (CTR) prior revolutionizes the prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma: a large-scale diagnostic study.
Article in Translational lung cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Spread through air spaces as a marker of nonlinear evolutionary trajectories in lung cancer: implications for the 9th edition TNM classification.Journal of pathology and translational medicine · 2026Article
- Predicting STAS in peripheral stage I lung adenocarcinoma: the incremental value of CT-based tumor disappearance rate, with a focus on part-solid nodules.BMC medical imaging · 2026Article
- Development of a CT-based integrated model combining clinical features, radiomics, and deep learning to predict STAS in subsolid nodules of stage I lung cancer: a retrospective cohort study.Journal of thoracic disease · 2026Article
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16 authors.
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Abstract
Background: The preoperative prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma (LUAD) is crucial for selecting the appropriate surgical approach and improving patient outcomes. Previous research has confirmed that there is a significant correlation between consolidation-to-tumor ratio (CTR) and STAS. This study aimed to develop a Bayesian deep learning (DL) model based on the CTR prior to predict STAS in patients with stage IA LUAD. Methods: This large-scale diagnostic study included patients with solitary primary invasive LUAD who underwent complete resection between November 2017 and October 2023. Enrolled patients were randomly assigned to training, validation, and test cohorts in a 7:2:1 ratio. Using a variational Bayesian inference framework, we developed a DL model based on the CTR prior (STAS-DL Results: A total of 1,374 patients were included, with 961 in the training cohort, 275 in the validation cohort, and 138 in the test cohort. The results showed that CTR in the STAS-positive group was significantly higher than that in the STAS-negative group [0.63 (interquartile range, 0.36, 0.98) Conclusions: The proposed model based on the CTR prior offers significant advantages in predicting STAS in patients with stage IA LUAD, and incorporating doctors' knowledge as priors can effectively guide the development of DL models.
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