ArticleJournal of cardiothoracic surgery2023
An individualized nomogram for predicting and validating spread through air space (STAS) in surgically resected lung adenocarcinoma: a single center retrospective analysis.
Article in Journal of cardiothoracic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 11 citations in OpenAlex.
- 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
- Development of a machine learning model for preoperative prediction of spread through air spaces in resectable non-small cell lung cancer: A single-center retrospective study.Oncology letters · 2026Article
- Correlations and prognostic impacts of tumor spread through airspaces in surgically resected non-small cell lung cancer: a retrospective study from Jordan.Journal of pathology and translational medicine · 2026Article
- Development and internal validation of predictive models for spread through air spaces in clinical stage IA lung adenocarcinoma.General thoracic and cardiovascular surgery · 2025Article
- A deep learning-clinical combined model with SHapley Additive exPlanations (SHAP) method for assessing the tumor spread through air spaces in lung adenocarcinoma: a multicohort retrospective study.Quantitative imaging in medicine and surgery · 2025Article
- CT feature-based nomogram for predicting tumor spread through air spaces in stage IA lung adenocarcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- 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.Translational lung cancer research · 2025Article
- Nomogram model for the preoperative prediction of spread through air spaces in sub-centimeter non-small cell lung cancer.Journal of cardiothoracic surgery · 2025Article
- Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.Respiratory research · 2025Article
- A nomogram integrating the clinical and CT imaging characteristics for assessing spread through air spaces in clinical stage IA lung adenocarcinoma.Frontiers in immunology · 2025Article
- Development and validation of machine learning models for predicting STAS in stage I lung adenocarcinoma with part-solid and solid nodules: a two-center study.Frontiers in oncology · 2025Article
- Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer.NPJ precision oncology · 2024Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveA single-center study was conducted to explore the association between STAS and other clinical features in surgically resected adenocarcinoma to enhance our current understanding of STAS.
methodsWe retrospectively enrolled patients with lung adenocarcinoma (n = 241) who underwent curative surgeries. Patients undergoing surgery in 2019 were attributed to the training group (n = 188) and those undergoing surgery in January 2022 to June 2022 were attributed to the validation (n = 53) group. Univariate and multivariate logistic regression analyses were used to identify predictive factors for STAS, which were used to construct a simple nomogram. Furthermore, ROC and calibration curves were used to evaluate the performance of the nomogram. In addition, we conducted decision curve analysis (DCA) to assess the clinical utility of this nomogram.
resultsIn our cohort, 52 patients were identified as STAS-positive (21.6%). In univariate analysis, STAS was significantly associated with age, surgical approach, CEA, CTR (Consolidation Tumor Ratio), TNM stage, tumor grade, gross tumor size, resection margin, vessel cancer embolus, pleural invasion, lymph node metastasis, high ki67 and positive PD-L1 staining (P < 0.05). Lower age, CTR > 0.75, vessel cancer embolus, high Ki67 and PD-L1 stain positive were significant predictors for STAS during multivariate logistics analysis. A simple nomogram was successfully constructed based on these five predictors. The AUC values of our nomogram for the probability of tumor STAS were 0.860 in the training group and 0.919 in the validation group. In addition, the calibration curve and DCA validated the good performance of this model.
conclusionA nomogram was successfully constructed to identify the presence of STAS in surgically resected lung adenocarcinoma patients.
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