ArticleCancer medicine2023
CT-based radiomics model to predict spread through air space in resectable lung cancer.
Article in Cancer medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of radiomics analysis for pulmonary cancer airway spread: a systematic review and meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Pooled it
- 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
- STAS More than a Prognostic Marker-An Evolving Factor in Operative and Adjuvant Treatment Decisions in Early-Stage NSCLC.Cancers · 2026Review
- Imaging advances of predicting spread through air spaces (STAS) in lung cancer: a narrative review.Journal of thoracic disease · 2026Review
- 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
- A narrative review of preoperative CT for predicting spread through air spaces of lung cancer.Translational lung cancer research · 2025Review
- Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.Cancers · 2024Article
- CT-based radiomics model to predict spread through air space in resectable lung cancer.Cancer medicine · 2023Article
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Authors and funding
7 authors.
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Abstract
backgroundSpread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS.
methodThere were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10-fold cross-validation.
resultsThe incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non-STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10-fold cross-validation had an AUC of 0.834.
conclusionCT-based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.
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