ArticleInternational journal of surgery (London, England)2025
Machine learning-based quantitative prediction of spread through air spaces in primary lung adenocarcinoma using intratumoural heterogeneity scores.
Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Ensemble machine learning classifiers based on computed tomography radiomics for predicting spread through air spaces in lung adenocarcinoma: a multicenter retrospective cohort study with transcriptomic interpretation.Quantitative imaging in medicine and surgery · 2026Article
- Development and validation of a machine-learning-based triage model incorporating systemic inflammatory, nutritional, coagulation, and tumor marker parameters for identifying baseline distant metastasis in lung cancer.Frontiers in oncology · 2026Article
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17 authors.
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Abstract
backgroundSpread through air spaces (STAS) is a distinct, aggressive pattern of primary lung adenocarcinoma (LUAD) that affects both prognosis and treatment strategies for patients. This study aimed to quantify intratumoural heterogeneity (ITH) and integrate the quantitative metrics with intratumoural-peritumoural habitat features and clinical-radiologic characteristics to preoperatively predict the STAS status of primary LUAD and further explore the potential biological basis underlying the prediction model.
methodsConventional radiomics features and habitat features were extracted from intratumoural and peritumoural regions on preoperative computerized tomography (CT) images. A new index, the ITH score, was developed to quantify ITH levels. Univariable and multivariable logistic regression analyses were conducted to identify clinical-radiologic characteristics associated with STAS. Various machine learning algorithms were used to build the prediction models. Additionally, intratumoural-peritumoural habitat features, ITH score, and clinical-radiologic characteristics were integrated into a combined model. Finally, 24 patients with RNA sequencing data were utilised for gene expression analysis.
resultsA total of 1268 patients (median age, 60 years; IQR, 53.8-66.0 years; 850 female) were divided into the training set (n = 943), validation set (n = 236), and external test set (n = 89). Using the Light Gradient Boosting Machine classifier, the combined model demonstrated the highest predictive performance for STAS, achieving an AUC value of 0.97 in the training, 0.98 in the validation, and 0.91 in the external test set. Differentially expressed genes in a high combined model probability group were associated with monocarboxylic acid transport and metabolism.
conclusionsThe combined model demonstrated superior performance in predicting STAS in patients with primary LUAD.
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