ArticleBMC cancer2025
Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.
Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in computational modeling of thrombosis: Bridging mechanistic insights and clinical translation.Journal of thrombosis and thrombolysis · 2026Pooled it
- Machine Learning Model Predicts New-Onset Lower Extremity Deep Vein Thrombosis After Pelvic Fracture Surgery and Targeted Diagnosis.Clinical epidemiology · 2026Article
- Role of artificial intelligence in screening and medical imaging of precancerous gastric diseases.World journal of clinical oncology · 2025Review
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Authors and funding
10 authors.
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Abstract
backgroundGastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in postoperative patients. Therefore, it is particularly important to find a suitable way to predict the risk of postoperative occurrence of DVT in GC patients. This study aims to explore the effectiveness of using machine learning (ML) assisted radiomics to build imaging models for prediction of lower extremity DVT occurrence in GC patients after surgery.
methodsIncluded in this retrospective study were eligible patients who underwent surgery for GC. CT imaging data from these patients were collected and divided into a training set and a validation set. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to reduce the dimensionality of variables in the training set. Four machine learning algorithms, known as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and naive Bayes (NB), were used to develop models for predicting the risk of lower extremity DVT occurrence in GC patients. These models were subsequently validated using the internal validation set and an external validation cohort.
resultsLASSO analysis identified 10 variables, based on which four ML models were established, which were then incorporated with the clinical characteristics to predict lower extremity DVT occurrence in the training set. Among these models, RF and NB demonstrated the highest predictive performance, achieving an AUC of 0.928, while SVM and XGBoost achieved a slightly lower AUC of 0.915 and 0.869, respectively.
conclusionML algorithms based on imaging information may prove to be novel non-invasive models for predicting postoperative occurrence of DVT in GC patients.
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