Key result
Machine learning models (RF and XGB) predicted venous thromboembolism in gastric cancer patients with an AUC of 0.825, accuracy of 0.799, sensitivity of 0.710, and specificity of 0.802.
Why the study?
Stomach cancer has a high thrombosis rate, but there is no suitable venous thromboembolism prediction model to assess risk in these patients.
Do machine learning algorithms accurately predict venous thromboembolism in gastric cancer patients compared to traditional logistic regression?
Observational (n=3,092)
Do machine learning algorithms accurately predict venous thromboembolism in gastric cancer patients compared to traditional logistic regression?
Machine learning models using routine clinical variables can accurately predict the risk of venous thromboembolism in patients with gastric cancer, potentially aiding in targeted prevention strategies.
Answers are grounded in this paper and cite their sources.
ML models show moderate VTE prediction accuracy in gastric cancer; hypothesis-generating and should not yet change practice.
Stomach cancer (GC) has one of the highest rates of thrombosis among cancers and can lead to considerable morbidity, mortality, and additional costs. However, to date, there is no suitable venous thromboembolism (VTE) prediction model for gastric cancer patients to predict risk. Therefore, there is an urgent need to establish a clinical prediction model for VTE in gastric cancer patients. We collected data on 3092 patients between January 1, 2018 and December 31, 2021. And after feature selection, 11 variables are reserved as predictors to build the model. Five machine learning (ML) algorithms are used to build different VTE predictive models. The accuracy, sensitivity, specificity, and AUC of these five models were compared with traditional logistic regression (LR) to recommend the best VTE prediction model. RF and XGB models have selected the essential characters in the model: Clinical stage, Blood Transfusion History, D-Dimer, AGE, and FDP. The model has an AUC of 0.825, an accuracy of 0.799, a sensitivity of 0.710, and a specificity of 0.802 in the validation set. The model has good performance and high application value in clinical practice, and can identify high-risk groups of gastric cancer patients and prevent venous thromboembolism.
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Xu et al. (2023) conducted an observational in Gastric cancer (n=3,092). Machine learning prediction models (RF and XGB) vs. Traditional logistic regression (LR) was evaluated on Venous thromboembolism (VTE) prediction (AUC 0.825). Machine learning models (RF and XGB) predicted venous thromboembolism in gastric cancer patients with an AUC of 0.825, accuracy of 0.799, sensitivity of 0.710, and specificity of 0.802.
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