Key result
Machine learning models, particularly extreme gradient boosting, demonstrated superior predictive capacity for cancer-associated thrombosis compared to the traditional Khorana score.
Why the study?
The Khorana score ignores several risk factors and has poor predictability in some cancers, warranting consolidation of literature evaluating machine learning algorithms to predict cancer-associated thrombosis.
Do machine learning algorithms improve the prediction of thrombosis in adult cancer patients compared to traditional risk assessment models like the Khorana score?
Systematic Review (n=22,893)
Do machine learning algorithms improve the prediction of thrombosis in adult cancer patients compared to traditional risk assessment models like the Khorana score?
Machine learning algorithms, particularly XGBoost and SVM, demonstrate superior predictive accuracy for cancer-associated thrombosis compared to traditional tools like the Khorana score, highlighting their potential for improving clinical risk stratification.
May enhance cancer-associated thrombosis risk stratification; leaves open need for prospective validation before practice change.
Khorana score (KS) is an established risk assessment model for predicting cancer-associated thrombosis. However, it ignores several risk factors and has poor predictability in some cancer types. Machine learning (ML) is a novel technique used for the diagnosis and prognosis of several diseases, including cancer-associated thrombosis, when trained on specific diagnostic modalities. Consolidating the literature on the use of ML for the prediction of cancer-associated thrombosis is necessary to understand its diagnostic and prognostic abilities relative to KS. This systematic review aims to evaluate the current use and performance of ML algorithms to predict thrombosis in cancer patients. This study was conducted per Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Databases Medline, EMBASE, Cochrane, and ClinicalTrials.gov, were searched from inception to September 15, 2023, for studies evaluating the use of ML models for the prediction of thrombosis in cancer patients. Search terms “machine learning,” “artificial intelligence,” “thrombosis,” and “cancer” were used. Studies that examined adult cancer patients using any ML model were included. Two independent reviewers conducted study selection and data extraction. Three hundred citations were screened, of which 29 studies underwent a full-text review, and ultimately, 8 studies with 22,893 patients were included. Sample sizes ranged from 348 to 16,407 patients. Thrombosis was characterized as venous thromboembolism (n = 6) or peripherally inserted central catheter thrombosis (n = 2). The types of cancer included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, head–neck, and sarcoma. All studies reported outcomes on the ML's predictive capacity. The extreme gradient boosting appears to be the best-performing model, and several models outperform KS in their respective datasets.
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Sherbini et al. (2024) conducted a systematic review in Cancer-associated thrombosis (n=22,893). Machine learning models vs. Khorana score or traditional logistic regression was evaluated on Predictive capacity for thrombosis (AUC, sensitivity, specificity, accuracy). Machine learning models, particularly extreme gradient boosting, demonstrated superior predictive capacity for cancer-associated thrombosis compared to the traditional Khorana score.
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