Seminars in Thrombosis and Hemostasis

Risk of Venous Thromboembolism by Cancer Type: A Network Meta-Analysis

AbstractPatients with cancer have an increased risk of venous thromboembolism (VTE). Comparing tumor-specific VTE risk is complicated by factors such as surgery, disease stage, and chemotherapy. Network meta-analysis (NMA) using cancer types as network nodes enabled us to estimate VTE rates by leveraging comparisons across cancer types while adjusting for baseline VTE risk in individual studies. This study was conducted to estimate the risk of VTE by cancer type and factors influencing VTE risk. The Embase, MEDLINE, and Cochrane Library repositories were systematically searched to identify clinical trials and observational studies published from 2005 to 2022 that assessed the risk of primary cancer-related VTE among two or more distinct cancer types. Studies with similar cancer populations and study methods reporting VTE occurring within 1 year of diagnosis were included in the NMA. Relative VTE rates across cancer types were estimated with random-effects Bayesian NMAs. Absolute VTE rates were calculated from these estimates using the average VTE incidence in lung cancer (the most frequently reported type) as the “anchor.” From 2,603 records reviewed, 30 studies were included in this NMA. The general network described 3,948,752 patients and 18 cancer types: 3.1% experienced VTE within 1 year of diagnosis, with cancer-specific rates ranging from 0.7 to 7.4%. Consistent with existing VTE risk prediction tools, pancreatic cancer was associated with higher-than-average VTE risk. Other cancer types with high VTE risk were brain and ovarian cancers. The relative rankings of VTE risk for certain cancers changed based on disease stage and/or receipt of chemotherapy or surgery.

Machine Learning as a Diagnostic and Prognostic Tool for Predicting Thrombosis in Cancer Patients: A Systematic Review

AbstractKhorana 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.