Abstract
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.
Keywords
thrombosis - cancer - machine learning - artificial intelligence - risk assessment
