Department of Biomedical and Molecular Sciences, School of Medicine, Queen's University, Kingston, Ontario, Canada.
School of Baccalaureate Nursing, St Lawrence College, Kingston, Ontario, Canada.
Semin Thromb Hemost. 2024 Sep;50(6):809-816. doi: 10.1055/s-0044-1785482. Epub 2024 Apr 11.
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 ( = 6) or peripherally inserted central catheter thrombosis ( = 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.
Khorana 评分（KS）是一种用于预测癌症相关血栓形成的既定风险评估模型。然而，它忽略了一些风险因素，并且在某些癌症类型中的预测能力较差。机器学习（ML）是一种用于诊断和预测包括癌症相关血栓形成在内的多种疾病的新技术，当在特定诊断模式上进行训练时。综合关于使用 ML 预测癌症相关血栓形成的文献，对于了解其相对于 KS 的诊断和预后能力是必要的。本系统评价旨在评估 ML 算法在预测癌症患者血栓形成方面的当前使用情况和性能。本研究按照系统评价和荟萃分析的首选报告项目进行。从成立到 2023 年 9 月 15 日，检索了 Medline、EMBASE、Cochrane 和 ClinicalTrials.gov 数据库，以评估评估 ML 模型在预测癌症患者血栓形成中的使用情况。使用了“机器学习”、“人工智能”、“血栓形成”和“癌症”等搜索词。纳入了使用任何 ML 模型检查成年癌症患者的研究。两名独立审查员进行了研究选择和数据提取。筛选了 300 条引文，其中 29 项研究进行了全文审查，最终纳入了 8 项研究，共 22893 名患者。样本量范围从 348 到 16407 名患者。血栓形成特征为静脉血栓栓塞症（ = 6）或外周插入中心导管血栓形成（ = 2）。所包括的癌症类型包括乳腺癌、胃癌、结直肠癌、膀胱癌、肺癌、食管癌、胰腺癌、胆管癌、前列腺癌、卵巢癌、泌尿生殖系统癌、头颈部癌和肉瘤。所有研究均报告了 ML 预测能力的结果。极端梯度增强似乎是表现最佳的模型，并且在其各自的数据集，几种模型的性能优于 KS。
