Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Xu, Qianjie; Lei, Haike; Li, Xiaosheng; Li, Fang; Shi, Hao; Wang, Guixue; Sun, Anlong; Wang, Ying; Peng, Bin

doi:10.1016/j.heliyon.2022.e12681

Cited by 11 publications

(14 citation statements)

References 36 publications

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“…Reviewers excluded studies that did not provide data or failed to meet the inclusion criteria. Overall, eight studies that discussed the use of ML for the diagnosis and prognosis of thrombosis in cancer patients [15][16][17][18][19][20][21][22] were included. These studies examined a total of 22,893 patients.…”

Section: Resultsmentioning

confidence: 99%

“…The types of cancer investigated included breast, gastric, colorectal, bladder, lung, esophageal, pancreatic, biliary, prostate, ovarian, genitourinary, headneck, and sarcoma. [15][16][17][18][19][20][21][22] Thrombosis was characterized as VTE (n ¼ 6), or peripherally inserted central catheter (PICC) thrombosis (n ¼ 2). All studies reported outcomes on the ML's predictive capacity.…”

Section: Resultsmentioning

confidence: 99%

“…The accuracy of the SVM model was 79.9%, with a sensitivity of 71.0% and a specificity of 80.2%. 22 These metrics reflect the model's ability to correctly identify patients at risk of VTE. 22 The study highlights the significant potential of ML models, particularly SVM, in enhancing the prediction of VTE among gastric cancer patients, leading to improved clinical decision-making and patient care.…”

Section: Venous Thromboembolismmentioning

confidence: 99%

“…22 These metrics reflect the model's ability to correctly identify patients at risk of VTE. 22 The study highlights the significant potential of ML models, particularly SVM, in enhancing the prediction of VTE among gastric cancer patients, leading to improved clinical decision-making and patient care. 22 Finally, Jin et al developed and validated ML models to predict cancer-associated DVT and compared these models with the KS.…”

Section: Venous Thromboembolismmentioning

confidence: 99%

See 3 more Smart Citations

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

El-Sherbini,

Coroneos,

Zidan

et al. 2024

Semin Thromb Hemost

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Venous Thromboembolismmentioning

confidence: 99%

Section: Venous Thromboembolismmentioning

confidence: 99%

See 2 more Smart Citations

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

El-Sherbini,

Coroneos,

Zidan

et al. 2024

Semin Thromb Hemost

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“…ML has been used by several groups to predict cancer-associated VTE, with encouraging results. [53][54][55][56][57] The models presented in those reports were limited to a combination of demographic, cancer-speci c and routine laboratory assay predictors. This is the rst attempt to use ML to estimate the risk of CAT based on somatic genomic predictors in a large cohort of individuals with a solid tumor.…”

Section: External Validation and Transfer Learningmentioning

confidence: 99%

Application of Machine Learning to the Prediction of Cancer-Associated Venous Thromboembolism

Mantha

Chatterjee

Singh

et al. 2023

Preprint

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Venous thromboembolism (VTE) is a common and impactful complication of cancer. Several clinical prediction rules have been devised to estimate the risk of a thrombotic event in this patient population, however they are associated with limitations. We aimed to develop a predictive model of cancer-associated VTE using machine learning as a means to better integrate all available data, improve prediction accuracy and allow applicability regardless of timing for systemic therapy administration. A retrospective cohort was used to fit and validate the models, consisting of adult patients who had next generation sequencing performed on their solid tumor for the years 2014 to 2019. A deep learning survival model limited to demographic, cancer-specific, laboratory and pharmacological predictors was selected based on results from training data for 23,800 individuals and was evaluated on an internal validation set including 5,951 individuals, yielding a time-dependent concordance index of 0.72 (95% CI = 0.70–0.74) for the first 6 months of observation. Adapted models also performed well overall compared to the Khorana Score (KS) in two external cohorts of individuals starting systemic therapy; in an external validation set of 1,250 patients, the C-index was 0.71 (95% CI = 0.65–0.77) for the deep learning model vs 0.66 (95% CI = 0.59–0.72) for the KS and in a smaller external cohort of 358 patients the C-index was 0.59 (95% CI = 0.50–0.69) for the deep learning model vs 0.56 (95% CI = 0.48–0.64) for the KS. The proportions of patients accurately reclassified by the deep learning model were 25% and 26% respectively. In this large cohort of patients with a broad range of solid malignancies and at different phases of systemic therapy, the use of deep learning resulted in improved accuracy for VTE incidence predictions. Additional studies are needed to further assess the validity of this model.

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Development of a VTE Prediction Model Based on Automatically Selected Features in Glioma Patients

Leontev,

Simakova,

Lukinov

et al. 2024

Lecture Notes in Computer Science

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Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

Cited by 11 publications

References 36 publications

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

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

Application of Machine Learning to the Prediction of Cancer-Associated Venous Thromboembolism

Development of a VTE Prediction Model Based on Automatically Selected Features in Glioma Patients

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