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Complementary Clinical and Explainable Artificial Intelligence Models for Risk Stratification of Cancer-associated Venous Thromboembolism

Year: 2026

Type: article

Abstract: Introduction Cancer-associated venous thromboembolism (CAT) causes major morbidity and mortality, and current risk models show only moderate performance with limited biological interpretability. Combining clinical prediction with explainable artificial intelligence (AI) may offer complementary strategies for personalized CAT risk assessment. Objectives To develop and internally validate two complementary approaches for CAT prediction: a pragmatic clinical risk score adaptable to D-dimer availability, and an explainable machine learning model integrating clinical and laboratory biomarkers. Materials and methods A prospective multicenter cohort of 806 newly diagnosed cancer patients without prior chemotherapy was followed for six months; 47 CAT events occurred. Patients were split into derivation/training (70%) and validation (30%) cohorts. A weighted clinical score was derived by multivariable logistic regression, scoring: tumor site (pancreatic/gastric +3; lung, gynecologic, lymphoma, bladder, or testicular +2), adenocarcinoma/carcinoma histology (+3), platinum-based or gemcitabine chemotherapy (+1), diabetes mellitus or long-term insulin (+1), prior VTE (+1), platelets ≥150 × 10⁹/L (+1), leukocytes ≥9 × 10⁹/L (+2), hemoglobin ≤8 g/dL or erythropoiesis-stimulating agent use (+2), age ≥75y (+2), and D-dimer ≥1.44 mg/L when available (+2). Thresholds: without D-dimer, low risk <9, high risk ≥9; with D-dimer, low risk <10, high risk ≥10. Only D-dimer added independent predictive value among biomarkers tested. ML algorithms were compared via 10-fold cross-validation; LightGBM performed best. Performance was assessed by C-index, sensitivity, specificity, predictive values, and bootstrap CIs; SHAP was used for interpretability. Results CAT patients more often had adenocarcinoma (59.6% vs 37.8%, p = 0.005) and pancreatic tumors (14.9% vs 3.2%, p < 0.001), higher D-dimer (median 1963 vs 699 ng/mL, p < 0.001), lower hemoglobin, and higher platelets/leukocytes. The clinical score reached validation C-indices of 0.65 without D-dimer and 0.68 with D-dimer (n = 469, 31 events), with specificity rising from 0.91 to 0.94 and positive predictive value from 0.23 to 0.33; negative predictive value stayed high (0.96-0.97). CAT incidence was 22-33% in high-risk versus 3-4% in low-risk patients. The explainable LightGBM model reached a validation C-index of 0.68, specificity of 0.82, and negative predictive value of 0.96. SHAP ranked D-dimer as the strongest predictor, followed by tumor histology, platelet count, hemoglobin, inflammatory biomarkers, diabetes mellitus, and age. Discussion The two approaches are complementary: the clinical score is simple and robust even without D-dimer, while adding D-dimer improves classification where available. The explainable AI model gives individualized, transparent, biologically plausible predictions with differently weighted clinical and laboratory contributions. Both models reliably identified low-risk patients. Conclusion These complementary clinical and explainable machine learning models achieved similar discrimination while addressing different clinical needs, offering practical, biologically interpretable strategies for CAT risk stratification, though external validation is still required before routine clinical use. Funding Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, grant #2016/14172-6). Approved by the Research Ethics Committee, University of Campinas (CAAE 87212318.7.0000.5404).

Source: Hematology Transfusion and Cell Therapy

DOI: 10.1016/j.htct.2026.108078

Institutions: Hospital e Maternidade Celso Pierro, Instituto de Ciências Ambientais, Químicas e Farmacêuticas, Universidade Federal de São Paulo, Universidade Estadual de Campinas (UNICAMP)

Language: English

Topic: Venous Thromboembolism Diagnosis and Management

Subfield: Internal Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W7223115897

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