Enhancing venous thromboembolism risk prediction after immunotherapy for lung cancer.

Authors

Shengyuan Wang, Siwan Wen, Mengmeng Zhao, Wanzhen Li, Minglei Yang, Chang Chen, Xianghuai Xu

Published in

iScience. Volume 29. Issue 8. Pages 116890. Aug 21, 2026. Epub Jul 21, 2026.

Abstract

Assessing venous thromboembolism (VTE) risk after immunotherapy remains important for lung cancer management. We analyzed 2,300 patients receiving first-line immunotherapy from two centers, randomly assigned to training (70%), validation (15%), and internal test (15%) sets, and included 491 patients from an independent external center for external validation. Five feature-selection methods and five machine-learning algorithms were compared to develop a 6-month VTE prediction model. The Lasso-logistic model showed the best performance, with areas under the curve of 0.692 and 0.728 in the internal and external test sets, respectively, outperforming Khorana, Padua, PROTECHT, ONKOTEV, and COMPASS-CAT scores (all p < 0.05). The high-risk group had a higher cumulative VTE incidence than the low-risk group (12.3% vs. 4.8%, p = 0.006). Shapley additive explanations (SHAP) analysis identified D-dimer and Eastern Cooperative Oncology Group (ECOG) performance status as the most influential predictors, supporting individualized thromboprophylaxis decisions.

PMID:
42555406
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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