Enhancing venous thromboembolism risk prediction after immunotherapy for lung cancer.

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

Abstract read

In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Shengyuan WangDepartment of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.

Siwan WenDepartment of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.

Mengmeng ZhaoDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.

Wanzhen LiDepartment of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.

Minglei YangDepartment of Thoracic Surgery, Ningbo No. 2 Hospital, Wenzhou Medical University, Zhejiang, China.

Chang ChenDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.

Xianghuai XuDepartment of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

clinical modelimmunotherapylung cancervenous thromboembolism

Identifiers

PMID42555406

DOI10.1016/j.isci.2026.116890

PMCPMC13392860

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.