Development and multicenter validation of a predictive model for malignant pleural effusion recurrence.

Xin Hu, Yongjie Jiang, Yiluo Heibi, Li Jiang, Yuying Li

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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

–field-weighted citation impact

1 · What the graph read from it

What it found

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xin HuDepartment of Respiratory and Critical Care Medicine, Inflammations & Allergic Diseases Research Unit, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Yongjie JiangDepartment of Respiratory and Critical Care Medicine, Inflammations & Allergic Diseases Research Unit, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Yiluo HeibiDepartment of Respiratory and Critical Care Medicine, Guang'an People's Hospital, Guang'an, China.

Li JiangDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.

Yuying LiDepartment of Respiratory and Critical Care Medicine, Inflammations & Allergic Diseases Research Unit, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early prediction of malignant pleural effusion (MPE) recurrence within 3 months is essential for optimizing management in lung cancer patients. This study developed and validated a machine learning model to estimate the 3-month recurrence risk of MPE in patients with newly diagnosed lung cancer. Using data from 221 patients for model training and 237 from two external validation cohorts, the Elastic Net model-based solely on four routine clinical features (treatment regimen, alanine aminotransferase, total pleural effusion volume, and tumor diameter)-achieved excellent performance, with areas under the curve of 0.848 and 0.940 in external validation. The model significantly outperformed other machine learning approaches. An interactive risk stratification tool was further developed to classify patients into four risk groups, enabling early identification and individualized management of high-risk patients. This tool offers a practical and generalizable solution for guiding clinical decision-making.

Indexed as

health scienceshealth technologymedical specialtymedicineoncology

Identifiers

PMID41847612

DOI10.1016/j.isci.2026.115040

PMCPMC12989844

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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.