An interpretable machine learning model for predicting visceral pleural invasion in cT1 lung adenocarcinoma based on habitat analysis
The model demonstrates robust predictive accuracy for VPI status and effective prognosis stratification in cT1 stage lung adenocarcinoma patients, providing a non-invasive technique that significantly aids surgeons in preoperative planning and enhances clinical decision-making and patient outcomes.
Authors
- Xiangfeng Gan (ORCID: https://orcid.org/0000-0001-5839-0468)
- H. Dai (ORCID: https://orcid.org/0000-0002-3576-5121)
- Wenzeng Chen
- Xiaohui Duan (ORCID: https://orcid.org/0000-0002-2224-0887)
- Baishen Chen
- Wei Zhang (ORCID: https://orcid.org/0000-0002-6968-6974)
- Ju Chen (ORCID: https://orcid.org/0000-0001-7674-4776)
- Zhuojian Shen (ORCID: https://orcid.org/0000-0003-4738-5780)
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-11-21
- DOI
- https://doi.org/10.21037/qims-2024-2890
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00