Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study
The CatBoost model integrating CECT environmental radiomics and clinicopathological parameters effectively predicts postoperative LA recurrence, supporting personalized adjuvant therapy decisions. Its interpretable framework emphasizes tumor heterogeneity (Radscore3/4) as a critical prognostic biomarker, providing mechanistic insights into LA recurrence.
Authors
- Yingjian Ye
- Jinfang Yang
- Yanli Niu (ORCID: https://orcid.org/0000-0002-8667-553X)
- Xin Zhou (ORCID: https://orcid.org/0000-0002-5026-8959)
- Lina Song (ORCID: https://orcid.org/0009-0008-9987-7617)
- Peng An (ORCID: https://orcid.org/0009-0007-4915-347X)
- Song Lin (ORCID: https://orcid.org/0000-0001-5721-274X)
- Junjie Liu (ORCID: https://orcid.org/0000-0002-0463-521X)
Institutions
- Hubei University of Medicine (CN)
- Xiang Yang No.1 People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-05-23
- DOI
- https://doi.org/10.3389/fonc.2025.1601674
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Health Commission of Hubei Province
- National Natural Science Foundation of China
- Natural Science Foundation of Hubei Province