Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study
The proposed decision-level (late fusion) model significantly reducing the risk of overfitting and demonstrating excellent robustness in multicenter external validation, which can predict VPI status in LAC, aid in prognostic stratification, and assist radiologists in achieving higher diagnostic performance.
Authors
- Junbo Zhao (ORCID: https://orcid.org/0000-0002-3637-2936)
- Liren Ding
- Bin Wang (ORCID: https://orcid.org/0000-0001-7771-5360)
- Tingting Wang (ORCID: https://orcid.org/0009-0005-4706-9659)
- Xiwen Sun
- Caizhong Chen
- Bhuva Maheshkumar Satishkumar
Institutions
- Tongji University (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Shanghai Mental Health Center (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Zhongshan Hospital (CN)
- Shanghai Pulmonary Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Shuguang Hospital (CN)
- Yangpu Hospital of Tongji University (CN)
Publication Details
- Journal
- Journal of Cardiothoracic Surgery
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1186/s13019-025-03488-6
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality