A Bayesian deep learning model with consolidation-to-tumor ratio (CTR) prior revolutionizes the prediction of spread through air spaces (STAS) in stage IA lung adenocarcinoma: a large-scale diagnostic study
The proposed model based on the CTR prior offers significant advantages in predicting STAS in patients with stage IA LUAD, and incorporating doctors' knowledge as priors can effectively guide the development of DL models.
Authors
- Jiandong Mei (ORCID: https://orcid.org/0000-0003-0842-5540)
- Huahang Lin
- Zhiyu Peng (ORCID: https://orcid.org/0000-0003-2349-750X)
- Xin Tan (ORCID: https://orcid.org/0000-0001-9515-2346)
- Haokun Wu
- Zetao Liu (ORCID: https://orcid.org/0000-0002-5011-6429)
- Yi Zhang (ORCID: https://orcid.org/0000-0003-4299-1511)
- Nan Chen
- Xiuyuan Xu (ORCID: https://orcid.org/0009-0006-4526-2946)
- Yi Le (ORCID: https://orcid.org/0000-0002-7734-3808)
- Zhaokang Huang (ORCID: https://orcid.org/0009-0005-0872-0111)
- Lingyu Zhou (ORCID: https://orcid.org/0000-0003-2783-2918)
- Chenglin Guo
- Jie Cao (ORCID: https://orcid.org/0000-0003-1555-7795)
- Kunhao Wu
- Lin Qiu (ORCID: https://orcid.org/0009-0007-2419-7281)
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Chengdu University (CN)
Publication Details
- Journal
- Translational Lung Cancer Research
- Published
- 2025-05-01
- DOI
- https://doi.org/10.21037/tlcr-24-890
- Primary Topic
- Inhalation and Respiratory Drug Delivery
- Type
- article
- Field-Weighted Citation Impact
- 0.00