A deep learning approach for predicting visceral pleural invasion in cT1 lung adenocarcinoma
The developed deep learning signature offers a robust instrument for the precise prognostication of vascular invasion in stage cT1 lung adenocarcinoma, thereby enhancing stratification for prognostic evaluation. Moreover, the application of this advanced computational model aids in the refinement of therapeutic approach formulation for individuals diagnosed with cT1 lung adenocarcinoma.
Authors
- Wenwen Huo (ORCID: https://orcid.org/0000-0001-5112-1951)
- Xiangfeng Gan (ORCID: https://orcid.org/0000-0001-5839-0468)
- Shijiancong Liu
- Xiaojian Li (ORCID: https://orcid.org/0000-0002-3175-7810)
- Beilong Zhong
- Xujie Xiao
- Huili Li (ORCID: https://orcid.org/0009-0004-4667-1587)
- Qingdong Cao
Institutions
- Qianjiang Central Hospital (CN)
Publication Details
- Journal
- Journal of Thoracic Disease
- Published
- 2024-09-01
- DOI
- https://doi.org/10.21037/jtd-24-601
- Citations
- 6
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.51