Cross-dataset adaptation of voxel-level deep radiomics for predicting survival in inoperable locally advanced NSCLC treated with immunotherapy
We developed a voxel-level deep radiomics framework that bridges the data gap in immunotherapy research through fine-tuning on a limited immunotherapy dataset, and subsequent validation on an independent immunotherapy testing dataset, demonstrating robust generalizability.
Authors
- Meng Yan (ORCID: https://orcid.org/0000-0001-7073-0007)
- Zhaoyu Jiang (ORCID: https://orcid.org/0000-0002-6865-7602)
- Wenhao Ji
- Z. Zhang
- Andre Dekker
- Xiaojing Lai
- Leonard Wee
- Han Cheng
- Jin Wang
Institutions
- Colorado School of Public Health (US)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Zhejiang Cancer Hospital (CN)
- Maastro Clinic (NL)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2026-03-03
- DOI
- https://doi.org/10.3389/fimmu.2026.1787518
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00