Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study
The application of weakly supervised deep learning to WSIs offers a powerful tool for predicting MPR in NSCLC patients treated with NICT.
Authors
- Shaoyu Hao
- Wei Huang (ORCID: https://orcid.org/0000-0002-8894-0982)
- Ran Wei (ORCID: https://orcid.org/0000-0003-4293-6686)
- Zhongtang Wang (ORCID: https://orcid.org/0000-0003-3153-6775)
- Shenbo Fu
- Dan Han (ORCID: https://orcid.org/0000-0003-0208-6904)
- Qian Zhao (ORCID: https://orcid.org/0009-0003-7108-9735)
- Hao Li (ORCID: https://orcid.org/0009-0008-2573-8032)
- Xin Zheng
- Chengxin Liu (ORCID: https://orcid.org/0000-0003-2962-0820)
Institutions
- Shandong University (CN)
- Shandong Tumor Hospital (CN)
- Shanxi Provincial Cancer Hospital (CN)
- Jining First People's Hospital (CN)
- Shandong Provincial QianFoShan Hospital (CN)
- Jimo District Qingdao Hospital of Traditional Chinese Medicine (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-09-20
- DOI
- https://doi.org/10.3389/fimmu.2024.1453232
- Citations
- 9
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.77
Funders
- Natural Science Foundation of Shandong Province