ResearcherFudan UniversityActive in the last 2 years
Jia-Hao Chen
Matched on ORCID iD 0009-0005-8699-9373; OpenAlex author ID A5120183200. Sources: OpenAlex.
Their work
- Papers
- 2 (first author 1, last author 0)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 5
2025: 2 papers
Papers 2
- CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resectable pancreatic ductal adenocarcinoma. Co-author DOI 10.1007/s00261-025-04798-y↗
- Predictors for low rates of surgical resection in elderly patients with resectable pancreatic ductal adenocarcinoma: a retrospective case-control study. First author DOI 10.1097/js9.0000000000001957↗
Closest collaborators
The 5 strongest of 5 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Chunjing Li · 2 shared papers, 2025 · strength 0.53
- Paper Predictors for low rates of surgical resection in elderly patients with resectable pancreatic ductal adenocarcinoma: a … DOI 10.1097/js9.0000000000001957↗
- Paper CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resec… DOI 10.1007/s00261-025-04798-y↗
- Hong‐Fei Yao · 2 shared papers, 2025 · strength 0.53
- Paper Predictors for low rates of surgical resection in elderly patients with resectable pancreatic ductal adenocarcinoma: a … DOI 10.1097/js9.0000000000001957↗
- Paper CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resec… DOI 10.1007/s00261-025-04798-y↗
- Chongyi Jiang · 2 shared papers, 2025 · strength 0.53
- Paper Predictors for low rates of surgical resection in elderly patients with resectable pancreatic ductal adenocarcinoma: a … DOI 10.1097/js9.0000000000001957↗
- Paper CT-based machine learning radiomics predicts Ki-67 expression level and its relationship with overall survival in resec… DOI 10.1007/s00261-025-04798-y↗
- Zhiwei Cai · 1 shared paper, 2025 · strength 0.28
- Ming Li · 1 shared paper, 2025 · strength 0.25
How to reach this person
A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.