ResearcherWuhan Union HospitalActive in the last 2 years
Ping Han
Matched on ORCID iD 0000-0003-0758-6668; OpenAlex author ID A5101452005. Sources: OpenAlex.
Their work
- Papers
- 6 (first author 0, last author 3)
- Trials
- 0
- Grants
- 0
- Labs led
- 1
- Collaborators
- 11
2019: 1 paper · 2020: none · 2021: none · 2022: 1 paper · 2023: 1 paper · 2024: 1 paper · 2025: 1 paper · 2026: 1 paper
Papers 6
- Prognostic value of body composition on early recurrence and long-term survival of resectable pancreatic ductal adenocarcinoma. Co-author DOI 10.1007/s00330-025-12028-8↗
- Interpretable machine learning model for predicting early recurrence of pancreatic cancer: integrating intratumoral and peritumoral radiomics with body composi… Co-author DOI 10.1097/js9.0000000000003078↗
- A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma. Last author DOI 10.1186/s40644-024-00700-z↗
- Utility of Dark-Blood Dual-Energy CT Images for Predicting Vascular Involvement and R0 Resection in Patients With Pancreatic Cancer. Co-author DOI 10.2214/ajr.22.28640↗
- The Effects of Radiotherapy on Pancreatic Ductal Adenocarcinoma in Patients with Liver Metastases. Last author DOI 10.3390/curroncol29100625↗
- Identification of differentially expressed genes in pancreatic ductal adenocarcinoma and normal pancreatic tissues based on microarray datasets. Last author DOI 10.3892/mmr.2019.10414↗
Closest collaborators
The 11 strongest of 11 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Heshui Wu · 4 shared papers, 2023–2026 · strength 0.89
- Paper Prognostic value of body composition on early recurrence and long-term survival of resectable pancreatic ductal adenoca… DOI 10.1007/s00330-025-12028-8↗
- Paper Interpretable machine learning model for predicting early recurrence of pancreatic cancer: integrating intratumoral and… DOI 10.1097/js9.0000000000003078↗
- Paper A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal ade… DOI 10.1186/s40644-024-00700-z↗
- Chuansheng Zheng · 4 shared papers, 2022–2026 · strength 0.88
- Paper Prognostic value of body composition on early recurrence and long-term survival of resectable pancreatic ductal adenoca… DOI 10.1007/s00330-025-12028-8↗
- Paper Interpretable machine learning model for predicting early recurrence of pancreatic cancer: integrating intratumoral and… DOI 10.1097/js9.0000000000003078↗
- Paper A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal ade… DOI 10.1186/s40644-024-00700-z↗
- Linxia Wu · 4 shared papers, 2022–2026 · strength 0.88
- Paper Prognostic value of body composition on early recurrence and long-term survival of resectable pancreatic ductal adenoca… DOI 10.1007/s00330-025-12028-8↗
- Paper Interpretable machine learning model for predicting early recurrence of pancreatic cancer: integrating intratumoral and… DOI 10.1097/js9.0000000000003078↗
- Paper A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal ade… DOI 10.1186/s40644-024-00700-z↗
- Chunyuan Cen · 4 shared papers, 2019–2025 · strength 0.70
- Paper Interpretable machine learning model for predicting early recurrence of pancreatic cancer: integrating intratumoral and… DOI 10.1097/js9.0000000000003078↗
- Paper A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal ade… DOI 10.1186/s40644-024-00700-z↗
- Paper The Effects of Radiotherapy on Pancreatic Ductal Adenocarcinoma in Patients with Liver Metastases. DOI 10.3390/curroncol29100625↗
- Xin Li · 1 shared paper, 2025 · strength 0.24
- Keke Si · 1 shared paper, 2023 · strength 0.18
- Xin Li · 1 shared paper, 2023 · strength 0.18
- Jun Xue · 1 shared paper, 2023 · strength 0.18
- Ling Ma · 1 shared paper, 2019 · strength 0.09
- Siqi Wang · 1 shared paper, 2019 · strength 0.09
- Liying Liu · 1 shared paper, 2019 · strength 0.09
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.