Pancreatic Cancer Research

ResearcherSecond Military Medical UniversityActive in the last 2 years

Jieyu Yu

ORCID-verified (high confidence, 0.95), identity_conf@1As of 8 November 2026

Matched on ORCID iD 0000-0002-8701-7440; OpenAlex author ID A5055584854. Sources: OpenAlex.

Their work

Papers
18 (first author 3, last author 0)
Trials
0
Grants
0
Labs led
0
Collaborators
25
Papers, trials and grants by year

2021: 4 papers · 2022: 7 papers · 2023: 3 papers · 2024: 1 paper · 2025: 1 paper · 2026: 2 papers

Papers 18

  1. Routine H&E Histology Encodes Postoperative Risk Heterogeneity in Resected Pancreatic Ductal Adenocarcinoma: A Multicohort Evaluation of TIGERS. Co-author DOI 10.1016/j.modpat.2026.101099↗
  2. Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma. First author DOI 10.1148/ryai.260357↗
  3. A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1097/mpa.0000000000002463↗
  4. Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma. Co-author DOI 10.1097/js9.0000000000001604↗
  5. Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinom… Co-author DOI 10.1007/s00261-023-03801-8↗
  6. Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1148/radiol.220329↗
  7. Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcino… Co-author DOI 10.3389/fonc.2023.1108545↗
  8. Preoperative Radiomics Approach to Evaluating Tumor-Infiltrating CD8+ T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Reso… Co-author DOI 10.1002/jmri.27871↗
  9. Contrast-enhanced computed tomography radiomics and multilayer perceptron network classifier: an approach for predicting CD20+ B cells in patients with pancrea… Co-author DOI 10.1007/s00261-021-03285-4↗
  10. CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. First author DOI 10.1007/s00261-022-03512-6↗
  11. Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis. Co-author DOI 10.1007/s00330-022-08698-3↗
  12. CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis. Co-author DOI 10.1016/j.acra.2021.04.014↗
  13. Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1016/j.acra.2021.08.013↗
  14. Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-… First author DOI 10.1016/j.acra.2021.11.013↗
  15. Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in P… Co-author DOI 10.1002/jmri.27648↗
  16. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Co-author DOI 10.1007/s00261-021-03159-9↗
  17. XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.3389/fonc.2021.671333↗
  18. CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.3389/fonc.2021.707288↗

Closest collaborators

The 25 strongest of 25 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Yun Bian · 18 shared papers, 2021–2026 · strength 2.73
  2. Chengwei Shao · 16 shared papers, 2021–2026 · strength 2.30
  3. Yinghao Meng · 12 shared papers, 2021–2023 · strength 1.51
  4. Hui Jiang · 9 shared papers, 2021–2026 · strength 1.28
  5. Li Wang · 9 shared papers, 2021–2025 · strength 1.22
  6. Jianping Lu · 7 shared papers, 2022–2026 · strength 1.20
  7. Jianping Lu · 9 shared papers, 2021–2022 · strength 1.10
  8. Qi Li · 5 shared papers, 2021–2023 · strength 0.69
  9. Jing Li · 5 shared papers, 2021–2023 · strength 0.60
  10. Chao Ma · 4 shared papers, 2021–2022 · strength 0.48
  11. Kai Cao · 4 shared papers, 2021–2022 · strength 0.46
  12. Hao Zhang · 3 shared papers, 2021–2022 · strength 0.35
  13. Jing Li · 2 shared papers, 2022–2025 · strength 0.34
  14. Chengwei Chen · 1 shared paper, 2026 · strength 0.26
  15. Yixuan Shen · 1 shared paper, 2026 · strength 0.24
  16. Qi Li · 2 shared papers, 2021–2022 · strength 0.24
  17. Wenbin Liu · 1 shared paper, 2025 · strength 0.20
  18. Jie Fan · 1 shared paper, 2025 · strength 0.20
  19. Xinyue Zhang · 1 shared paper, 2025 · strength 0.20
  20. Jianping Lu · 1 shared paper, 2024 · strength 0.19
  21. Ling Zhang · 1 shared paper, 2023 · strength 0.17
  22. Na Li · 1 shared paper, 2023 · strength 0.14
  23. Jing Li · 1 shared paper, 2022 · strength 0.13
  24. Jing Li · 1 shared paper, 2022 · strength 0.13
  25. Hao Zhang · 1 shared paper, 2022 · strength 0.13

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.