Pancreatic Cancer Research

ResearcherSecond Military Medical UniversityActive in the last 2 years

Yun Bian

ORCID-verified (high confidence, 0.95), identity_conf@1As of 7 November 2026

Matched on ORCID iD 0000-0002-4863-4956; OpenAlex author ID A5031808825, A5127891136. Sources: OpenAlex.

Their work

Papers
31 (first author 0, last author 11)
Trials
0
Grants
0
Labs led
1
Collaborators
42
Papers, trials and grants by year

2017: 2 papers · 2018: none · 2019: 2 papers · 2020: 1 paper · 2021: 6 papers · 2022: 9 papers · 2023: 4 papers · 2024: 3 papers · 2025: 1 paper · 2026: 3 papers

Papers 31

  1. Simultaneous detection of H₂S and viscosity in pancreatic cancer with a lysosome-targeted NIR fluorescent probe. Co-author DOI 10.1016/j.bioorg.2026.109724↗
  2. Routine H&E Histology Encodes Postoperative Risk Heterogeneity in Resected Pancreatic Ductal Adenocarcinoma: A Multicohort Evaluation of TIGERS. Last author DOI 10.1016/j.modpat.2026.101099↗
  3. Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma. Last author DOI 10.1148/ryai.260357↗
  4. A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma. Last author DOI 10.1097/mpa.0000000000002463↗
  5. D-A-D type based NIR fluorescence probe for monitoring the cysteine levels in pancreatic cancer cell during ferroptosis. Co-author DOI 10.1016/j.bioorg.2024.107260↗
  6. Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma. Last author DOI 10.1097/js9.0000000000001604↗
  7. Values of apparent diffusion coefficient in pancreatic cancer patients receiving neoadjuvant therapy. Co-author DOI 10.1186/s12885-024-12934-y↗
  8. Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinom… Last author DOI 10.1007/s00261-023-03801-8↗
  9. Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1148/radiol.220329↗
  10. Tumor size measurements of pancreatic cancer with neoadjuvant therapy based on RECIST guidelines: is MRI as effective as CT? Co-author DOI 10.1186/s40644-023-00528-z↗
  11. Non-enhanced magnetic resonance imaging-based radiomics model for the differentiation of pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcino… Last author DOI 10.3389/fonc.2023.1108545↗
  12. 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↗
  13. 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↗
  14. CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. Last author DOI 10.1007/s00261-022-03512-6↗
  15. Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis. Last author DOI 10.1007/s00330-022-08698-3↗
  16. CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis. Last author DOI 10.1016/j.acra.2021.04.014↗
  17. 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↗
  18. Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-… Last author DOI 10.1016/j.acra.2021.11.013↗
  19. Machine Learning for Computed Tomography Radiomics: Prediction of Tumor-Infiltrating Lymphocytes in Patients With Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1097/mpa.0000000000002069↗
  20. Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Co-author DOI 10.1186/s40644-021-00443-1↗
  21. 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↗
  22. 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↗
  23. The relationship between microscopic tumor size and CT tumor size in pancreatic ductal adenocarcinoma. Co-author DOI 10.1016/j.clinimag.2020.11.039↗
  24. XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma. Last author DOI 10.3389/fonc.2021.671333↗
  25. 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↗
  26. Tumor Budding Score Is a Strong and Independent Prognostic Factor in Patients With Pancreatic Ductal Adenocarcinoma: An Evaluation of Whole Slide Pathology Ima… Co-author DOI 10.3389/fonc.2021.740212↗
  27. Pancreatic adenocarcinoma: variability in measurements of tumor size among computed tomography, magnetic resonance imaging, and pathologic specimens. Co-author DOI 10.1007/s00261-019-02125-w↗
  28. Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1097/mpa.0000000000001404↗
  29. Comparisons of different neoadjuvant chemotherapy regimens with or without stereotactic body radiation therapy for borderline resectable pancreatic cancer: stu… Co-author DOI 10.1186/s13014-019-1254-8↗
  30. Aberrant expression of STYK1 and E-cadherin confer a poor prognosis for pancreatic cancer patients. Co-author DOI 10.18632/oncotarget.22794↗
  31. [Analysis on the clinical therapeutic effects of arterial first approach pancreatoduodenectomy in the treatment of borderline resectable pancreatic adenocarcin… Co-author DOI 10.3760/cma.j.issn.0529-5815.2017.12.007↗

Closest collaborators

The 25 strongest of 42 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Chengwei Shao · 24 shared papers, 2017–2026 · strength 3.88
  2. Jieyu Yu · 18 shared papers, 2021–2026 · strength 2.73
  3. Jianping Lu · 16 shared papers, 2017–2024 · strength 2.14
  4. Hui Jiang · 14 shared papers, 2019–2026 · strength 1.88
  5. Li Wang · 12 shared papers, 2019–2025 · strength 1.57
  6. Yinghao Meng · 12 shared papers, 2021–2023 · strength 1.51
  7. Jianping Lu · 9 shared papers, 2019–2026 · strength 1.43
  8. Chao Ma · 10 shared papers, 2017–2024 · strength 1.35
  9. Kai Cao · 8 shared papers, 2019–2022 · strength 0.91
  10. Qi Li · 5 shared papers, 2021–2023 · strength 0.69
  11. Jing Li · 5 shared papers, 2021–2023 · strength 0.60
  12. Panpan Yang · 3 shared papers, 2020–2024 · strength 0.52
  13. Yufei Chen · 2 shared papers, 2023–2024 · strength 0.39
  14. Gang Jin · 4 shared papers, 2017–2022 · strength 0.38
  15. Jianming Zheng · 3 shared papers, 2019–2022 · strength 0.37
  16. Fei Kong · 1 shared paper, 2026 · strength 0.36
  17. jingcan qin · 1 shared paper, 2026 · strength 0.36
  18. Hao Zhang · 3 shared papers, 2021–2022 · strength 0.35
  19. Jing Li · 2 shared papers, 2022–2025 · strength 0.34
  20. Suizhi Gao · 3 shared papers, 2019–2022 · strength 0.31
  21. Jingcan Qin · 1 shared paper, 2024 · strength 0.31
  22. Luguang Chen · 2 shared papers, 2017–2024 · strength 0.28
  23. Chengwei Chen · 1 shared paper, 2026 · strength 0.26
  24. Yixuan Shen · 1 shared paper, 2026 · strength 0.24
  25. Qi Li · 2 shared papers, 2021–2022 · strength 0.24

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.