Pancreatic Cancer Research

ResearcherSecond Military Medical UniversityActive in the last 2 years

Changjing Zuo

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

Matched on ORCID iD 0000-0002-2051-766X; OpenAlex author ID A5101674981. Sources: OpenAlex.

Their work

Papers
19 (first author 0, last author 10)
Trials
0
Grants
0
Labs led
1
Collaborators
37
Papers, trials and grants by year

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

Papers 19

  1. Prognostic value of multiparameter [68Ga]Ga-DOTA-FAPI-04 PET/MR imaging biomarkers for patients with advanced pancreatic cancer. Last author DOI 10.1007/s00259-025-07491-w↗
  2. Research progress and trends on PET/CT imaging of pancreatic cancer: a bibliometric analysis. Co-author DOI 10.1007/s12672-026-04605-0↗
  3. Cerenkov radiation modulates the extracellular matrix for improved pancreatic cancer chemotherapy. Co-author DOI 10.1016/j.celbio.2025.100221↗
  4. Preoperative assessment of pancreatic cancer with [68Ga]Ga-DOTA-FAPI-04 PET/MR versus [18F]-FDG PET/CT plus contrast-enhanced CT: a prospective preliminary stu… Last author DOI 10.1007/s00259-024-06943-z↗
  5. Role of EFNAs in Shaping the Tumor Immune Microenvironment and Their Impact on Pancreatic Adenocarcinoma Prognosis. Last author DOI 10.2147/cmar.s502401↗
  6. Integrated 68 Ga-FAPI-04 PET/MR in Pancreatic Cancer : Prediction of Tumor Response and Tumor Resectability After Neoadjuvant Therapy. Last author DOI 10.1097/rlu.0000000000005300↗
  7. Autophagy inhibition improves the targeted radionuclide therapy efficacy of 131I-FAP-2286 in pancreatic cancer xenografts. Co-author DOI 10.1186/s12967-024-04958-6↗
  8. A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images. Last author DOI 10.1007/s11604-022-01363-1↗
  9. Comparison of the diagnostic efficacy of 68 Ga-FAPI-04 PET/MR and 18F-FDG PET/CT in patients with pancreatic cancer. Last author DOI 10.1007/s00259-022-05729-5↗
  10. Prognostic Evaluation Based on Dual-Time 18F-FDG PET/CT Radiomics Features in Patients with Locally Advanced Pancreatic Cancer Treated by Stereotactic Body Rad… Co-author DOI 10.1155/2022/6528865↗
  11. Prognostic evaluation based on dual time 18F-FDG PET/CT radiomics features in patients with locally advanced pancreatic cancer treated by Stereotactic Body Rad… Co-author DOI 10.21203/rs.3.rs-1357369/v2↗
  12. The exploration of quantitative intra-tumoral metabolic heterogeneity in dual-time 18F-FDG PET/CT of pancreatic cancer. Last author DOI 10.1007/s00261-021-03068-x↗
  13. Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis. Co-author DOI 10.1007/s00330-021-07778-0↗
  14. Preliminary study on SPECT/CT imaging of pancreatic cancer xenografts by targeting integrin α5 in pancreatic stellate cells. Last author DOI 10.7150/jca.51190↗
  15. Prognostic value of 18F-FDG PET /CT metabolic parameters in patients with locally advanced pancreatic Cancer treated with stereotactic body radiation therapy. Co-author DOI 10.1186/s40644-020-00301-6↗
  16. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18 F-FDG PET/CT. Co-author DOI 10.1002/mp.13733↗
  17. Prognostic values of 18F-FDG PET/CT metabolic parameters and clinical figures in locally advanced pancreatic cancer underwent chemotherapy combined with stereo… Last author DOI 10.1097/md.0000000000015064↗
  18. [Differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma based on multi-modality texture features in 18F-FDG PET/CT]. Last author DOI 10.7507/1001-5515.201807012↗
  19. 18F- FDG PET/CT helps differentiate autoimmune pancreatitis from pancreatic cancer. Co-author DOI 10.1186/s12885-017-3665-y↗

Closest collaborators

The 25 strongest of 37 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Chao Cheng · 10 shared papers, 2019–2026 · strength 1.74
  2. Zhaobang Liu · 6 shared papers, 2019–2023 · strength 0.84
  3. Tao Wang · 4 shared papers, 2021–2026 · strength 0.83
  4. Weiwei Su · 3 shared papers, 2025–2026 · strength 0.80
  5. Shiwei Guo · 3 shared papers, 2024–2026 · strength 0.75
  6. Gang Jin · 3 shared papers, 2024–2026 · strength 0.75
  7. Guorong Jia · 4 shared papers, 2017–2023 · strength 0.59
  8. Yu-Chao Li · 2 shared papers, 2025–2026 · strength 0.55
  9. Shengnan Ren · 4 shared papers, 2019–2022 · strength 0.52
  10. Zeyu Zhang · 2 shared papers, 2025–2026 · strength 0.51
  11. Xiaodong Yang · 4 shared papers, 2019–2022 · strength 0.48
  12. Kai Cao · 2 shared papers, 2022–2024 · strength 0.39
  13. Gaofeng Sun · 3 shared papers, 2019–2021 · strength 0.33
  14. Juanli Mao · 1 shared paper, 2026 · strength 0.32
  15. Xiaojiao Wang · 1 shared paper, 2026 · strength 0.32
  16. Dalong Ni · 1 shared paper, 2026 · strength 0.29
  17. Weibo Cai · 1 shared paper, 2026 · strength 0.29
  18. Xiao Li · 2 shared papers, 2019–2021 · strength 0.27
  19. Haojun Chen · 1 shared paper, 2026 · strength 0.26
  20. Zeyu Zhang · 1 shared paper, 2024 · strength 0.24
  21. Anyu Zhang · 2 shared papers, 2019–2020 · strength 0.24
  22. Xiaofei Zhu · 2 shared papers, 2019–2020 · strength 0.24
  23. Jianming Cai · 1 shared paper, 2024 · strength 0.23
  24. Xingyu Liu · 1 shared paper, 2024 · strength 0.23
  25. Wenting Wei · 1 shared paper, 2023 · strength 0.18

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.