Pancreatic Cancer Research

ResearcherSecond Military Medical UniversityActive in the last 2 years

Chengwei Shao

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

Matched on ORCID iD 0000-0003-1640-9663; OpenAlex author ID A5026197259. Sources: OpenAlex.

Their work

Papers
27 (first author 0, last author 7)
Trials
0
Grants
0
Labs led
1
Collaborators
41
Papers, trials and grants by year

2016: 1 paper · 2017: 2 papers · 2018: 1 paper · 2019: none · 2020: none · 2021: 5 papers · 2022: 9 papers · 2023: 4 papers · 2024: 2 papers · 2025: 1 paper · 2026: 2 papers

Papers 27

  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. Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma. Co-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. D-A-D type based NIR fluorescence probe for monitoring the cysteine levels in pancreatic cancer cell during ferroptosis. Last author DOI 10.1016/j.bioorg.2024.107260↗
  5. Values of apparent diffusion coefficient in pancreatic cancer patients receiving neoadjuvant therapy. Co-author DOI 10.1186/s12885-024-12934-y↗
  6. 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↗
  7. Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma. Last author DOI 10.1148/radiol.220329↗
  8. 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↗
  9. 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↗
  10. 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↗
  11. 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↗
  12. CT radiomics signature: a potential biomarker for fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma. Co-author DOI 10.1007/s00261-022-03512-6↗
  13. 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↗
  14. 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↗
  15. Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma. Last author DOI 10.1016/j.acra.2021.08.013↗
  16. Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Perceptron Network Classifier Based on Non-… Co-author DOI 10.1016/j.acra.2021.11.013↗
  17. Machine Learning for Computed Tomography Radiomics: Prediction of Tumor-Infiltrating Lymphocytes in Patients With Pancreatic Ductal Adenocarcinoma. Last author DOI 10.1097/mpa.0000000000002069↗
  18. Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Co-author DOI 10.1186/s40644-021-00443-1↗
  19. Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibroblast Activation Protein Expression in P… Last author DOI 10.1002/jmri.27648↗
  20. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Last author DOI 10.1007/s00261-021-03159-9↗
  21. 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↗
  22. CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma. Last author DOI 10.3389/fonc.2021.707288↗
  23. 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↗
  24. Glypican-1-antibody-conjugated Gd-Au nanoclusters for FI/MRI dual-modal targeted detection of pancreatic cancer. Co-author DOI 10.2147/ijn.s158559↗
  25. Effect of region of interest size on ADC measurements in pancreatic adenocarcinoma. Co-author DOI 10.1186/s40644-017-0116-6↗
  26. Aberrant expression of STYK1 and E-cadherin confer a poor prognosis for pancreatic cancer patients. Co-author DOI 10.18632/oncotarget.22794↗
  27. In vitro and in vivo targeting imaging of pancreatic cancer using a Fe3O4@SiO2 nanoprobe modified with anti-mesothelin antibody. Co-author DOI 10.2147/ijn.s104501↗

Closest collaborators

The 25 strongest of 41 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Yun Bian · 24 shared papers, 2017–2026 · strength 3.88
  2. Jieyu Yu · 16 shared papers, 2021–2026 · strength 2.30
  3. Jianping Lu · 15 shared papers, 2017–2024 · strength 1.93
  4. Hui Jiang · 11 shared papers, 2021–2026 · strength 1.56
  5. Yinghao Meng · 12 shared papers, 2021–2023 · strength 1.51
  6. Jianping Lu · 8 shared papers, 2021–2026 · strength 1.35
  7. Li Wang · 10 shared papers, 2017–2025 · strength 1.23
  8. Chao Ma · 9 shared papers, 2017–2024 · strength 1.13
  9. Qi Li · 5 shared papers, 2021–2023 · strength 0.69
  10. Jing Li · 5 shared papers, 2021–2023 · strength 0.60
  11. Kai Cao · 5 shared papers, 2021–2022 · strength 0.60
  12. Yufei Chen · 2 shared papers, 2023–2024 · strength 0.39
  13. Panpan Yang · 2 shared papers, 2023–2024 · strength 0.39
  14. Fei Kong · 1 shared paper, 2026 · strength 0.36
  15. jingcan qin · 1 shared paper, 2026 · strength 0.36
  16. Hao Zhang · 3 shared papers, 2021–2022 · strength 0.35
  17. Jing Li · 2 shared papers, 2022–2025 · strength 0.34
  18. Luguang Chen · 3 shared papers, 2016–2024 · strength 0.34
  19. Jingcan Qin · 1 shared paper, 2024 · strength 0.31
  20. Jianming Zheng · 2 shared papers, 2021–2022 · strength 0.28
  21. Chengwei Chen · 1 shared paper, 2026 · strength 0.26
  22. Qi Li · 2 shared papers, 2021–2022 · strength 0.24
  23. Wenbin Liu · 1 shared paper, 2025 · strength 0.20
  24. Jie Fan · 1 shared paper, 2025 · strength 0.20
  25. Xinyue Zhang · 1 shared paper, 2025 · strength 0.20

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.