Pancreatic Cancer Research

ResearcherNational Institutes of HealthActive in the last 2 years

Li Wang

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

Matched on ORCID iD 0000-0003-3722-5986; OpenAlex author ID A5107889976, A5116736541. Sources: OpenAlex.

Their work

Papers
29 (first author 3, last author 1)
Trials
0
Grants
0
Labs led
0
Collaborators
74
Papers, trials and grants by year

2015: 3 papers · 2016: 2 papers · 2017: 5 papers · 2018: 1 paper · 2019: 1 paper · 2020: 3 papers · 2021: 5 papers · 2022: 5 papers · 2023: 1 paper · 2024: 1 paper · 2025: 2 papers

Papers 29

  1. Author Correction: Exome-wide analysis identifies three low-frequency missense variants associated with pancreatic cancer risk in Chinese populations. Co-author DOI 10.1038/s41467-025-58613-9↗
  2. A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1097/mpa.0000000000002463↗
  3. Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma. Co-author DOI 10.1097/js9.0000000000001604↗
  4. Integrative Analysis of PAIP2B to Identify a Novel Biomarker for Pancreatic Ductal Adenocarcinoma. First author DOI 10.1055/s-0043-1777789↗
  5. 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↗
  6. 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↗
  7. 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↗
  8. Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma. Co-author DOI 10.1186/s40644-021-00443-1↗
  9. Adjusting CA19-9 values with clinical stage and bilirubin to better predict survival of resectable pancreatic cancer patients: 5-year-follow-up of a single cen… Co-author DOI 10.3389/fonc.2022.966256↗
  10. 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↗
  11. 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↗
  12. A Transcriptionally Distinct Subpopulation of Healthy Acinar Cells Exhibit Features of Pancreatic Progenitors and PDAC. Co-author DOI 10.1158/0008-5472.can-21-0427↗
  13. 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↗
  14. 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↗
  15. 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↗
  16. Early recurrence detected by 18F-FDG PET/CT in patients with resected pancreatic ductal adenocarcinoma. Co-author DOI 10.1097/md.0000000000019504↗
  17. Enhancement of Antitumor Efficacy of Paclitaxel-Loaded PEGylated Liposomes by N,N-Dimethyl Tertiary Amino Moiety in Pancreatic Cancer. First author DOI 10.2147/dddt.s261017↗
  18. Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma. Co-author DOI 10.1097/mpa.0000000000001404↗
  19. Exome-wide analysis identifies three low-frequency missense variants associated with pancreatic cancer risk in Chinese populations. Co-author DOI 10.1038/s41467-018-06136-x↗
  20. Asporin promotes pancreatic cancer cell invasion and migration by regulating the epithelial-to-mesenchymal transition (EMT) through both autocrine and paracrin… Co-author DOI 10.1016/j.canlet.2017.04.001↗
  21. Surgeons' knowledge regarding the diagnosis and management of pancreatic cancer in China: a cross-sectional study. First author DOI 10.1186/s12913-017-2345-6↗
  22. Expression of signaling adaptor proteins predicts poor prognosis in pancreatic ductal adenocarcinoma. Co-author DOI 10.1186/s13000-017-0633-4↗
  23. Effect of region of interest size on ADC measurements in pancreatic adenocarcinoma. Co-author DOI 10.1186/s40644-017-0116-6↗
  24. Optimized ROI size on ADC measurements of normal pancreas, pancreatic cancer and mass-forming chronic pancreatitis. Co-author DOI 10.18632/oncotarget.18457↗
  25. Apparent diffusion coefficient (ADC) measurements in pancreatic adenocarcinoma: A preliminary study of the effect of region of interest on ADC values and inter… Co-author DOI 10.1002/jmri.25007↗
  26. Expression of amphiregulin predicts poor outcome in patients with pancreatic ductal adenocarcinoma. Co-author DOI 10.1186/s13000-016-0512-4↗
  27. Comparison of the Diagnostic Performances of Three Techniques of ROI Placement for ADC Measurements in Pancreatic Adenocarcinoma. Co-author DOI 10.1016/j.acra.2015.06.017↗
  28. Interaction of Serum microRNAs and Serum Folate With the Susceptibility to Pancreatic Cancer. Last author DOI 10.1097/mpa.0000000000000204↗
  29. Paracrine SDF-1α signaling mediates the effects of PSCs on GEM chemoresistance through an IL-6 autocrine loop in pancreatic cancer cells. Co-author DOI 10.18632/oncotarget.3099↗

Closest collaborators

The 25 strongest of 74 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Yun Bian · 12 shared papers, 2019–2025 · strength 1.57
  2. Chengwei Shao · 10 shared papers, 2017–2025 · strength 1.23
  3. Jieyu Yu · 9 shared papers, 2021–2025 · strength 1.22
  4. Jianping Lu · 10 shared papers, 2015–2022 · strength 1.08
  5. Chao Ma · 9 shared papers, 2015–2022 · strength 0.86
  6. Yinghao Meng · 7 shared papers, 2021–2022 · strength 0.83
  7. Hui Jiang · 7 shared papers, 2019–2022 · strength 0.81
  8. Kai Cao · 6 shared papers, 2019–2022 · strength 0.68
  9. Jianping Lu · 3 shared papers, 2019–2025 · strength 0.42
  10. Jing Li · 3 shared papers, 2021 · strength 0.33
  11. Li Mao · 2 shared papers, 2020–2022 · strength 0.29
  12. Bole Tian · 2 shared papers, 2020–2022 · strength 0.29
  13. Chao Wu · 2 shared papers, 2020–2022 · strength 0.28
  14. Huanwen Wu · 4 shared papers, 2015–2017 · strength 0.27
  15. Zhiyong Liang · 4 shared papers, 2015–2017 · strength 0.27
  16. Jianping Lu · 2 shared papers, 2017–2024 · strength 0.25
  17. Ying Zhu · 2 shared papers, 2018–2025 · strength 0.24
  18. Jianbo Tian · 2 shared papers, 2018–2025 · strength 0.24
  19. Kun Huang · 2 shared papers, 2018–2025 · strength 0.24
  20. Rong Zhong · 2 shared papers, 2018–2025 · strength 0.24
  21. Xiaoping Miao · 2 shared papers, 2018–2025 · strength 0.24
  22. Ming Yang · 2 shared papers, 2018–2025 · strength 0.24
  23. Juntao Ke · 2 shared papers, 2018–2025 · strength 0.24
  24. Chen Hsuan Wu · 2 shared papers, 2018–2025 · strength 0.24
  25. Beibei Zhu · 2 shared papers, 2018–2025 · 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.