Pancreatic Cancer Research

ResearcherSecond Military Medical UniversityNo recent work listed

Yinghao Meng

Verified by OpenAlex author ID (good confidence, 0.85), identity_conf@1As of 7 November 2026

Matched on OpenAlex author ID A5050377845. Sources: OpenAlex.

Their work

Papers
12 (first author 1, last author 0)
Trials
0
Grants
0
Labs led
0
Collaborators
18
Papers, trials and grants by year

2021: 4 papers · 2022: 7 papers · 2023: 1 paper

Papers 12

  1. 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↗
  2. 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↗
  3. 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↗
  4. 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↗
  5. Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis. First author DOI 10.1007/s00330-022-08698-3↗
  6. 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↗
  7. 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↗
  8. 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↗
  9. 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↗
  10. 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↗
  11. 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↗
  12. 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 18 strongest of 18 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

  1. Yun Bian · 12 shared papers, 2021–2023 · strength 1.51
  2. Jieyu Yu · 12 shared papers, 2021–2023 · strength 1.51
  3. Chengwei Shao · 12 shared papers, 2021–2023 · strength 1.51
  4. Jianping Lu · 9 shared papers, 2021–2022 · strength 1.10
  5. Hui Jiang · 7 shared papers, 2021–2022 · strength 0.85
  6. Li Wang · 7 shared papers, 2021–2022 · strength 0.83
  7. Jing Li · 5 shared papers, 2021–2023 · strength 0.60
  8. Qi Li · 4 shared papers, 2021–2023 · strength 0.53
  9. Chao Ma · 4 shared papers, 2021–2022 · strength 0.48
  10. Kai Cao · 4 shared papers, 2021–2022 · strength 0.46
  11. Jianping Lu · 3 shared papers, 2022–2023 · strength 0.41
  12. Hao Zhang · 3 shared papers, 2021–2022 · strength 0.35
  13. Qi Li · 2 shared papers, 2021–2022 · strength 0.24
  14. Na Li · 1 shared paper, 2023 · strength 0.14
  15. Jing Li · 1 shared paper, 2022 · strength 0.13
  16. Jing Li · 1 shared paper, 2022 · strength 0.13
  17. Jing Li · 1 shared paper, 2022 · strength 0.13
  18. 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.