ResearcherSecond Military Medical UniversityNo recent work listed
Jing Li
Matched on ORCID iD 0000-0001-6553-3914; OpenAlex author ID A5032719066. Sources: OpenAlex.
Their work
- Papers
- 5
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 15
2021: 3 papers · 2022: 1 paper · 2023: 1 paper
Papers 5
- 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↗
- 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↗
- 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↗
- 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↗
- 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 15 strongest of 15 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Yinghao Meng · 5 shared papers, 2021–2023 · strength 0.60
- Paper Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcin… DOI 10.1007/s00261-023-03801-8↗
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Yun Bian · 5 shared papers, 2021–2023 · strength 0.60
- Paper Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcin… DOI 10.1007/s00261-023-03801-8↗
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Jieyu Yu · 5 shared papers, 2021–2023 · strength 0.60
- Paper Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcin… DOI 10.1007/s00261-023-03801-8↗
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Chengwei Shao · 5 shared papers, 2021–2023 · strength 0.60
- Paper Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcin… DOI 10.1007/s00261-023-03801-8↗
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Jianping Lu · 4 shared papers, 2021–2022 · strength 0.45
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Paper XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients… DOI 10.3389/fonc.2021.671333↗
- Hui Jiang · 3 shared papers, 2021–2022 · strength 0.34
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Paper XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients… DOI 10.3389/fonc.2021.671333↗
- Li Wang · 3 shared papers, 2021 · strength 0.33
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Paper XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients… DOI 10.3389/fonc.2021.671333↗
- Paper CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarc… DOI 10.3389/fonc.2021.707288↗
- Qi Li · 2 shared papers, 2021–2023 · strength 0.25
- Paper Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcin… DOI 10.1007/s00261-023-03801-8↗
- Paper CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarc… DOI 10.3389/fonc.2021.707288↗
- Qi Li · 2 shared papers, 2021–2022 · strength 0.24
- Paper Prediction of Tumor-Infiltrating CD20+ B-Cells in Patients with Pancreatic Ductal Adenocarcinoma Using a Multilayer Per… DOI 10.1016/j.acra.2021.11.013↗
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Chao Ma · 2 shared papers, 2021 · strength 0.22
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Paper CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarc… DOI 10.3389/fonc.2021.707288↗
- Hao Zhang · 2 shared papers, 2021 · strength 0.22
- Paper XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients… DOI 10.3389/fonc.2021.671333↗
- Paper CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarc… DOI 10.3389/fonc.2021.707288↗
- Kai Cao · 2 shared papers, 2021 · strength 0.22
- Paper Noncontrast Magnetic Resonance Radiomics and Multilayer Perceptron Network Classifier: An approach for Predicting Fibro… DOI 10.1002/jmri.27648↗
- Paper XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients… DOI 10.3389/fonc.2021.671333↗
- Na Li · 1 shared paper, 2023 · strength 0.14
- Jianping Lu · 1 shared paper, 2023 · strength 0.14
- 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.