ResearcherWest China Hospital of Sichuan UniversityActive in the last 2 years

Yuqi Wang

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

Matched on ORCID iD 0000-0002-9793-6002; OpenAlex author ID A5100451687. Sources: OpenAlex.

Their work

Papers: 2
Trials: 0
Grants: 0
Labs led: 0
Collaborators: 5

Papers, trials and grants by year

2026: 2 papers

Papers 2

Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multicenter study with prospective validation … Co-author DOI 10.1136/jitc-2026-015705 ↗
Comparative efficacy and safety of NALIRIFOX versus other first-line chemotherapy regimens in metastatic pancreatic ductal adenocarcinoma: a retrospective stud… Co-author DOI 10.1177/17588359261449092 ↗

Closest collaborators

The 5 strongest of 5 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

Dawei Chen · 1 shared paper, 2026 · strength 0.36
- Paper Comparative efficacy and safety of NALIRIFOX versus other first-line chemotherapy regimens in metastatic pancreatic duc… DOI 10.1177/17588359261449092 ↗
Xiaoying Li · 1 shared paper, 2026 · strength 0.30
- Paper Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multic… DOI 10.1136/jitc-2026-015705 ↗
Yu Li · 1 shared paper, 2026 · strength 0.30
- Paper Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multic… DOI 10.1136/jitc-2026-015705 ↗
Haopeng Yu · 1 shared paper, 2026 · strength 0.30
- Paper Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multic… DOI 10.1136/jitc-2026-015705 ↗
Dan Cao · 1 shared paper, 2026 · strength 0.30
- Paper Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multic… DOI 10.1136/jitc-2026-015705 ↗

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.