Papers

3

Total Citations

12

H-Index

2

About

Lin Dong is a surgical researcher whose work centers on comparative effectiveness research in oncologic surgery, with a particular focus on urological and colorectal cancers. Through rigorous systematic reviews, meta-analyses, and Bayesian network analyses, Dong has made meaningful contributions to evidence-based surgical decision-making, helping clinicians navigate the complex landscape of competing operative approaches. Dong's most recognized work examines radical cystectomy for bladder cancer, systematically comparing open, laparoscopic, and robot-assisted techniques across both perioperative safety and long-term oncologic outcomes. These paired Bayesian network analyses — accumulating 6 and 5 citations respectively — represent a methodologically sophisticated effort to synthesize fragmented trial data into actionable clinical guidance at a time when robotic surgery was rapidly expanding in urological practice. By rigorously evaluating survival, recurrence, and complication profiles across all three modalities simultaneously, Dong's research helps surgeons and patients make more informed choices. More recently, Dong extended this comparative surgical methodology to colorectal oncology, investigating minimally invasive versus open proctectomy for locally advanced colon cancer — a historically underexplored area given conventional contraindications to minimally invasive approaches in this setting. Though an early-stage contribution, this work signals a broadening research agenda with significant potential clinical relevance for a challenging patient population.

Research Focus

Key Achievements

2
H-Index
3
Papers
12
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
Bayesian network analysis of open, laparoscopic, and robot-assisted radical cystectomy for bladder cancer
6 citations · 2020
📈 Most Prolific Year: 2020 (1 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: Affiliated Hospital of North Sichuan Medical College

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago