Papers

9

Total Citations

147

H-Index

7

About

Dacheng Jin is a prominent surgical researcher specializing in robotic-assisted thoracic surgery, with a particular focus on minimally invasive techniques for esophageal cancer, lung cancer, and mediastinal tumors. His work sits at the cutting edge of surgical innovation, systematically evaluating how robotic platforms — most notably the da Vinci surgical system — compare to conventional thoracoscopic approaches across a range of thoracic conditions. Jin's most impactful contribution is his 2019 meta-analysis and systematic review on robotic-assisted minimally invasive esophagectomy (RAMIE) versus conventional minimally invasive esophagectomy, which has accumulated 86 citations and remains a key reference for surgeons navigating this evolving field. Beyond esophageal surgery, he has made meaningful contributions evaluating RATS for posterior mediastinal neurogenic tumors, bronchial sleeve lobectomy, mediastinal cysts, and anterior mediastinal tumors, demonstrating consistently favorable perioperative outcomes for robotic approaches. His research extends to nuanced clinical questions, including the immunological and physiological impact of robotic versus thoracoscopic surgery on lung cancer patients, as well as comparative instrument efficacy. Collectively, Jin's body of work provides clinicians with rigorous, evidence-based guidance on integrating robotic surgery into thoracic practice, helping to define when and how these advanced technologies deliver meaningful patient benefit.

Research Focus

Key Achievements

7
H-Index
9
Papers
147
Total Citations
16
Avg Citations/Paper
🏆 Most Cited Paper
Robotic‐assisted minimally invasive esophagectomy versus the conventional minimally invasive one: A meta‐analysis and systematic review
86 citations · 2019
📈 Most Prolific Year: 2023 (6 Papers)
🤝 Key Collaborators: 27
🏛 Institutions: Gansu University of Traditional Chinese Medicine, Gansu Provincial Hospital

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago