Papers

2

Total Citations

14

H-Index

2

About

Fabrizio Longo is a surgical researcher whose work centers on refining robot-assisted radical prostatectomy (RARP) and optimizing perioperative outcomes for prostate cancer patients. His primary contributions lie in standardizing surgical techniques and evaluating the learning curve’s impact on clinical variables. In his 2018 study on the role of the bed assistant during RARP, Longo demonstrated how team coordination and procedural experience directly reduce operative times and complications, earning 9 citations for its practical insights. More recently, his 2024 investigation into extended pelvic lymph node dissection (ePLND) compared antegrade versus retrograde approaches, providing crucial data on technique standardization and lymph node yield—a study already accruing 5 citations for its timely relevance. Longo’s work bridges the gap between surgical innovation and reproducible practice, emphasizing how meticulous procedural steps can enhance oncologic control while minimizing morbidity. His research is particularly valuable for urologists and trainees navigating the complexities of robotic surgery, offering evidence-based guidance to improve patient outcomes. With a focus on actionable, technique-driven improvements, Longo continues to shape the evolution of minimally invasive prostate cancer surgery.

Research Focus

Key Achievements

2
H-Index
2
Papers
14
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Role of Bed Assistant During Robot-assisted Radical Prostatectomy: The Effect of Learning Curve on Perioperative Variables
9 citations · 2018
📈 Most Prolific Year: 2018 (1 Papers)
🤝 Key Collaborators: 12
🏛 Institutions: Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago