Papers
1
Total Citations
4
H-Index
1
About
Haohao Wan is a surgical researcher whose work focuses on improving outcomes in gastric cancer treatment, particularly through the integration of robotic surgery and predictive analytics. Their most-cited study, "Construction and validation of a nomogram prediction model for the occurrence of complications in patients following robotic radical surgery for gastric cancer" (2025, 4 citations), represents a significant contribution to precision oncology. By developing and validating a clinical prediction tool, Wan has addressed a critical gap in postoperative care: the ability to anticipate complications after robotic gastrectomy. This nomogram model offers surgeons a practical, evidence-based method to stratify patient risk, enabling more personalized perioperative management and potentially reducing morbidity. While still early in their career, Wan’s work demonstrates a clear commitment to translating complex surgical data into actionable clinical tools. Their research sits at the intersection of minimally invasive surgery, biostatistics, and patient safety, reflecting a growing trend toward data-driven decision-making in oncology. As robotic surgery becomes more prevalent, Wan’s predictive framework could become a standard component of preoperative planning, helping to optimize recovery and resource allocation for gastric cancer patients worldwide.
Research Focus
Key Achievements
Top Papers
- 1