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Beyond Pathology: A Procedure-Based Approach to Planning and Predicting Outcomes in Robotic Gynaecological Oncology
Mohamed Abdelwanis Mohamed Abdelaziz1, Ayodele Olaleye2, Khaled Sabrah3
1Department of Gynaecological oncology, Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, NG5 1PB, UK. mohamedwanis15@gmail.com.
BMC Surgery
|October 29, 2025
Summary
Procedural requirements, not pathology, better predict operating time in robotic gynaecology. This study introduces a new framework for surgical planning, showing improved efficiency and safety in complex cases.
Area of Science:
- Robotic Gynaecological Surgery
- Surgical Planning
- Machine Learning in Medicine
Background:
- Current robotic gynaecological surgery planning relies on pathology, but operating theatre use may be better predicted by procedural needs.
- Advances in machine learning for surgical prediction necessitate more accurate planning models and challenge existing complexity assumptions.
Purpose of the Study:
- To compare procedural requirements against traditional complexity markers for predicting operating time and complications in robotic gynaecological surgery.
- To develop a practical, procedure-based surgical planning framework to complement machine learning approaches.
Main Methods:
- Retrospective analysis of 80 robotic gynaecological surgeries (2021-2024) at a single tertiary centre.
- Examined relationships between procedural requirements (lymphadenectomy, adhesiolysis), traditional complexity markers (BMI, pathology, prior surgery), and outcomes (operating time, complications).
- Developed and validated a Preoperative Procedural Demand Score (PPDS) against pathology-based predictions using multivariable regression analysis.
Main Results:
- Procedural requirements were stronger predictors of operating time than pathology type.
- Lymphadenectomy and adhesiolysis significantly increased operating time (33.1 min and 19.8 min, respectively).
- Traditional markers like BMI > 35 and previous surgery had minimal impact on operating time. Operating time and intraoperative complications significantly decreased over the study period.
Conclusions:
- Procedural requirements offer a novel, superior framework for predicting operating time in robotic gynaecological surgery compared to traditional markers.
- While promising, the single-centre study necessitates multi-centre prospective validation for widespread adoption, especially given modest predictive accuracy versus machine learning.
- Robotic surgery demonstrates feasibility and an excellent safety profile even in complex cases, warranting further investigation in larger studies.
