Predicting anatomical difficulty in robot-assisted rectal resection using a novel anatomic landmark-based grading...

Area of Science:

• Robotic surgery
• Surgical oncology
• Anatomical landmark-based systems

Background:

• Robot-assisted rectal resection presents challenges in predicting surgical difficulty.
• Accurate preoperative assessment is crucial for surgical planning and patient outcomes.

Purpose of the Study:

• To develop and validate an anatomic landmark-based ordinal grading system for surgical difficulty in robot-assisted rectal resection.
• To assess the preoperative predictability of this novel grading system.

Main Methods:

• The procedure was divided into mesocolon and mesorectal dissection phases.
• An anatomy-based ordinal grading system (grades 1-3) was developed for each phase.
• Regression analysis and tenfold cross-validation were used with data from 158 patients.

Main Results:

• Mesocolic phase model (BMI, sex, lung disease, age) showed moderate predictive performance (AUCs 0.787-0.831).
• Mesorectal phase model (BMI, sex, neoadjuvant chemoradiotherapy) demonstrated good predictive performance (AUCs 0.850-0.927).
• A clinical calculator was developed for easy application of the grading system.

Conclusions:

• A novel, phase-specific grading system for robot-assisted rectal resection difficulty was developed.
• The system effectively estimates preoperative anatomical difficulty using standard patient data.
• This tool aids surgeons in anticipating operative complexity and personalizing surgical strategies.