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

K Jinno^1,2, F Pfeffer^1,3, B S Nedrebø¹

¹Department of Gastrointestinal Surgery, Haukeland University Hospital, Bergen, Norway.

Techniques in Coloproctology

|June 26, 2026

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A new grading system estimates surgical difficulty in robot-assisted rectal resection. This tool uses preoperative data to help surgeons anticipate operative complexity and tailor their approach for better patient outcomes.

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.

Keywords:

Preoperative prediction Rectal cancer Robot-assisted surgery Surgical difficulty

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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.