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Updated: Jul 31, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning.

Kevin A Chen¹, Chinmaya U Joisa², Jonathan M Stem¹

¹Department of Surgery, University of North Carolina at Chapel Hill, NC, USA.

The American Surgeon

|May 3, 2023

Summary

This summary is machine-generated.

Machine learning models accurately predict ureteral injury (UI) during colorectal surgery, outperforming traditional methods. These advanced models can help decide on ureteral stent placement to prevent complications.

Keywords:

artificial intelligence colorectal surgery machine learning ureteral injury

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An Immature Murine Model of Reversible Unilateral Ureteral Obstruction

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Published on: April 4, 2025

Area of Science:

Surgical Outcomes
Predictive Analytics in Medicine
Machine Learning Applications

Background:

Ureteral injury (UI) is a rare but severe complication of colorectal surgery.
Ureteral stents can mitigate UI but have associated risks.
Identifying UI risk predictors is crucial for targeted stent use.

Purpose of the Study:

To develop and evaluate machine learning models for predicting UI during colorectal surgery.
To compare the accuracy of machine learning models against traditional logistic regression (LR).

Main Methods:

Utilized the National Surgical Quality Improvement Program (NSQIP) database for patient data.
Compared three machine learning approaches: random forest (RF), gradient boosting (XGB), and neural networks (NN).
Assessed model performance using the area under the receiver operating characteristic curve (AUROC).

Main Results:

The dataset comprised 262,923 patients, with 0.578% experiencing UI.
XGB achieved the highest AUROC of 0.774, significantly outperforming LR (0.738).
Procedure type, work RVUs, surgical indication, and bowel preparation were key predictors.

Conclusions:

Machine learning models demonstrate superior accuracy in predicting UI compared to LR.
These validated models can aid in preoperative decisions regarding ureteral stent placement.
Improved prediction of UI can enhance patient safety in colorectal surgery.