Comparison of Six Different Machine Learning Methods With Traditional Model for Low Anterior Resection Syndrome After Minimally Invasive Surgery for Rectal Cancer -- Development and External Validation of a Nomogram : A Dual-center Cohort Study

November 25, 2025 updated by: Daorong Wang, Northern Jiangsu People's Hospital

Following thorough screening based on inclusion and exclusion criteria, patients from the two sizable medical centers were split up into two cohorts for this study. Cohort 1 served primarily as the training and internal validation set, while Cohort 2 was used for external validation of the predictive model constructed from Cohort 1. We used six distinct machine learning methodss, including DT, RF, XGBOOST, SVM, lightGBM, and SHLNN, in addition to conventional logistic regression to create the predictive model. We chose the approach with the best sensitivity and specificity by comparing the concordance index(C-index) akin to the area under the ROC curve (AUC) of these seven distinct model-building methods. The predictive model for Cohort 1 was then built using this method, and internal validation was finished. Lastly, Cohort 2 underwent external validation of the predictive model

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

3500

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Child
Adult
Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

This retrospective analysis included 3,937 radical rectal cancer cases from two Chinese university hospitals (Northern Jiangsu People's Hospital 2015-2023, n=2612; Jilin University's China-Japan Union Hospital 2021-2023, n=1325), with rigorous selection criteria ensuring cohort homogeneity

Description

Inclusion Criteria:(1) rectal adenocarcinoma (2) minimally invasive sphincter-preserving surgery (taTME/ISR/LAR) (3) intact baseline anal function (4) no emergent presentations or metastases.

Exclusion Criteria:emergent presentations or metastases

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
low anterior resection syndrome Time Frame: 1 and 3 months after surgery		1 and 3 months after surgery
Comparison of Six Different Machine Learning Methods With Traditional Model for Low Anterior Resection Syndrome After Minimally Invasive Surgery for Rectal Cancer -- Development and External Validation of a Nomogram : A Dual-center Cohort Study Time Frame: 3 months	using LARS Score to assess the LARS situation	3 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Northern Jiangsu People's Hospital

Collaborators

China-Japan Union Hospital, Jilin University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

April 10, 2015

Primary Completion (Actual)

October 7, 2023

Study Completion (Actual)

June 20, 2024

Study Registration Dates

First Submitted

July 9, 2025

First Submitted That Met QC Criteria

November 25, 2025

First Posted (Actual)

December 5, 2025

Study Record Updates

Last Update Posted (Actual)