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

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: Rectal Cancer; LARS - Low Anterior Resection Syndrome
• Intervention / Treatment: Procedure: nCRT; Behavioral: BMI; Diagnostic test: Distance from AV; Procedure: Surgical type; Procedure: Surgical approach; Procedure: LCA Preserving; Procedure: Prophylactic stoma; Procedure: Anastomotic leakage

• Study Type: Observational
• Enrollment (Actual): 3500

Participation Criteria

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.

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Exclusion Criteria:emergent presentations or metastases

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Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
low anterior resection syndrome		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	using LARS Score to assess the LARS situation	3 months

Collaborators and Investigators

• Sponsor: Northern Jiangsu People's Hospital
• Collaborators: China-Japan Union Hospital, Jilin University

Study record dates

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): December 5, 2025
• Last Update Submitted That Met QC Criteria: November 25, 2025
• Last Verified: November 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Postoperative Complications; Pathologic Processes; Neoplasms by Site; Neoplasms; Intestinal Diseases; Gastrointestinal Neoplasms; Digestive System Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colorectal Neoplasms; Intestinal Neoplasms; Rectal Diseases; Colonic Diseases; Pathological Conditions, Signs and Symptoms; Low Anterior Resection Syndrome; Rectal Neoplasms
• Other Study ID Numbers: jiangsuNorthen20