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

NCT07267767 | Completed

• 1 other identifier: jiangsuNorthen20
• Study Type: observational
• Target: 3,500
• Locations: 0 countries
• Sites: N/A

Brief Summary

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

Conditions

Rectal Cancer; LARS - Low Anterior Resection Syndrome

Outcome Measures

Primary Outcomes (2)

• 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

Interventions

BMI BEHAVIORAL

Body Mass Index

Surgical type PROCEDURE

laparoscopic and robotic surgery

Surgical approach PROCEDURE

tatme + isr

LCA Preserving PROCEDURE

LCA Preserving

nCRT PROCEDURE

neoadjuvant chemoradiotherapy

Distance from AV DIAGNOSTIC_TEST

Distance from AV

Prophylactic stoma PROCEDURE

Prophylactic stoma

Anastomotic leakage PROCEDURE

Anastomotic leakage

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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

Sponsors & Collaborators

• China-Japan Union Hospital, Jilin University: collaborator
• Northern Jiangsu People's Hospital: lead

MeSH Terms

Conditions

Rectal Neoplasms; Low Anterior Resection Syndrome

Condition Hierarchy (Ancestors)

Colorectal Neoplasms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Intestinal Diseases; Rectal Diseases; Colonic Diseases; Postoperative Complications; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Target Duration: 15 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: NANJING UNIVERSITY

Study Record Dates

• First Submitted: July 9, 2025
• First Posted: December 5, 2025
• Study Start: April 10, 2015
• Primary Completion: October 7, 2023
• Study Completion: June 20, 2024
• Last Updated: December 5, 2025

Record last verified: 2025-11