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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 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 3500

Last updated 2025-12-05

No results posted yet for this study

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

Interventions

PROCEDURE

nCRT

neoadjuvant chemoradiotherapy

BEHAVIORAL

BMI

Body Mass Index

DIAGNOSTIC_TEST

Distance from AV

Distance from AV

PROCEDURE

Surgical type

laparoscopic and robotic surgery

PROCEDURE

Surgical approach

tatme + isr

PROCEDURE

LCA Preserving

LCA Preserving

PROCEDURE

Prophylactic stoma

Prophylactic stoma

PROCEDURE

Anastomotic leakage

Anastomotic leakage

Sponsors & Collaborators

  • China-Japan Union Hospital, Jilin University

    collaborator OTHER

  • Northern Jiangsu People's Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2015-04-10
Primary Completion
2023-10-07
Completion
2024-06-20

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Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07267767 on ClinicalTrials.gov