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NCT Number: NCT07267767

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

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Key information

Conditions

Rectal Cancer Colonic Diseases Colorectal Neoplasms Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms LARS - Low Anterior Resection Syndrome Low Anterior Resection Syndrome Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Postoperative Complications Rectal Diseases Rectal Neoplasms

Sex eligibility

All sexes

Study type

Observational

Who can participate

Only the study team can determine whether someone qualifies for participation.

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

-

Treatment and study plan

nCRT

Procedure

neoadjuvant chemoradiotherapy

BMI

Behavioral

Body Mass Index

Distance from AV

Diagnostic Test

Distance from AV

Surgical type

Procedure

laparoscopic and robotic surgery

Surgical approach

Procedure

tatme + isr

LCA Preserving

Procedure

LCA Preserving

Prophylactic stoma

Procedure

Prophylactic stoma

Anastomotic leakage

Procedure

Anastomotic leakage

Primary outcomes

  1. low anterior resection syndrome

    Time frame: 1 and 3 months after surgery

  2. 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

Sponsors and collaborators

Lead sponsor

Northern Jiangsu People's Hospital

Other

Collaborators

  • China-Japan Union Hospital, Jilin University

Registry information

Important dates

Study start
2015
Primary completion
2023
Study completion
2024
First posted
Dec 5, 2025
Registry last updated
Dec 5, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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