Establishment of a Feasibility Model for NOSE Surgery Based on Machine Learning

April 3, 2023 updated by: Yanxin Luo,MD, Sixth Affiliated Hospital, Sun Yat-sen University

Establishment of a Feasibility Model for Predicting Natural Orifice Specimen Extraction Surgery (NOSES) Based on Machine Learning.

The goal of this observational study is to test in patients with resectable rectosigmoid cancers. The main question it aims to answer is establishment of a feasibility model for predicting natural orifice specimen extraction surgery (NOSES) based on machine learning.

Study Overview

Study Type

Observational

Enrollment (Anticipated)

460

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

    • Guangdong
      • GuangZhou, Guangdong, China
        • The Sixth Affiliate Hospital of Sun Yat-Sen University
        • Contact:

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

18 years to 80 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients diagnosed with resectable rectosigmoid cancer.

Description

Inclusion Criteria:

  1. Patients diagnosed with colorectal cancer or large adenoma who are suitable for laparoscopic colorectal surgery;
  2. Tumor staging ≤ T3 without invasion of surrounding organs;
  3. No abdominal seeding or distant organ metastasis;
  4. Clear and complete imaging data (CT, pelvic MRI) that can be processed by a computer;
  5. Feasible evaluation and determination for obtaining specimens through the rectal channel during preoperative and intraoperative assessments.

Exclusion Criteria:

  1. Contraindications for laparoscopic colorectal surgery;
  2. Tumor staging is T4, or there are cancer nodules;
  3. Presence of metastasis or distant organ metastasis;
  4. Incomplete imaging data;
  5. Preoperative intestinal obstruction;
  6. Tumor or specimen diameter larger than the transverse diameter of the pelvic outlet;
  7. Previous rectal radiotherapy;
  8. Unsuitable evaluation and determination for obtaining specimens through the rectal channel during preoperative and intraoperative assessments.

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training set
The training set is a dataset used to train the model, which includes randomly enrolled patients with colon and rectal cancer. The inputs include data such as gender, age, height, weight, BMI, tumor stage, tumor pathology type, and the output information is whether NOSES surgery was successful or not. During training, the model learns from this dataset to make predictions on whether new patients with colon and rectal cancer can undergo NOSES surgery successfully.
Natural Orifice Specimen Extraction Surgery (NOSES) is a minimally invasive surgical technique that aims to reduce the size and number of incisions required during certain surgeries. In NOSES, the surgical specimen (such as a diseased organ or tumor) is removed from the body through a natural orifice (such as the mouth, anus, or vagina), rather than through an incision in the abdominal wall. In this trial, we will extract surgical specimens from the rectum to reduce trauma to the abdominal wall.
Other Names:
  • NOSES
test set
The test set is a dataset used to evaluate the performance of a trained machine learning model. It includes another randomly enrolled group of patients with colon and rectal cancer, as well as their clinical and pathological data and surgical outcomes. The outputs are not used during training, but are used to test the trained model to evaluate its predictive ability on unknown data. The purpose is to evaluate the model's generalization ability, that is, its performance on new and unknown data.
Natural Orifice Specimen Extraction Surgery (NOSES) is a minimally invasive surgical technique that aims to reduce the size and number of incisions required during certain surgeries. In NOSES, the surgical specimen (such as a diseased organ or tumor) is removed from the body through a natural orifice (such as the mouth, anus, or vagina), rather than through an incision in the abdominal wall. In this trial, we will extract surgical specimens from the rectum to reduce trauma to the abdominal wall.
Other Names:
  • NOSES

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The number of successful operations performed
Time Frame: 3 years
Accuracy will be calculated by the number of successful operations performed
3 years
The number of successful operations actually completed.
Time Frame: 3 years
Accuracy will be calculated by the number of successful operations actually completed.
3 years

Collaborators and Investigators

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

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 (Anticipated)

June 1, 2023

Primary Completion (Anticipated)

June 1, 2026

Study Completion (Anticipated)

June 1, 2026

Study Registration Dates

First Submitted

March 21, 2023

First Submitted That Met QC Criteria

April 3, 2023

First Posted (Actual)

April 4, 2023

Study Record Updates

Last Update Posted (Actual)

April 4, 2023

Last Update Submitted That Met QC Criteria

April 3, 2023

Last Verified

April 1, 2023

More Information

Terms related to this study

Other Study ID Numbers

  • 1010PY(2022)-09

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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