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

Not yet recruiting

• Conditions: Machine Learning; Rectosigmoid Cancer; Surgery; Natural Orifice Specimen Extraction Surgery
• Interventions: Procedure: Natural Orifice Specimen Extraction Surgery

• Registration Number: NCT05797064
• Lead Sponsor: Sixth Affiliated Hospital, Sun Yat-sen University

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2023-03-21
• Status Verified: 2023-04
• Study First Submitted QC: 2023-04-03
• Last Update Submitted: 2023-04-03
• Study First Posted: 2023-04-04
• Last Update Posted: 2023-04-04
• Study Start: 2023-06-01
• Primary Completion: 2026-06-01
• Study Completion: 2026-06-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 460

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
test set	Natural Orifice Specimen Extraction Surgery	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.
Training set	Natural Orifice Specimen Extraction Surgery	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.

Primary Outcome Measures

Name	Time	Method
The number of successful operations performed	3 years	Accuracy will be calculated by the number of successful operations performed
The number of successful operations actually completed.	3 years	Accuracy will be calculated by the number of successful operations actually completed.

Trial Locations

Locations (1)

The Sixth Affiliate Hospital of Sun Yat-Sen University; 🇨🇳 GuangZhou, Guangdong, China