Establishment of a Feasibility Model for Predicting Natural Orifice Specimen Extraction Surgery (NOSES) Based on Machine Learning.
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Machine Learning
- Sponsor
- Sixth Affiliated Hospital, Sun Yat-sen University
- Enrollment
- 460
- Locations
- 1
- Primary Endpoint
- The number of successful operations performed
- Status
- Not yet recruiting
- Last Updated
- 3 years ago
Overview
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.
Investigators
Yanxin Luo,MD
Principal Investigator
Sixth Affiliated Hospital, Sun Yat-sen University
Eligibility Criteria
Inclusion Criteria
- •Patients diagnosed with colorectal cancer or large adenoma who are suitable for laparoscopic colorectal surgery;
- •Tumor staging ≤ T3 without invasion of surrounding organs;
- •No abdominal seeding or distant organ metastasis;
- •Clear and complete imaging data (CT, pelvic MRI) that can be processed by a computer;
- •Feasible evaluation and determination for obtaining specimens through the rectal channel during preoperative and intraoperative assessments.
Exclusion Criteria
- •Contraindications for laparoscopic colorectal surgery;
- •Tumor staging is T4, or there are cancer nodules;
- •Presence of metastasis or distant organ metastasis;
- •Incomplete imaging data;
- •Preoperative intestinal obstruction;
- •Tumor or specimen diameter larger than the transverse diameter of the pelvic outlet;
- •Previous rectal radiotherapy;
- •Unsuitable evaluation and determination for obtaining specimens through the rectal channel during preoperative and intraoperative assessments.
Outcomes
Primary Outcomes
The number of successful operations performed
Time Frame: 3 years
Accuracy will be calculated by the number of successful operations performed
The number of successful operations actually completed.
Time Frame: 3 years
Accuracy will be calculated by the number of successful operations actually completed.