Machine Learning, Natural Orifice Specimen Extraction Surgery, Rectosigmoid Cancer, Surgery
Conditions
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.
Interventions
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.
Sponsors
Sixth Affiliated Hospital, Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
China
Outcome results
None listed