Clinical trial · Observational
Establishment of a Feasibility Model for NOSE Surgery Based on Machine Learning
Establishment of a Feasibility Model for Predicting Natural Orifice Specimen Extraction Surgery (NOSES) Based on Machine Learning.
NCT05797064CI-TRIAL-00065300unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Machine Learning | — | UNRESOLVED | — |
| Natural Orifice Specimen Extraction Surgery | — | UNRESOLVED | — |
| Rectosigmoid Cancer | Malignant Rectosigmoid Neoplasm | ALIAS | 0.90 |
| Surgery | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Natural Orifice Specimen Extraction Surgery | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Training set
- description
- 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.
- interventionNames
- Procedure: Natural Orifice Specimen Extraction Surgery
- label
- test set
- description
- 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.
- interventionNames
- Procedure: Natural Orifice Specimen Extraction Surgery
Primary outcomes (2)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available
No reference posted for this study.