Machine LearningNCT05797064
Establishment of a Feasibility Model for NOSE Surgery Based on Machine Learning
An observational study of Machine Learning and Surgery, run by Sixth Affiliated Hospital, Sun Yat-sen University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Machine Learning, Surgery, Rectosigmoid Cancer
- What is being tested?
- Natural Orifice Specimen Extraction Surgery
- Type of treatment
- Surgery or procedure
- How many people
- 460 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2023to 1 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients diagnosed with resectable rectosigmoid cancer.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
The Sixth Affiliate Hospital of Sun Yat-Sen University
Guangzhou, Guangdong
Yanxin Luo, PhD,MD, 86-13826190263, luoyx25@mail.sysu.edu.cn
Yaoyi Huang, BS, 86-15986423743, huangyy355@mail2.sysu.edu.cn
About this study
Official title: 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.
- Conditions
- Machine Learning, Surgery, Rectosigmoid Cancer, Natural Orifice Specimen Extraction Surgery
- Being tested
- Natural Orifice Specimen Extraction Surgery
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Natural Orifice Specimen Extraction SurgeryProcedure or surgery
Also called NOSES
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Procedure: Natural Orifice Specimen Extraction Surgery | |
| 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. Receives: Procedure: Natural Orifice Specimen Extraction Surgery |
What is being measured
The primary outcome is the main question the study is designed to answer.
The number of successful operations performed
Accuracy will be calculated by the number of successful operations performed
Measured: 3 years
The number of successful operations actually completed.
Accuracy will be calculated by the number of successful operations actually completed.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Apr 2023
- Study start1 Jun 2023 (estimated)
- Primary completion1 Jun 2026 (estimated)
- Study completion1 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Machine LearningStudies of the same condition, open and under way.
- More trials run by Sixth Affiliated Hospital, Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 460 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yanxin Luo,MD, Sixth Affiliated Hospital, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05797064
- Sponsor's protocol ID
- 1010PY(2022)-09
- Status last verified
- Apr 2023
- First submitted
- 21 Mar 2023
- Last updated
- 4 Apr 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 4 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishment of a Feasibility Model for Predicting Natural Orifice Specimen Extraction Surgery (NOSES) Based on Machine Learning.
- ClinicalTrials.gov ID
- NCT05797064
- Acronym / sponsor ID
- 1010PY(2022)-09
- Conditions
- Machine Learning, Surgery, Rectosigmoid Cancer, Natural Orifice Specimen Extraction Surgery
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 460 participants (estimated)
- Lead sponsor
- Sixth Affiliated Hospital, Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 1 Jun 2023 (estimated)
- Primary completion
- 1 Jun 2026 (estimated)
- Study completion
- 1 Jun 2026 (estimated)
- First posted
- 4 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training set | – | Procedure: Natural Orifice Specimen Extraction SurgeryThe 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. |
| test set | – | Procedure: Natural Orifice Specimen Extraction SurgeryThe 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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with resectable rectosigmoid cancer.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (8 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Natural Orifice Specimen Extraction SurgeryAlso: NOSESNatural 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. | Procedure or surgery | Training set, test set |
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Apr 2023.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The Sixth Affiliate Hospital of Sun Yat-Sen University
Guangzhou, China
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Machine Learning and Surgery, run by Sixth Affiliated Hospital, Sun Yat-sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 4 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The number of successful operations performed (time frame: 3 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Apr 2023; AzTrials last refreshed its copy on 6 Nov 2026.