Machine Learning Predicts Survival and Mutations in Ovarian Metastases of Colorectal Cancer
NCT ID: NCT06192030
Last Updated: 2024-01-05
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
200 participants
OBSERVATIONAL
2022-08-27
2025-08-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
OTHER
Study Groups
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Retrospective cohort
The cohort was retrospectively enrolled in The Sixth Affiliated Hospital, Sun Yat-sen University from August 2010 to August 2022. It is a training cohort.
Prediction model
We develop and validate clinical models to predict patient survival and gene signatures in ovarian metastases of colorectal cancer.
Prospective cohort
The same inclusion/exclusion criteria were applied for the same center prospectively. It is a validation cohort.
Prediction model
We develop and validate clinical models to predict patient survival and gene signatures in ovarian metastases of colorectal cancer.
Interventions
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Prediction model
We develop and validate clinical models to predict patient survival and gene signatures in ovarian metastases of colorectal cancer.
Eligibility Criteria
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Inclusion Criteria
* Unilateral or bilateral ovarian masses confirmed by peroperative imaging examination
* Patient requiring resection of their ovarian and/or peritoneal carcinomatosis
* 18 ≤ Age ≤ 85
* World Health Organization performance status ≤ 1
* Life expectancy \> 12 weeks
* Adequate haematological, liver and renal function
* Patient information and signature of the informed consent form before the start of any treatment procedures
Exclusion Criteria
* Primary ovarian tumor
* Clinical data missing
18 Years
85 Years
FEMALE
No
Sponsors
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Sixth Affiliated Hospital, Sun Yat-sen University
OTHER
Responsible Party
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Locations
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Sixth Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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wanghm7
Identifier Type: -
Identifier Source: org_study_id
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