NCT06548464Completed
Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients
Development and Validation of Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients: a Multicenter Retrospective Study
Source: ClinicalTrials.gov · Last updated 12 Aug 2024
Brief summary (registry, in English)
This multicenter, retrospective cohort study aimed to develop and validate an explainable prediction model for prognosis after gastrectomy in patients with gastric cancer.
- Status (registry)
- Completed
- Study type
- Observational
- Enrollment
- 18000 (actual)
- First submitted
- 6 Aug 2024
- Start date
- Jun 2024
- Primary completion date
- Aug 2024
- Lead sponsor
- Chang-Ming Huang, Prof.
- NCT number
- NCT06548464
What you commit to
- Study type: Observational
- Participants enrolled: 18000
- From Jun 2024 to the primary completion date, Aug 2024
- 1 site in 1 country
- Visits and tests: ask the trial team, the registry does not list them
Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.
Who cannot take part
Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.
Prior treatment
- Presence of distant metastases before surgery
Other
- Non-adenocarcinoma histology
- Incomplete follow-up data
Who the study is looking for
- Patients diagnosed with primary gastric or gastroesophageal junction cancer
- Underwent radical gastrectomy
- Complete clinical and pathological data available
Sex: All · Does not accept healthy volunteers
Sites (1)
Sites in registry order. Pick your city on the disease page to sort them by distance.
| Site | City | Country | Site status |
|---|---|---|---|
| Chang-ming Huang Coordinator of this site? Confirm its status | Fuzhou | China | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.