NCT06876584Completed
The CT-based Deep Learning Model Predicts Complications in Partial Nephrectomy
The CT-based Deep Learning Model Outperforms Traditional Anatomical Classification Models in Preoperatively Predicting Complications and Risk Grade in Partial Nephrectomy
Source: ClinicalTrials.gov · Last updated 14 Mar 2025
Brief summary (registry, in English)
The investigators combine radiomics and deep learning to analyze the lesions more thoroughly, aiming for a more accurate prediction of complications in partial nephrectomy, and compare this approach with traditional models.
- Status (registry)
- Completed
- Study type
- Observational
- Enrollment
- 1474 (actual)
- First submitted
- 8 Mar 2025
- Start date
- 1 Jun 2024
- Primary completion date
- 31 Dec 2024
- Lead sponsor
- Du Lingzhi
- Diseases
- Kidney cancer, Childhood cancers
- NCT number
- NCT06876584
What you commit to
- Study type: Observational
- Participants enrolled: 1474
- From 1 Jun 2024 to the primary completion date, 31 Dec 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.
Other
- Missing or unavailable imaging data
- No available enhanced CT images
Who the study is looking for
- Clinical diagnosis of renal cell carcinoma or renal cyst
- Underwent partial nephrectomy between June 2014 and July 2024
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 |
|---|---|---|---|
| Name: Zhongshan Hospital Fudan University, Location: 180th Fenglin Road, Xuhui District, Shanghai, China Coordinator of this site? Confirm its status | Shanghai | China | Unknown status |
This page is information, not medical advice. Discuss any trial with your doctor.