Recruiting NCT06088134
Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Clear Cell Renal Cell Carcinoma, Prognostic Cancer Model, Recurrent Renal Cell Cancer. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Urology Department of the First Affiliated Hospital of Chongqing Medical University
Overview
This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.
Primary outcome measures
- disease-free survival (DFS) [Time frame: recruitment occurred between June 2013 and March 2020]
Eligibility criteria
Inclusion criteria
- underwent partial/radical nephrectomies
- histologically diagnosed as ccRCC
- with complete clinical data and preoperative CT image data
Exclusion criteria
- with incomplete clinic-pathological data
- lack of preoperative contrast-enhanced CT images or the image quality was unsuitable for analysis
- who received pre-surgery neoadjuvant or adjuvant therapies
- with multiple renal tumors or/and had synchronous metastasis
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Yingjie Xv — Chongqing
Publications
- Xv Y, Wei Z, Jiang Q, Zhang X, Chen Y, Xiao B, Yin S, Xia Z, Qiu M, Li Y, Tan H, Xiao M. Three-dimensional deep learning model complements existing models for preoperative disease-free survival prediction in localized clear cell renal cell carcinoma: a multicenter retrospective cohort study. Int J Surg. 2024 Nov 1;110(11):7034-7046. doi: 10.1097/JS9.0000000000001808. PMID 38896853
Identifiers
NCT: NCT06088134 · DL-ccRCC