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Recruiting NCT07166445

Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT

— Observational Carcinoma, Renal Cell Diagnostic Imaging Pathology Deep Learning

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
The protocol lists: None intervention.
Who it may be relevant to
Registry conditions: Carcinoma, Renal Cell, Diagnostic Imaging, Pathology, Deep Learning. Basic parameters: 18 years — 85 years · 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 →

Unsure about the terms? Read our patient guide →

Overview

This study aims to develop and validate a contrast-enhanced CT-based deep-learning model for automatic and accurate preoperative discrimination between T1-T2 and T3 renal cell carcinoma. By quantifying the model's diagnostic performance on an independent test set-using AUC, sensitivity, specificity, positive/negative predictive values, and decision-curve analysis-we will establish a decision-support tool that can be seamlessly integrated into clinical PACS, thereby reducing staging errors, refining surgical planning, and improving patient outcomes.

Interventions

  • Other None intervention
    this study is retrospective based on the CT images, which dose include any intervention.

Primary outcome measures

  • diagnostic performance [Time frame: from 2024 to 2027]

Eligibility criteria

Inclusion criteria

  • Histopathologically confirmed renal cell carcinoma on postoperative specimen.
  • Preoperative contrast-enhanced CT performed at our institution with slice thickness ≤ 1 mm and complete DICOM datasets.
  • Postoperative pathologic staging clearly defined as pT1a-T2b or pT3a.
  • CT image quality deemed adequate for analysis.

Exclusion criteria

  • 1\. Pathologic subtype other than RCC. 2. Images with severe artifacts.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model
Cohort

Study locations

China · 1 center
  • Peking University First Hospital, Beijing, — Beijing

Identifiers

NCT: NCT07166445 · PUH-2025-RCC-DL-TS001

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗