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

NCT07166445 · tracked via the Priya Life Science China tracker

Sponsor

Peking University First Hospital

Phase

Observational

Started

2024-09-01

Last updated

2025-09-10

Condition(s) studied

Carcinoma, Renal CellDiagnostic ImagingPathologyDeep Learning

Investigational drug(s) / intervention(s)

None intervention

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

Study summary

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.

Eligibility

Sex

ALL

Min age

18 Years

Max age

85 Years

Healthy volunteers

Accepted

Inclusion Criteria: 1. Histopathologically confirmed renal cell carcinoma on postoperative specimen. 2. Preoperative contrast-enhanced CT performed at our institution with slice thickness ≤ 1 mm and complete DICOM datasets. 3. Postoperative pathologic staging clearly defined as pT1a-T2b or pT3a. 4. CT image quality deemed adequate for analysis. Exclusion Criteria: * 1\. Pathologic subtype other than RCC. 2. Images with severe artifacts.

Primary outcome measure(s)

diagnostic performance — from 2024 to 2027

Trial sites (1)

Facility	City	Region	Status
Peking University First Hospital, Beijing,	Beijing	China	Recruiting

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Official registry record

This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.

View NCT07166445 on ClinicalTrials.gov ↗ ← All trials in China