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

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.

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Key information

Conditions

Carcinoma, Renal Cell Adenocarcinoma Carcinoma Deep Learning Diagnostic Imaging Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Pathology Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Age range

18 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University First Hospital, Beijing,

Beijing, China

Location status: Recruiting

Location contact

Peking University First Hospital Peking University First Hospital

CONTACT

[email protected]

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

None intervention

Other

this study is retrospective based on the CT images, which dose include any intervention.

Primary outcomes

diagnostic performance

Time frame: from 2024 to 2027

Study contacts

Contact information is provided by the study sponsor or research team.

Zejin Ou

CONTACT

[email protected]

159 1494 4390

Sponsors and collaborators

Lead sponsor

Peking University First Hospital

Other

Registry information

Important dates

Study start: 2024
Primary completion: 2025
Study completion: 2027
First posted: Sep 10, 2025
Registry last updated: Sep 10, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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