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

NCT07166445 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2025-09-10

No results posted yet for this 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.

Conditions

  • Carcinoma, Renal Cell
  • Diagnostic Imaging
  • Pathology
  • Deep Learning

Interventions

OTHER

None intervention

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

Sponsors & Collaborators

  • Peking University First Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-09-01
Primary Completion
2025-12-01
Completion
2027-12-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07166445 on ClinicalTrials.gov