Deep Learning for Automated Discrimination Between Stage T1-T2 and T3 Renal Cell Carcinoma on Contrast-Enhanced CT
Peking University First Hospital
1,000 participants
Sep 1, 2024
OBSERVATIONAL
Conditions
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
Inclusion Criteria4
- 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 Criteria1
- \. Pathologic subtype other than RCC. 2. Images with severe artifacts.
Interventions
this study is retrospective based on the CT images, which dose include any intervention.
Locations(1)
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NCT07166445