Carcinoma, Renal Cell, Deep Learning, Diagnostic Imaging, Pathology
Conditions
Brief 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.
Interventions
OTHERNone intervention
this study is retrospective based on the CT images, which dose include any intervention.
Sponsors
Peking University First Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| diagnostic performance | from 2024 to 2027 |
Countries
China
Contacts
Primary ContactZejin Ou
Outcome results
None listed