A CT-BASED Deep Learning Model for Predicting WHO/ISUP Pathological Grades of Clear Cell Renal Cell Carcinoma (ccRCC) :A Multicenter Cohort Study

Completed

• Conditions: Tumor Grading; Deep Learning; Clear Cell Renal Cell Carcinoma

• Registration Number: NCT06559046
• Lead Sponsor: Ting Huang

Brief Summary

This study aims to establish an effective deep learning model to extract relevant information about renal tumors and kidneys from computed tomography (CT) images and predict the pathological grades of clear cell renal cell carcinoma (ccRCC).

Retrospective data were collected from 483 ccRCC patients across three medical centers. Arterial phase and portal venous phase CT images from the dataset were segmented for renal tumors and kidneys. Three convolutional neural networks (CNNs) were employed to extract features from the regions of interest (ROI) in the CT images across multiple dimensions including 3D, 2.5D, and 2D. Least absolute shrinkage and selection (LASSO) regression was used for feature selection. The models were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

Detailed Description

Not available

Study Timeline

• Study Start: 2019-01-01
• Primary Completion: 2024-06-30
• Study Completion: 2024-06-30
• Status Verified: 2024-08
• Study First Submitted: 2024-08-12
• Study First Submitted QC: 2024-08-14
• Last Update Submitted: 2024-08-16
• Study First Posted: 2024-08-19
• Last Update Posted: 2024-08-20

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 483

Inclusion Criteria

• Patients with a single kidney tumor have complete imaging and clinical data
• Contrast-enhanced CT scan within 30 days before surgery
• No treatment was performed before CT examination

Exclusion Criteria

• Patients with tumor recurrence
• Obvious artifacts on CT images
• The tumor is cystic
• Multiple cysts on the affected kidney affect the delineation of renal parenchyma

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)	2019-2024	DCA curve

Trial Locations

Locations (1)

Department of Urology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua,Zhejiang, China; 🇨🇳 Jinhua, Zhejiang, China