NCT06559046

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 483 (actual)
• Sponsor: Ting Huang · Academic / Other
• Sex: All
• Age: 30 Years – 88 Years
• Healthy volunteers: Not accepted

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).

Conditions

• Tumor Grading
• Deep Learning
• Clear Cell Renal Cell Carcinoma

Timeline

• Start date: 2019-01-01
• Primary completion: 2024-06-30
• Completion: 2024-06-30
• First posted: 2024-08-19
• Last updated: 2024-08-20

Locations

1 site across 1 country: China