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

NCT06559046 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 483

Last updated 2024-08-20

No results posted yet for this study

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

Sponsors & Collaborators

  • Ting Huang

    lead OTHER

Eligibility

Min Age
30 Years
Max Age
88 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-01-01
Primary Completion
2024-06-30
Completion
2024-06-30

Countries

  • China

Study Locations

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Entities

Diseases

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 NCT06559046 on ClinicalTrials.gov