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

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).

Study Overview

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

• Study Type: Observational
• Enrollment (Actual): 483

Contacts and Locations

Study Locations

• China
  • Zhejiang
    • Jinhua, Zhejiang, China, 321000
      • Department of Urology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua,Zhejiang, China

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

This study recruited three cohorts of patients with pathologically diagnosed ccRCC, including a total of 483 patients who underwent nephrectomy or partial nephrectomy

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
High grade
Low grade

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)	AUC curve	2019-2024
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)	DCA curve	2019-2024

Collaborators and Investigators

• Sponsor: Ting Huang

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2019
• Primary Completion (Actual): June 30, 2024
• Study Completion (Actual): June 30, 2024

Study Registration Dates

• First Submitted: August 12, 2024
• First Submitted That Met QC Criteria: August 14, 2024
• First Posted (Actual): August 19, 2024

Study Record Updates

• Last Update Posted (Actual): August 20, 2024
• Last Update Submitted That Met QC Criteria: August 16, 2024
• Last Verified: August 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Histologic Type; Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Kidney Diseases; Urologic Diseases; Adenocarcinoma; Neoplasms, Glandular and Epithelial; Kidney Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Carcinoma, Renal Cell; Carcinoma
• Other Study ID Numbers: 2024107

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No