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

NCT06559046 | Completed

• 1 other identifier: 2024107
• Study Type: observational
• Target: 483
• Locations: 1 country
• Sites: 1

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).

Conditions

Tumor Grading; Deep Learning; Clear Cell Renal Cell Carcinoma

Outcome Measures

Primary Outcomes (2)

• 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

Study Arms (2)

High grade

Low grade

Eligibility Criteria

• Age: 30 Years - 88 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Ting Huang: lead

Study Sites (1)

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

Jinhua, Zhejiang, 321000, China

Location

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Kidney Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: August 12, 2024
• First Posted: August 19, 2024
• Study Start: January 1, 2019
• Primary Completion: June 30, 2024
• Study Completion: June 30, 2024
• Last Updated: August 20, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)