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).

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Key information

Conditions

Tumor Grading Adenocarcinoma Carcinoma Carcinoma, Renal Cell Clear Cell Renal Cell Carcinoma Deep Learning Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Age range

30 year–88 year

Sex eligibility

All sexes

Study type

Observational

Primary location

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

Jinhua, Zhejiang, 321000, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

predict the pathological grades of clear cell renal cell carcinoma (ccRCC)

Time frame: 2019-2024

AUC curve
predict the pathological grades of clear cell renal cell carcinoma (ccRCC)

Time frame: 2019-2024

DCA curve

Sponsors and collaborators

Lead sponsor

Ting Huang

Other

Registry information

Important dates

Study start: 2019
Primary completion: 2024
Study completion: 2024
First posted: Aug 19, 2024
Registry last updated: Aug 20, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed