Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06559046
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-08-12
Brief Title:
A CT-BASED Deep Learning Model for Predicting WHOISUP Pathological Grades of Clear Cell Renal Cell Carcinoma ccRCC A Multicenter Cohort Study
Sponsor:
None
Organization:
None
Study Overview
Official Title:
A CT-BASED Deep Learning Model for Predicting WHOISUP Pathological Grades of Clear Cell Renal Cell Carcinoma ccRCC A Multicenter Cohort Study
Status:
COMPLETED
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 25D 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
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 25D 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None