Ignite Creation Date:
2025-12-24 @ 4:54 PM
Ignite Modification Date:
2026-01-05 @ 5:48 PM
Study NCT ID:
NCT05179850
Status:
UNKNOWN
Last Update Posted:
2022-01-20
First Post:
2021-12-16
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children
Sponsor:
West China Hospital
Organization:
Study Overview
Official Title:
Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children
Status:
UNKNOWN
Status Verified Date:
2022-01
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for retroperitoneal tumor using machine learning and deep learning techniques on computed tomography images in children.
Detailed Description:
The retroperitoneal space extends from the lumbar region to the pelvic region and houses vital structures such as the kidney, the ureter, the adrenal glands, the pancreas, the aorta and its branches, the inferior vena cava and its tributaries, lymph nodes, and loose connective tissue meshwork along with fat. This space thus allows the silent growth of primary and metastatic tumors, such that clinical features appear often too late. The therapeutic regimen differs on various types of retroperitoneal tumor in children. It is damaging for pediatric patients to acquire histological specimens through invasive procedures. Hence, an urgent evaluation is absolutely necessary for preoperative diagnosis in such cases via noninvasive approaches. This study is a retrospective-prospective design by West China Hospital, Sichuan University, including clinical data and radiological images. A retrospective database was enrolled for patients with definite histological diagnosis and available computed tomography images from June 2010 and December 2020. The investigators have constructed deep learning and machine learning radiomics diagnostic models on this retrospective cohort and validated it internally. A prospective cohort would recruit infantile patients diagnosed as retroperitoneal tumor since January 2021. The proposed deep learning model would also be validated in this prospective cohort externally. The aim of this study was to evaluate the diagnostic efficacy of computer aided diagnostic tool for retroperitoneal tumor using machine learning and deep learning techniques on computed tomography images in children.
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: