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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05179850
Other study ID # HX-2021477
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date January 1, 2021
Est. completion date December 31, 2023

Study information

Verified date January 2022
Source West China Hospital
Contact Yuhan Yang, MD
Phone 8613258389785
Email yyh_1023@163.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 400
Est. completion date December 31, 2023
Est. primary completion date December 31, 2023
Accepts healthy volunteers No
Gender All
Age group N/A to 18 Years
Eligibility Inclusion Criteria: - Age up to 18 years old - Receiving no treatment before diagnosis - With written informed consent Exclusion Criteria: - Clinical data missing - Unavailable computed tomography images - Without written informed consent

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
Radiomic Algorithm
Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

Locations

Country Name City State
China West China Hospital, Sichuan University Chengdu Sichuan

Sponsors (1)

Lead Sponsor Collaborator
West China Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Pathological tumor diagnosis The diagnosis is defined by histopathological specimens from surgery and/or biopsy. Baseline
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