Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children

Lymphoma Clinical Trial

Official title:

Computer Aided Diagnostic Tool on Computed Tomography Images for Diagnosis of Retroperitoneal Tumor in Children

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05179850
• Other study ID #: HX-2021477
• Status: Recruiting
• 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
• 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

Related Conditions & MeSH terms

• Germ Cell Tumor
• Lymphoma
• Neoplasms
• Neoplasms, Germ Cell and Embryonal
• Neuroblastoma
• Sarcoma
• Teratoma
• Wilms Tumor
• Wilms' Tumor

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