Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants

Hepatoblastoma Clinical Trial

Official title:

Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05170282
• Other study ID #: HX2021-345
• Status: Recruiting
• Start date: January 1, 2021
• Est. completion date: December 31, 2023

Study information

• Verified date: December 2021
• Source: West China Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Hepatic tumors in the perinatal period are associated with significant morbidity and mortality in affected patients. The conventional diagnostic tool, such as alpha-fetoprotein (AFP) shows limited value in diagnosis of infantile hepatic tumors. This retrospective-prospective study is aimed to evaluate the diagnostic efficiency of the deep learning system through analysis of magnetic resonance imaging (MRI) images before initial treatment.

Description:

Hepatic tumors seldom occur in the perinatal period. They comprise approximately 5% of the total neoplasms of various types occurring in the fetus and neonate. Infantile hemangioendothelioma is the leading primary hepatic tumor followed by hepatoblastoma. It should be mentioned that alpha-fetoprotein (AFP) is highly elevated during the first several months after birth even in normal infants, thus the diagnostic value of AFP is limited for infantile patients with hepatic tumors. 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 magnetic resonance imaging (MRI) images from June 2010 and December 2020. The investigators have constructed a deep learning radiomics diagnostic model on this retrospective cohort and validated it internally. A prospective cohort would recruit infantile patients diagnosed as liver tumor since January 2021. The proposed deep learning model would also be validated in this prospective cohort externally. The established model would be able to assist diagnosis for hepatic tumor in infants.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: December 31, 2023
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A to 12 Months

Eligibility

Inclusion Criteria:

• Age between newborn and 12 months

• Receiving no treatment before diagnosis

• With written informed consent

Exclusion Criteria:

• Clinical data missing

• Unavailable MRI images

• Without written informed consent

Related Conditions & MeSH terms

• Hemangioendothelioma
• Hepatic Hemangioendothelioma
• Hepatoblastoma
• Liver Neoplasms

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	Chendu	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	The diagnostic accuracy of infantile liver tumors with deep learning algorithm	The diagnostic accuracy of infantile liver tumors with deep learning algorithm.	1 month
Secondary	The diagnostic sensitivity of infantile liver tumors with deep learning algorithm	The diagnostic sensitivity of infantile liver tumors with deep learning algorithm.	1 month
Secondary	The diagnostic specificity of infantile liver tumors with deep learning algorithm	The diagnostic specificity of infantile liver tumors with deep learning algorithm.	1 month
Secondary	The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm	The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm.	1 month
Secondary	The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm	The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm.	1 month