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

Sichuan University

Status: Unknown

Conditions: Hepatoblastoma; Hepatic Hemangioendothelioma

Treatments: Diagnostic Test: Radiomic Algorithm

Study type: Observational

Funder types: Other

Identifiers

NCT05170282

HX2021-345

Details and patient eligibility

About

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.

Full 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.

Enrollment: 200 estimated patients

Sex: All

Ages: Under 12 months old

Volunteers: No Healthy Volunteers

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

Trial design

200 participants in 2 patient groups

Retrospective cohort

Description:

The internal cohort was retrospectively enrolled in West China Hospital, Sichuan University from June 2010 and December 2020. It is a training and internal validation cohort.

Treatment:

Diagnostic Test: Radiomic Algorithm

Prospective cohort

Description:

The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.

Treatment:

Diagnostic Test: Radiomic Algorithm