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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Hepatoblastoma, Hepatic Hemangioendothelioma

Intervention / Treatment
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* Diagnostic Test: Radiomic Algorithm


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 0 Months
Maximum Age: 12 Months

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Retrospective cohort<br>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. | Diagnostic Test: Radiomic Algorithm<br>* Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.<br>|
| Prospective cohort<br>The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort. | Diagnostic Test: Radiomic Algorithm<br>* Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The diagnostic sensitivity of infantile liver tumors with deep learning algorithm | The diagnostic sensitivity of infantile liver tumors with deep learning algorithm. | 1 month | 
| The diagnostic specificity of infantile liver tumors with deep learning algorithm | The diagnostic specificity of infantile liver tumors with deep learning algorithm. | 1 month | 
| 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 | 
| 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 |