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

NCT ID: NCT05170282

Last Updated: 2021-12-27

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-01-01

Study Completion Date

2023-12-31

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.

Conditions

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Hepatoblastoma Hepatic Hemangioendothelioma

Study Design

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Observational Model Type

COHORT

Study Time Perspective

OTHER

Study Groups

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Retrospective cohort

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.

Radiomic Algorithm

Intervention Type DIAGNOSTIC_TEST

Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

Prospective cohort

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

Radiomic Algorithm

Intervention Type DIAGNOSTIC_TEST

Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

Interventions

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Radiomic Algorithm

Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.

Intervention Type DIAGNOSTIC_TEST

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
Minimum Eligible Age

0 Months

Maximum Eligible Age

12 Months

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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West China Hospital

OTHER

Sponsor Role lead

Responsible Party

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Yuhan Yang

Doctor of Medicine

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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West China Hospital, Sichuan University

Chengdu, Sichuan, China

Site Status RECRUITING

Countries

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China

Facility Contacts

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Yuhan Yang, MD

Role: primary

References

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Yang Y, Zhou Z, Li Y. MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy. Eur J Pediatr. 2023 Oct;182(10):4365-4368. doi: 10.1007/s00431-023-05113-x. Epub 2023 Jul 18.

Reference Type DERIVED
PMID: 37462798 (View on PubMed)

Other Identifiers

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HX2021-345

Identifier Type: -

Identifier Source: org_study_id