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

NCT05170282 | Unknown

• 1 other identifier: HX2021-345
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Hepatoblastoma; Hepatic Hemangioendothelioma

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• The diagnostic sensitivity of infantile liver tumors with deep learning algorithm

  1 month

• 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

  1 month

• The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm

  1 month

Study Arms (2)

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.

Diagnostic Test: Radiomic Algorithm

Prospective cohort

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

Diagnostic Test: Radiomic Algorithm

Interventions

Radiomic Algorithm DIAGNOSTIC_TEST

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

Prospective cohort; Retrospective cohort

Eligibility Criteria

• Age: 0 Months - 12 Months
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Patients who had liver tumor and completed the abdominal MRI examination before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.

You may qualify if:

• Age between newborn and 12 months
• Receiving no treatment before diagnosis
• With written informed consent

You may not qualify if:

• Clinical data missing
• Unavailable MRI images
• Without written informed consent

Sponsors & Collaborators

• West China Hospital: lead

Study Sites (1)

West China Hospital, Sichuan University

Chengdu, Sichuan, 610041, China

RECRUITING

Related Publications (1)

• 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.

  PMID: 37462798 DERIVED

MeSH Terms

Conditions

Hepatoblastoma

Condition Hierarchy (Ancestors)

Neoplasms, Complex and Mixed; Neoplasms by Histologic Type; Neoplasms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Doctor of Medicine

Study Record Dates

• First Submitted: December 8, 2021
• First Posted: December 27, 2021
• Study Start: January 1, 2021
• Primary Completion: December 31, 2023
• Study Completion: December 31, 2023
• Last Updated: December 27, 2021

Record last verified: 2021-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)