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

NCT05170282 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2021-12-27

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

Radiomic Algorithm

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

Sponsors & Collaborators

  • West China Hospital

    lead OTHER

Eligibility

Min Age
0 Months
Max Age
12 Months
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-01-01
Primary Completion
2023-12-31
Completion
2023-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05170282 on ClinicalTrials.gov