Clinical trial · Observational
Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants
NCT05170282CI-TRIAL-00055672unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Hepatic Hemangioendothelioma | — | UNRESOLVED | — |
| Hepatoblastoma | Hepatoblastoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Radiomic Algorithm | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- 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.
- interventionNames
- Diagnostic Test: Radiomic Algorithm
- label
- Prospective cohort
- description
- The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort.
- interventionNames
- Diagnostic Test: Radiomic Algorithm
Primary outcomes (1)
- measure
- The diagnostic accuracy of infantile liver tumors with deep learning algorithm
- timeFrame
- 1 month
- description
- The diagnostic accuracy of infantile liver tumors with deep learning algorithm.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 0 Months
- Maximum age
- 12 Months
Show eligibility criteria text
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
References
Publications (1)
- DERIVEDYang 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