Clinical trial NCT05170282: Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants
Official title: Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Hepatoblastoma
Hepatic Hemangioendothelioma
Lead sponsor: West China Hospital
Sponsor class: OTHER
First posted: 2021-12-27
Enrollment: 200
Sex: ALL
Minimum age (years): 0
Maximum age (years): 12
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
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.

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.

Detailed description: 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.

Eligibility criteria: 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

Primary outcomes:
measure: The diagnostic accuracy of infantile liver tumors with deep learning algorithm
timeFrame: 1 month
Secondary outcomes:
measure: The diagnostic sensitivity of infantile liver tumors with deep learning algorithm
timeFrame: 1 month
measure: The diagnostic specificity of infantile liver tumors with deep learning algorithm
timeFrame: 1 month
measure: The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm
timeFrame: 1 month
measure: The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm
timeFrame: 1 month
Other outcomes:
Locations:
city: Chendu
country: China
facility: West China Hospital, Sichuan University
state: Sichuan
Study officials:
References: