HepatoblastomaNCT05170282
Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants
An observational study of Hepatoblastoma and Hepatic Hemangioendothelioma, run by West China Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 0 to 12 months
- Conditions the study is for
- Hepatoblastoma, Hepatic Hemangioendothelioma
- What is being tested?
- Radiomic Algorithm
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2021to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 to 12 months
- All sexes
- No healthy volunteers
- Child
Study population: Patients who had liver tumor and completed the abdominal MRI examination before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.
Who may be able to join
- Age between newborn and 12 months
- Receiving no treatment before diagnosis
- With written informed consent
Who cannot join
- Clinical data missing
- Unavailable MRI images
- Without written informed consent
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
West China Hospital, Sichuan University
Chengdu, Sichuan, 610041
Yuhan Yang, MD, 8613258389785, yyh_1023@163.com
About this study
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.
Read the 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.
- Conditions
- Hepatoblastoma, Hepatic Hemangioendothelioma
- Being tested
- Radiomic Algorithm
- Medical subject headings
- Hepatoblastoma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Radiomic AlgorithmDiagnostic test
Different radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Radiomic Algorithm | |
| Prospective cohort | The same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort. Receives: Diagnostic Test: Radiomic Algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnostic accuracy of infantile liver tumors with deep learning algorithm
The diagnostic accuracy of infantile liver tumors with deep learning algorithm.
Measured: 1 month
Secondary outcomes (4)
The diagnostic sensitivity of infantile liver tumors with deep learning algorithm
The diagnostic sensitivity of infantile liver tumors with deep learning algorithm.
Measured: 1 month
The diagnostic specificity of infantile liver tumors with deep learning algorithm
The diagnostic specificity of infantile liver tumors with deep learning algorithm.
Measured: 1 month
The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm
The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm.
Measured: 1 month
The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm
The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Dec 2021
- Study start1 Jan 2021
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for HepatoblastomaStudies of the same condition, open and under way.
- More trials run by West China HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- West China Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yuhan Yang, West China Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05170282
- Sponsor's protocol ID
- HX2021-345
- Status last verified
- Dec 2021
- First submitted
- 8 Dec 2021
- Last updated
- 27 Dec 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 37462798
Study at a glance
From the registry record, last updated by the sponsor on 27 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Magnetic Resonance Imaging Radiomics for Diagnostic Value of Hepatic Tumors in Infants
- ClinicalTrials.gov ID
- NCT05170282
- Acronym / sponsor ID
- HX2021-345
- Conditions
- Hepatoblastoma, Hepatic Hemangioendothelioma
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- West China HospitalUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 27 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective cohort | – | Diagnostic Test: Radiomic AlgorithmThe 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. |
| Prospective cohort | – | Diagnostic Test: Radiomic AlgorithmThe same inclusion/exclusion criteria were applied for the same center prospectively. It is an external validation cohort. |
Who may qualify
- Ages
- 0 to 12 months
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- Patients who had liver tumor and completed the abdominal MRI examination before operation, biopsy, neoadjuvant chemotherapy, and radiotherapy.
Key inclusion criteria (3 listed)
- Age between newborn and 12 months
- Receiving no treatment before diagnosis
- With written informed consent
Key exclusion criteria (3 listed)
- Clinical data missing
- Unavailable MRI images
- Without written informed consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Radiomic AlgorithmDifferent radiomic, machine learning, and deep learning strategies for radiomic features extraction, sorting features and model constriction. | Diagnostic test | Prospective cohort, Retrospective cohort |
Primary outcome
The diagnostic accuracy of infantile liver tumors with deep learning algorithm
Time frame: 1 month
The diagnostic accuracy of infantile liver tumors with deep learning algorithm.
4 secondary outcomes
The diagnostic sensitivity of infantile liver tumors with deep learning algorithm
Time frame: 1 month
The diagnostic specificity of infantile liver tumors with deep learning algorithm
Time frame: 1 month
The diagnostic positive predictive value of infantile liver tumors with deep learning algorithm
Time frame: 1 month
The diagnostic negative predictive value of infantile liver tumors with deep learning algorithm
Time frame: 1 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Dec 2021.
- Derived from this study: PubMed 37462798Yang 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hepatoblastoma and Hepatic Hemangioendothelioma, run by West China Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 27 Dec 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic accuracy of infantile liver tumors with deep learning algorithm (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05170282, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Dec 2021.