Liver FibrosisPSLFNCT06509230
Prediction of Significant Liver Fibrosis
An observational study of Liver Fibrosis, run by Huang Haijun at 1 site in China.
Trial summary
The deep learning method based on convolutional neural network (CNN) was used to extract the relevant features of liver fibrosis classification from the multi-modal information of digital pathological sections, clinical parameters and biomarkers of a large number of existing cases of liver puncture, and the U-Net… Read more about this study
- Who is it for?
- Ages 18 to 60 yearsHealthy volunteers can take part
- Conditions the study is for
- Liver Fibrosis
- How many people
- 700 participants
- Where
- 1 siteChina
- Study dates
- 20 Jul 2024to 31 Dec 2026
- How long will I take part?
- 4 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 18 to 60 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: A patient with chronic hepatitis B liver fibrosis confirmed by liver biopsy
Who may be able to join
- Age of 18-60 years old
- The diagnosis of chronic hepatitis B is in line with the diagnostic criteria of China's 2019 Chronic Hepatitis B Prevention and Treatment Guidelines, and the diagnosis of non-alcoholic fatty liver is in line with the Asian Pacific Hepatology Association guidelines
- Imaging showed no liver cancer
Who cannot join
- There are contraindications for liver biopsy
- Liver pathology did not meet the criteria
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Haijun Huang
Hangzhou, Zhejiang, 310014
Haijun Huang, 13758186635, huanghaijun0826@163.com
About this study
Official title: Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
The deep learning method based on convolutional neural network (CNN) was used to extract the relevant features of liver fibrosis classification from the multi-modal information of digital pathological sections, clinical parameters and biomarkers of a large number of existing cases of liver puncture, and the U-Net architecture of CNN was used to segment and extract the features of clinical medical images.
Read the detailed description
Patients with chronic hepatitis B underwent B-ultrasound-guided liver biopsy, and were divided into mild liver fibrosis group (fibrosis grade 0-1, S1), significant liver fibrosis group (fibrosis grade 2, S2), advanced liver fibrosis group and early cirrhosis group (fibrosis grade 3-4, S3-4) according to the pathological results.In this study, 200 patients with different degrees of liver fibrosis and 200 normal volunteers were collected from 2018 to 2022, and their clinical biochemical data, imaging data and peripheral blood samples were collected.The pathological microenvironment characteristics, imaging characteristics, clinical parameter characteristics and other data of patients were extracted, and the distillation learning method based on teacher-student model was adopted to develop and construct a multi-modal big data analysis model for accurate grading of liver fibrosis, so as to achieve a non-invasive intelligent grading diagnosis system for liver fibrosis.
- Conditions
- Liver Fibrosis
- Keywords
- Chronic hepatitis, Liver fibrosis, Artificial intelligence, Auxiliary diagnosis
- Medical subject headings
- Liver Cirrhosis, Hepatitis, Chronic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Other
The fibrosis grades were grouped without drug intervention
| Study group | Role | What they receive |
|---|---|---|
| mild fibrosis | S0-1 | |
| significant liver fibrosis | S2 | |
| Advanced liver fibrosis | S3-4 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Model development
Imaging (such as CT scan, MRI, X-ray, etc.) features and clinical parameters of patients were extracted, including population baseline characteristics (such as age, gender, comorbiditions, etc.), blood biochemical indicators (such as blood glucose, lipids, liver function indicators, etc.), and blood cytology indicators (such as white blood cell count, red blood cell count, etc.). Completed case selection and cohort establishment, multi-modal feature extraction and model development
Measured: 2024.6-2024.12
Secondary outcomes (1)
Build a multi-modal big data liver fibrosis early warning cloud platform system
We intend to design a cloud platform with data storage, processing and analysis components.Select the appropriate technology stack to ensure that the platform has the ability to handle large-scale data, and has good scalability and performance.The previously built multimodal liver fibrosis precision typing model was then embedded into the platform, ensuring that the model could handle a variety of data types and integrate seamlessly with other components of the platform.At the same time, the stream processing technology is used to integrate the real-time monitoring and analysis function of the platform, so as to make rapid prediction and classification of the newly acquired liver fibrosis case data.The accuracy and stability of the model were further verified in the multi-center clinical data, and the multi-modal big data liver fibrosis early warning cloud platform system was built
Measured: 2025.1-2025.12
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jul 2024
- Study start20 Jul 2024 (estimated)
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Liver FibrosisStudies of the same condition, open and under way.
- 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
- Cross-sectional
- Enrolment
- 700 participants (estimated)
- Target duration
- 4 Months
Sponsor and investigators
- Lead sponsor
- Huang Haijun
- Sponsor type
- University, hospital or other
- Collaborators
- East China University of Science and Technology
- Responsible party
- Sponsor-investigator, Huang Haijun, Zhejiang Provincial People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06509230
- Sponsor's protocol ID
- KY2024072
- Acronym
- PSLF
- Status last verified
- Jul 2024
- First submitted
- 10 Jul 2024
- Last updated
- 19 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 19 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Digital Image Fusion Technology Based on Deep Learning to Predict Significant Liver Fibrosis and Its Application in Multi-center Research
- ClinicalTrials.gov ID
- NCT06509230
- Acronym / sponsor ID
- PSLF / KY2024072
- Conditions
- Liver Fibrosis
- Keywords
- Chronic hepatitis, Liver fibrosis, Artificial intelligence, Auxiliary diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 700 participants (estimated)
- Lead sponsor
- Huang HaijunUniversity, hospital or other
- Collaborators
- East China University of Science and Technology
- Study start
- 20 Jul 2024 (estimated)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 19 Jul 2024 (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.
The deep learning method based on convolutional neural network (CNN) was used to extract the relevant features of liver fibrosis classification from the multi-modal information of digital pathological sections, clinical parameters and biomarkers of a large number of existing cases of liver puncture, and the U-Net architecture of CNN was used to segment and extract the features of clinical medical images.
Detailed description
Patients with chronic hepatitis B underwent B-ultrasound-guided liver biopsy, and were divided into mild liver fibrosis group (fibrosis grade 0-1, S1), significant liver fibrosis group (fibrosis grade 2, S2), advanced liver fibrosis group and early cirrhosis group (fibrosis grade 3-4, S3-4) according to the pathological results.In this study, 200 patients with different degrees of liver fibrosis and 200 normal volunteers were collected from 2018 to 2022, and their clinical biochemical data, imaging data and peripheral blood samples were collected.The pathological microenvironment characteristics, imaging characteristics, clinical parameter characteristics and other data of patients were extracted, and the distillation learning method based on teacher-student model was adopted to develop and construct a multi-modal big data analysis model for accurate grading of liver fibrosis, so as to achieve a non-invasive intelligent grading diagnosis system for liver fibrosis.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Target follow-up
- 4 Months
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| mild fibrosis | – | –S0-1 |
| significant liver fibrosis | – | –S2 |
| Advanced liver fibrosis | – | –S3-4 |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- A patient with chronic hepatitis B liver fibrosis confirmed by liver biopsy
Key inclusion criteria (3 listed)
- Age of 18-60 years old
- The diagnosis of chronic hepatitis B is in line with the diagnostic criteria of China's 2019 Chronic Hepatitis B Prevention and Treatment Guidelines, and the diagnosis of non-alcoholic fatty liver is in line with the Asian Pacific Hepatology Association guidelines
- Imaging showed no liver cancer
Key exclusion criteria (2 listed)
- There are contraindications for liver biopsy
- Liver pathology did not meet the criteria
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 |
|---|---|---|
| The fibrosis grades were grouped without drug intervention | Other | Advanced liver fibrosis, mild fibrosis, significant liver fibrosis |
Primary outcome
Model development
Time frame: 2024.6-2024.12
Imaging (such as CT scan, MRI, X-ray, etc.) features and clinical parameters of patients were extracted, including population baseline characteristics (such as age, gender, comorbiditions, etc.), blood biochemical indicators (such as blood glucose, lipids, liver function indicators, etc.), and blood cytology indicators (such as white blood cell count, red blood cell count, etc.). Completed case selection and cohort establishment, multi-modal feature extraction and model development
1 secondary outcome
Build a multi-modal big data liver fibrosis early warning cloud platform system
Time frame: 2025.1-2025.12
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Jul 2024.
- Individual participant dataSharing planned: Undecided
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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.
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 Liver Fibrosis, run by Huang Haijun 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 2 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: looking for participants now, as of the sponsor's last update on 19 Jul 2024. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Haijun Huang (13758186635, huanghaijun0826@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Model development (time frame: 2024.6-2024.12). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 19 Jul 2024.