Prediction of Significant Liver Fibrosis (PSLF)

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.

Study Overview

• Status: Recruiting
• Conditions: Liver Fibrosis
• Intervention / Treatment: Other

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 Type: Observational
• Enrollment (Estimated): 700

Contacts and Locations

• Study Contact: Name: Haijun Huang; Phone Number: 13758186635; Email: huanghaijun0826@163.com

Study Locations

• China
  • Zhejiang
    • Hangzhou, Zhejiang, China, 310014
      • Recruiting
      • Haijun Huang
      • Contact: Haijun Huang; Phone Number: 13758186635; Email: huanghaijun0826@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

A patient with chronic hepatitis B liver fibrosis confirmed by liver biopsy

Description

Inclusion Criteria:

1. Age of 18-60 years old
2. 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
3. Imaging showed no liver cancer

Exclusion Criteria:

1. There are contraindications for liver biopsy
2. Liver pathology did not meet the criteria

Study Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
mild fibrosis; S0-1
significant liver fibrosis; S2
Advanced liver fibrosis; S3-4

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2024.6-2024.12

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2025.1-2025.12

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluation Multi-modal big data liver fibrosis warning platform system effectiveness	To test the effectiveness of the multi-modal big data liver fibrosis warning platform system in a real multi-center clinical environment. We will also explore the possibility of using the platform for long-term follow-up of patients, remote testing of patients and regular assessment of disease progression	2026.1-2026.12

Collaborators and Investigators

• Sponsor: Huang Haijun
• Collaborators: East China University of Science and Technology

Study record dates

Study Major Dates

• Study Start (Estimated): July 20, 2024
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: July 10, 2024
• First Submitted That Met QC Criteria: July 16, 2024
• First Posted (Actual): July 19, 2024

Study Record Updates

• Last Update Posted (Actual): July 19, 2024
• Last Update Submitted That Met QC Criteria: July 16, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Keywords: Artificial intelligence; Chronic hepatitis; Liver fibrosis; Auxiliary diagnosis
• Additional Relevant MeSH Terms: Digestive System Diseases; Pathologic Processes; Liver Diseases; Fibrosis; Liver Cirrhosis
• Other Study ID Numbers: KY2024072

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No