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Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Justified granulation aided noninvasive liver fibrosis classification system
Marcin Bernas1, Tomasz Orczyk2, Joanna Musialik3,4
1Institute of Computer Science, Faculty of Computer Science and Material Science, University of Silesia in Katowice, Katowice, Poland. marcin.bernas@us.edu.pl.
Insights
A novel data mining technique accurately stages liver fibrosis using routine blood tests, offering a robust, non-invasive alternative to liver biopsy for hepatitis C patients. This method aids in diagnosis and treatment planning.
Area of Science:
- Medical Informatics
- Hepatology
- Data Mining
Background:
- Chronic hepatitis C virus infection affects millions globally, potentially leading to liver cirrhosis and death.
- Accurate staging of liver fibrosis is crucial for patient management but traditional liver biopsy is invasive.
- Existing non-invasive tests show variable accuracy, necessitating improved diagnostic methods.
Purpose of the Study:
- To develop and validate a data mining and classification technique for staging liver fibrosis.
- To utilize easily accessible laboratory blood test data for fibrosis staging.
- To provide a robust and non-invasive diagnostic support tool for clinicians.
Main Methods:
- A granular model was developed using routine laboratory blood tests (morphology, coagulation, biochemistry, protein electrophoresis).
- Histopathology records of liver biopsy served as the reference standard.
- The model employs an aggregation method and voting procedure, robust to missing data.
Main Results:
- The model achieved an overall accuracy of 67.9% on a dataset of 290 hepatitis C patients.
- Validation on a separate dataset of 365 patients with various liver diseases confirmed robustness.
- Error rates for misclassifying early and late fibrosis stages were below 6.5%.
Conclusions:
- The proposed system effectively supports physicians in staging liver fibrosis in chronic hepatitis C.
- Its human-centric, interval-based approach allows for specialist verification.
- The system offers a robust, non-invasive diagnostic tool for real-world clinical application.
Background:
According to the World Health Organization 130-150 million (according to WHO) of people globally are chronically infected with hepatitis C virus. The virus is responsible for chronic hepatitis that ultimately may cause liver cirrhosis and death. The disease is progressive, however antiviral treatment may slow down or stop its development. Therefore, it is important to estimate the severity of liver fibrosis for diagnostic, therapeutic and prognostic purposes. Liver biopsy provides a high accuracy diagnosis, however it is painful and invasive procedure. Recently, we witness an outburst of non-invasive tests (biological and physical ones) aiming to define severity of liver fibrosis, but commonly used FibroTest®, according to an independent research, in some cases may have accuracy lower than 50 %. In this paper a data mining and classification technique is proposed to determine the stage of liver fibrosis using easily accessible laboratory data.
Methods:
Research was carried out on archival records of routine laboratory blood tests (morphology, coagulation, biochemistry, protein electrophoresis) and histopathology records of liver biopsy as a reference value. As a result, the granular model was proposed, that contains a series of intervals representing influence of separate blood attributes on liver fibrosis stage. The model determines final diagnosis for a patient using aggregation method and voting procedure. The proposed solution is robust to missing or corrupted data.
Results:
The results were obtained on data from 290 patients with hepatitis C virus collected over 6 years. The model has been validated using training and test data. The overall accuracy of the solution is equal to 67.9 %. The intermediate liver fibrosis stages are hard to distinguish, due to effectiveness of biopsy itself. Additionally, the method was verified against dataset obtained from 365 patients with liver disease of various etiologies. The model proved to be robust to new data. What is worth mentioning, the error rate in misclassification of the first stage and the last stage is below 6.5 % for all analyzed datasets.
Conclusions:
The proposed system supports the physician and defines the stage of liver fibrosis in chronic hepatitis C. The biggest advantage of the solution is a human-centric approach using intervals, which can be verified by a specialist, before giving the final decision. Moreover, it is robust to missing data. The system can be used as a powerful support tool for diagnosis in real treatment.
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