AI Article Synopsis
- The study aimed to investigate how effective radiomics analysis using multimodal MRI is for assessing advanced fibrosis in hepatitis B patients.
- The researchers divided 143 patients into training and validation groups, developing a clinical model and a radiomics signature with support vector machine (SVM) for diagnosis.
- The results showed that a nomogram combining these factors had the highest accuracy in predicting advanced liver fibrosis, indicating that radiomics analysis can significantly improve diagnosis.
Article Abstract
Background: The goal was to explore the value of using radiomics analysis based on multimodal MRI for evaluating the advanced fibrosis in patients with hepatitis B.
Methods: One hundred and forty-three patients with hepatitis B fibrosis were randomly divided into training and validation cohorts in a 2:1 ratio. In the training cohort, a clinical model was established with logistic regression, a radiomics signature based on multimodal MRI was established with support vector machine (SVM), and a nomogram integrated the radiomics signature and clinical factors. The value of three models was assessed by ROC analysis in the training and validation cohorts.
Results: The nomogram demonstrated the largest area under the ROC curve. The nomogram presented good agreement in the prediction probability of advanced liver fibrosis in two cohorts.
Conclusions: Radiomics analysis has good diagnostic value for advanced liver fibrosis and the nomogram can enhance the diagnostic value.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.7754/Clin.Lab.2023.221117 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!