ArticleInternational journal of computer assisted radiology and surgery2022
Development of a classification method for mild liver fibrosis using non-contrast CT image.
Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Non-invasive liver fibrosis screening on CT images using radiomics.BMC medical imaging · 2025Article
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8 authors.
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Abstract
purposeDetection of early-stage liver fibrosis has direct clinical implications on patient management and treatment. The aim of this paper is to develop a non-invasive, cost-effective method for classifying liver disease between "non-fibrosis" (F0) and "fibrosis" (F1-F4), and to evaluate the classification performance quantitatively.
methodsImage data from 75 patients who underwent a simultaneous liver biopsy and non-contrast CT examination were used for this study. Non-contrast CT image texture features such as wavelet-based features, standard deviation of variance filter, and mean CT number were calculated in volumes of interest (VOIs) positioned within the liver parenchyma. In addition, a combined feature was calculated using logistic regression with L2-norm regularization to further improve fibrosis detection. Based on the final pathology from the liver biopsy, the patients were labelled either as "non-fibrosis" or "fibrosis". Receiver-operating characteristic (ROC) curve, area under the ROC curve (AUROC), specificity, sensitivity, and accuracy were determined for the algorithm to differentiate between "non-fibrosis" and "fibrosis".
resultsThe combined feature showed the highest classification performance with an AUROC of 0.86, compared to the wavelet-based feature (AUROC, 0.76), the standard deviation of variance filter (AUROC, 0.65), and mean CT number (AUROC, 0.84). The combined feature's specificity, sensitivity, and accuracy were 0.66, 0.88, and 0.76, respectively, showing the most promising results.
conclusionA new non-invasive and cost-effective method was developed to classify liver diseases between "non-fibrosis" (F0) and "fibrosis" (F1-F4). The proposed method makes it possible to detect liver fibrosis in asymptomatic patients using non-contrast CT images for better patient management and treatment.
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