Development of a classification method for mild liver fibrosis using non-contrast CT image

• Published in: International Journal of Computer Assisted Radiology and Surgery Vol. 17; no. 11; pp. 2041 - 2049
• Main Authors: Hirano, Ryo, Rogalla, Patrik, Farrell, Christin, Hoppel, Bernice, Fujisawa, Yasuko, Ohyu, Shigeharu, Hattori, Chihiro, Sakaguchi, Takuya
• Format: Journal Article
• Language: English; Japanese
• Published: Cham Springer Science and Business Media LLC 01.11.2022; Springer International Publishing; Springer Nature B.V
• Subjects: Algorithms; Biopsy; Computed tomography; Computer Imaging; Computer Science; Fibrosis; Health Informatics; Health services; Humans; Image classification; Image contrast; Imaging; Liver; Liver Cirrhosis; Mathematical analysis; Mean; Medical imaging; Medicine; Medicine & Public Health; Original Article; Patients; Pattern Recognition and Graphics; Performance evaluation; Radiology; Regularization; ROC Curve; Sensitivity; Standard deviation; Surgery; Tomography, X-Ray Computed; Variance; Vision; Logistic regression; Non-contrast computed tomography; Texture analysis; Liver fibrosis; Classification
• ISSN: 1861-6429; 1861-6410; 1861-6429
• DOI: 10.1007/s11548-022-02724-x

Purpose Detection 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. Methods Image 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”. Results The 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. Conclusion A 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.