Georgiana Nagy1, Maria Adriana Neag2, Mihaela Gordan3, Doinita Crisan4, Mircea Petru1, Romeo Chira1. 1. 1st Department of Internal Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania. 2. Department of Pharmacology, Toxicology and Clinical Pharmacology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania. 3. Technical University of Cluj-Napoca, Romania. 4. Department of Morphopathological, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
Abstract
BACKGROUND: Assessing the diagnostic value of liver ultrasound image computerized analysis (USICA) for hepatic fibrosis (HF) staging in respect to the "gold standard" provided by liver biopsy (LB). METHODS: Two-hundred twenty-eight patients with chronic hepatopathies were prospectively enrolled in the study. All the patients underwent LB and abdominal ultrasound (US). For quantitative US assessment of HF, an image analysis software was developed and 3 parameters were extracted by wavelet processing of the region of interest: mHLlivermHHliver, mHLlivermLLliver, and mHLlivermHLspleen. To assess the relevance of each feature, the support vector machine (SVM) classifiers were employed to discriminate between the 2 severity classes (i.e., incipient F1-F2 vs advanced F3-F4 fibrosis). The statistical significance of the HF staging was assessed using SVM classifiers, in terms of sensitivity (Se), specificity (Sp), and receiver operating characteristic (ROC) curves. RESULTS: A cut-off value of 0.342 of mHLlivermHHliver allowed the discrimination between the incipient and advanced HF with 79.5% Se and 77.4% Sp, at an area under receiver operating characteristic (AUROC) value of 0.867 (P < .001). CONCLUSION: The proposed USICA using wavelet filter parameters proved to be an innovative method that is useful for the initial noninvasive evaluation and quantification of HF, with the advantages of simplicity, short calculation time, accessibility, and repeatability. The mHLlivermHHliver parameter has demonstrated good accuracy in distinguishing incipient and advanced HF and can be considered an effective non-invasive imaging marker for the assessment of HF in patients with chronic hepatic disease.
BACKGROUND: Assessing the diagnostic value of liver ultrasound image computerized analysis (USICA) for hepatic fibrosis (HF) staging in respect to the "gold standard" provided by liver biopsy (LB). METHODS: Two-hundred twenty-eight patients with chronic hepatopathies were prospectively enrolled in the study. All the patients underwent LB and abdominal ultrasound (US). For quantitative US assessment of HF, an image analysis software was developed and 3 parameters were extracted by wavelet processing of the region of interest: mHLlivermHHliver, mHLlivermLLliver, and mHLlivermHLspleen. To assess the relevance of each feature, the support vector machine (SVM) classifiers were employed to discriminate between the 2 severity classes (i.e., incipient F1-F2 vs advanced F3-F4 fibrosis). The statistical significance of the HF staging was assessed using SVM classifiers, in terms of sensitivity (Se), specificity (Sp), and receiver operating characteristic (ROC) curves. RESULTS: A cut-off value of 0.342 of mHLlivermHHliver allowed the discrimination between the incipient and advanced HF with 79.5% Se and 77.4% Sp, at an area under receiver operating characteristic (AUROC) value of 0.867 (P < .001). CONCLUSION: The proposed USICA using wavelet filter parameters proved to be an innovative method that is useful for the initial noninvasive evaluation and quantification of HF, with the advantages of simplicity, short calculation time, accessibility, and repeatability. The mHLlivermHHliver parameter has demonstrated good accuracy in distinguishing incipient and advanced HF and can be considered an effective non-invasive imaging marker for the assessment of HF in patients with chronic hepatic disease.
Authors: Naga Chalasani; Zobair Younossi; Joel E Lavine; Michael Charlton; Kenneth Cusi; Mary Rinella; Stephen A Harrison; Elizabeth M Brunt; Arun J Sanyal Journal: Hepatology Date: 2017-09-29 Impact factor: 17.425
Authors: Francesco Paparo; Francesco Corradi; Luca Cevasco; Matteo Revelli; Andrea Marziano; Lucio Molini; Giovanni Cenderello; Giovanni Cassola; Gian Andrea Rollandi Journal: Ultrasound Med Biol Date: 2014-06-25 Impact factor: 2.998