A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging

• Published in: Medical physics (Lancaster) Vol. 43; no. 3; pp. 1428 - 1436
• Main Authors: Gatos, Ilias, Tsantis, Stavros, Spiliopoulos, Stavros, Karnabatidis, Dimitris, Theotokas, Ioannis, Zoumpoulis, Pavlos, Loupas, Thanasis, Hazle, John D., Kagadis, George C.
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.03.2016
• Subjects: Adolescent; Adult; Aged; Analysis of texture; Biological material, e.g. blood, urine; Haemocytometers; biomechanics; biomedical ultrasonics; Chronic Disease; classification; Computer aided diagnosis; Computer software; Diagnosis using ultrasonic, sonic or infrasonic waves; Diagnosis, Computer-Assisted - methods; Digital computing or data processing equipment or methods, specially adapted for specific applications; diseases; elastic constants; Elastic properties; Elasticity; Elasticity Imaging Techniques - methods; feature selection; Female; fibrosis; Humans; Image analysis; image classification; image colour analysis; Image data processing or generation, in general; image texture; In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines; Inference methods or devices; liver; Liver Diseases - diagnostic imaging; Male; Mechanical Phenomena; medical image processing; Middle Aged; Radiologists; regression analysis; sensitivity analysis; shear wave elastography; support vector machines; Tissues; Ultrasonographic imaging; Ultrasonography; Young Adult; classification; fibrosis; shear wave elastography
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1118/1.4942383

Purpose: Classify chronic liver disease (CLD) from ultrasound shear-wave elastography (SWE) imaging by means of a computer aided diagnosis (CAD) system. Methods: The proposed algorithm employs an inverse mapping technique (red-green-blue to stiffness) to quantify 85 SWE images (54 healthy and 31 with CLD). Texture analysis is then applied involving the automatic calculation of 330 first and second order textural features from every transformed stiffness value map to determine functional features that characterize liver elasticity and describe liver condition for all available stages. Consequently, a stepwise regression analysis feature selection procedure is utilized toward a reduced feature subset that is fed into the support vector machines (SVMs) classification algorithm in the design of the CAD system. Results: With regard to the mapping procedure accuracy, the stiffness map values had an average difference of 0.01 ± 0.001 kPa compared to the quantification results derived from the color-box provided by the built-in software of the ultrasound system. Highest classification accuracy from the SVM model was 87.0% with sensitivity and specificity values of 83.3% and 89.1%, respectively. Receiver operating characteristic curves analysis gave an area under the curve value of 0.85 with [0.77–0.89] confidence interval. Conclusions: The proposed CAD system employing color to stiffness mapping and classification algorithms offered superior results, comparing the already published clinical studies. It could prove to be of value to physicians improving the diagnostic accuracy of CLD and can be employed as a second opinion tool for avoiding unnecessary invasive procedures.