Journal Article

Automatic classification of focal liver lesions based on MRI and risk factors

Wessels, Frank J., Viergever, Max A., Pluim, Josien P. W., Kuijf, Hugo J., Veldhuis, Wouter B., Jansen, Mariëlle J. A.

2019

Overview

Accurate classification of focal liver lesions is an important part of liver disease diagnostics. In clinical practice, the lesion type is often determined from the abdominal MR examination, which includes T2-weighted and dynamic contrast enhanced (DCE) MR images. To date, only T2-weighted images are exploited for automatic classification of focal liver lesions. In this study additional MR sequences and risk factors are used for automatic classification to improve the results and to make a step forward to a clinically useful aid for radiologists. Clinical MRI data sets of 95 patients with in total 125 benign lesions (40 adenomas, 29 cysts and 56 hemangiomas) and 88 malignant lesions (30 hepatocellular carcinomas (HCC) and 58 metastases) were included in this study. Contrast curve, gray level histogram, and gray level co-occurrence matrix texture features were extracted from the DCE-MR and T2-weighted images. In addition, risk factors including the presence of steatosis, cirrhosis, and a known primary tumor were used as features. Fifty features with the highest ANOVA F-score were selected and fed to an extremely randomized trees classifier. The classifier evaluation was performed using the leave-one-out principle and receiver operating characteristic (ROC) curve analysis. The overall accuracy for the classification of the five major focal liver lesion types is 0.77. The sensitivity/specificity is 0.80/0.78, 0.93/0.93, 0.84/0.82, 0.73/0.56, and 0.62/0.77 for adenoma, cyst, hemangioma, HCC, and metastasis, respectively. The proposed classification system using features derived from clinical DCE-MR and T2-weighted images, with additional risk factors is able to differentiate five common types of lesions and is a step forward to a clinically useful aid for focal liver lesion diagnosis.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adenoma / Adenoma - diagnostic imaging / Algorithms / Analysis / Area Under Curve / Automatic classification / Automation / Cancer / Cancer metastasis / Carcinoma, Hepatocellular - diagnostic imaging / Cirrhosis / Classification / Classifiers / Cysts / Cysts - diagnostic imaging / Diagnosis, Computer-Assisted - methods / Diagnostic imaging / False Positive Reactions / Fatty liver / Feasibility studies / Feature extraction / Gadobutrol / Hemangioma / Hemangioma - diagnostic imaging / Hepatocellular carcinoma / Histograms / Humans / Image classification / Image contrast / Image enhancement / Lesions / Liver / Liver - diagnostic imaging / Liver cancer / Liver cirrhosis / Liver diseases / Liver Neoplasms - diagnostic imaging / Machine Learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Medical imaging / Medical research / Medicine and Health Sciences / Metastases / Metastasis / Methods / NMR / Nuclear magnetic resonance / Patients / Pattern Recognition, Automated / People and Places / Radiology - methods / Reproducibility of Results / Research and Analysis Methods / Risk analysis / Risk assessment / Risk Factors / ROC Curve / Sensitivity and Specificity / Steatosis / Three dimensional imaging / Tumors / Variance analysis