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

Abstract

Objectives 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. Materials and methods 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. Results 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. Conclusion 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.

Radiomics of Liver Metastases: A Systematic Review

“…Most papers ( n = 28) described retrospective analyses, while four reported planned secondary analyses of prospectively acquired data [ 32 , 33 , 34 , 35 ]. Nineteen authors analyzed computed tomography (CT) [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], eight magnetic resonance imaging (MRI) [ 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], three positron-emission tomography (PET)/CT [ 59 , 60 , 61 ], and two multiple imaging modalities (CT and MRI; PET and MRI, respectively) [ 62 , 63 ]. Various software applications were used for texture analysis, with these being custom-made in a large proportion of cases ( n = 10).…”

“…Four studies investigated whether radiomic features could discriminate LM from other hepatic lesions. Jansen et al, analyzed metastases, primary hepatic tumors, and benign lesions (adenomas, cysts, and hemangiomas) on MRI images [ 52 ]. A model using, among other features, the time to peak histograms and the sum of squared variance could distinguish different liver lesions.…”

“…Inter-observer agreement was assessed by four studies [ 33 , 52 , 54 , 60 ]. Although they used different indices, such as K-statistic, intra-class correlation, and correlation index ( r -value), all studies reported a substantial or excellent agreement among different readers.…”

“…In the diagnostic field of research, radiomics can distinguish different types of hepatic lesions, differentiating metastases from benign lesions and primary tumors [ 48 , 51 , 52 , 53 ]. LM appear to be characterized by a high gray level entropy, heterogeneity, and variance.…”

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

“…Other studies have associated additional MRI sequences and risk factors plus the patient’s clinical data to apply an automated classification system cataloguing liver lesions as adenoma, cyst, hemangioma, HCC and metastasis, with a sensitivity and specificity of 0.8/0.78, 0.93/0.93, 0.84/0.82, 0.73/0.56 and 0.62/0.77, respectively[ 18 ]. Zhang et al[ 19 ] described a training model using MRI in 20 patients to classify liver tissues.…”