Currently, with the development of laboratory and imaging means for staging the fibrotic evolution of chronic liver diseases, clinical validation has highlighted the fact that, overall, liver biopsy is probably an imperfect gold standard [15
]. Actually, even a 25
mm long liver biopsy has a 25% rate of discordance for fibrosis staging [16
]. Therefore, liver biopsy is prone to sampling errors and to intraobserver and interobserver variability [17
]. Also, when the specimen size is adequate, the level of experience of the pathologist may even be more important [19
]. Invasive procedures are not suitable for regular clinical monitoring of disease progression. Even though there is a high prevalence of chronic liver disease worldwide and it represents a significant public health problem, liver biopsy is obviously not appropriate for screening liver fibrosis and cirrhosis.
Liver fibrosis is a kind of diffuse lesion involved in multiple structures of the liver. Several factors are taken into account, either by imaging or laboratory tests, before the diagnosis of liver fibrosis or cirrhosis can be made. When multiple and diverse factors are likely to influence decision making, computer-based decision support systems, such as neural networks, are capable of handling large amounts of data and are helpful in arriving at or supplementing a correct decision by clinicians [20
]. ANNs have been used in medicine for various purposes, including prediction of mortality of patients with cirrhosis of the liver [22
]. An intelligent mode was also compared to MELD scoring, Child–Pugh’s scoring and other conventional logistic regression models and performance of an ANN was significantly better than those of the models.
Real time ultrasonography has become an integral part of the non-invasive evaluation of chronic liver disease in many clinical settings while the search for a non-invasive imaging marker for staging liver fibrosis or cirrhosis is inactive. The performance of ultrasonographic imaging as a non-invasive diagnostic or prognostic modality for liver fibrosis or cirrhosis, as well as for correlation with histological changes and functional disorders of the liver, remains controversial and is still debated. However, recent advances in ultrasound technology have improved the diagnostic accuracy of fibrosis in patients with chronic liver disease. Aube et al. [24
] studied a high-resolution ultrasound probe of the liver parenchyma, liver surface smoothness, spleen size and portal vein blood flow rate, using 11 indicators in ultrasonic testing, and found an accuracy of 82
88% for surface nodular changes in the liver and spleen thickness in the diagnosis of cirrhosis of the liver.
In the present study, we constructed a multi-parameter dependent diagnostic model based on ultrasound in order to avoid the shortcomings of a single-parameter decision making model of ultrasonography. We took several ultrasonographic variables into consideration including grey-scale and Doppler indexes such as the liver parenchyma, liver edge, PVVel, and HAPI. Secondly, according to the different treatment principles for liver fibrosis and liver cirrhosis, we divided the patients into two groups: the fibrotic group (F1-F3 stage) and the cirrhotic group (F4 stage). Variables like the liver parenchyma, liver envelope, ascites and HV waveform were graded from 0 to 2 according to the imaging changes in different stages. The variables were quantitatively described, for example, as PVVel, HAPI, HARI and DI, and were compared by the actual values. DI was used to quantitatively assess the extent of the abnormal HV waveform. The relatively large number of intra- and extra-hepatic variables was considered in the study to work with the largest possible amount of information. In fact, data collection was performed by trying to include all variables that could have a connection with the problem. However, some of these variables may contain confusing information, or even completely irrelevant information. Selecting the significant variables after statistical analysis can increase diagnostic accuracy as well as sensitivity and specificity. Some experts would consider non-invasive serum tests of fibrosis with AUC-ROC values of 0.85 to 0.90 to be as good as a liver biopsy for staging fibrosis [25
]. In our study, the diagnostic performance achieved by the ultrasound-based ANN was considered as having AUC-ROC values around 0.92.
Limitation of the study
This study has several limitations that must be taken into account. Firstly, the ultrasound variables in the present study did not fully cover all involved parameters, although some of these variables could have contributed to improvement of the ANN. The ANN model was constructed using 10 variables as the proposed input neurons. This Secondly, the number of patients was limiting. In an ANN model, each group should have 100 patients to avoid the risk of overfitting the data. This was not fully achieved for the validation group (60 of 239 patients). Finally, we could not evaluate the accuracy of pathological diagnosis caused by sampling error or variation in the experience of the pathologists.