Contrast-enhanced ultrasonography parameters in neural network diagnosis of liver tumors

Abstract

Aim: To study the role of time-intensity curve (TIC) analysis parameters in a complex system of neural networks designed to classify liver tumors.

Methods: We prospectively included 112 patients with hepatocellular carcinoma (HCC) (n = 41), hypervascular (n = 20) and hypovascular (n = 12) liver metastases, hepatic hemangiomas (n = 16) or focal fatty changes (n = 23) who underwent contrast-enhanced ultrasonography in the Research Center of Gastroenterology and Hepatology, Craiova, Romania. We recorded full length movies of all contrast uptake phases and post-processed them offline by selecting two areas of interest (one for the tumor and one for the healthy surrounding parenchyma) and consecutive TIC analysis. The difference in maximum intensities, the time to reaching them and the aspect of the late/portal phase, as quantified by the neural network and a ratio between median intensities of the central and peripheral areas were analyzed by a feed forward back propagation multi-layer neural network which was trained to classify data into five distinct classes, corresponding to each type of liver lesion.

Results: The neural network had 94.45% training accuracy (95% CI: 89.31%-97.21%) and 87.12% testing accuracy (95% CI: 86.83%-93.17%). The automatic classification process registered 93.2% sensitivity, 89.7% specificity, 94.42% positive predictive value and 87.57% negative predictive value. The artificial neural networks (ANN) incorrectly classified as hemangyomas three HCC cases and two hypervascular metastases, while in turn misclassifying four liver hemangyomas as HCC (one case) and hypervascular metastases (three cases). Comparatively, human interpretation of TICs showed 94.1% sensitivity, 90.7% specificity, 95.11% positive predictive value and 88.89% negative predictive value. The accuracy and specificity of the ANN diagnosis system was similar to that of human interpretation of the TICs (P = 0.225 and P = 0.451, respectively). Hepatocellular carcinoma cases showed contrast uptake during the arterial phase followed by wash-out in the portal and first seconds of the late phases. For the hypovascular metastases did not show significant contrast uptake during the arterial phase, which resulted in negative differences between the maximum intensities. We registered wash-out in the late phase for most of the hypervascular metastases. Liver hemangiomas had contrast uptake in the arterial phase without agent wash-out in the portal-late phases. The focal fatty changes did not show any differences from surrounding liver parenchyma, resulting in similar TIC patterns and extracted parameters.

Conclusion: Neural network analysis of contrast-enhanced ultrasonography - obtained TICs seems a promising field of development for future techniques, providing fast and reliable diagnostic aid for the clinician.

Keywords: Artificial neural network; Computer-aided diagnosis system; Contrast enhanced ultrasound; Hepatocellular carcinoma; Liver tumors; Time-intensity curve.

Figure 1

Study protocol. The patients were registered and contrast-enhanced ultrasonography (CEUS) was performed, with subsequent movie registration and offline time-intensity curve (TIC) analysis. Relevant parameters were fed to the artificial neural networks (ANN) which divided the dataset into training, validation and fitting lots. A back propagation and 10-fold cross-validation algorithm assured a high accuracy for the classifications obtained by the ANN system. Imax: Maximum intensities; TTP: Time to reaching peak intensities; HCC: Hepatocellular carcinoma.

Figure 2

Graphical representation of an artificial neural networks and a neuron from the 2nd hidden layer. A: The four classes of parameters are imputed to corresponding neurons in the first layer of the artificial neural networks, which in turn establish synaptic connections with all neurons of the 2nd hidden layer. These neurons provide a value for the output layer, which in turn presents the user with a classification decision; B: Neurons in the hidden layer receive multiple inputs (V) which are attributed specific weights (W) and all products between these two values are summed. The corresponding result (output) is forwarded through a transfer function of the efferent synapse. Imax: Maximum intensities; TTP: Time to reaching peak intensities.

Figure 3

Examples of contrast-enhanced ultrasonography aspects and the selection of the two regions of interest, corresponding to the liver tumor and normal parenchyma, respectively. Graphical representation of the time-intensity curve (TIC) and the most important parameters extracted and later fed to the artificial neural networks. A: Hepatocellular carcinoma (HCC)-positive contrast uptake in the early arterial phase followed by wash-out in the portal/late phase; B: Hypovascular metastasis-hypoenhancement of the tumor compared to normal parenchyma; C: Hepatic hemangyoma-absence of the wash-out and positive peak intensity; D: Focal fatty change-similar TIC parameters for the two selected areas of interest.