Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Diagnostic Performance of an Artificial Intelligence Model Based on Contrast-Enhanced Ultrasound in Patients with Liver Lesions: A Comparative Study with Clinicians

Contrast-enhanced ultrasound (CEUS) is widely used in the characterization of liver tumors; however, the evaluation of perfusion patterns using CEUS has a subjective character. This study aims to evaluate the accuracy of an automated method based on CEUS for classifying liver lesions and to compare its performance with that of two experienced clinicians. The system used for automatic classification is based on artificial intelligence (AI) algorithms. For an interpretation close to the clinical setting, both clinicians knew which patients were at high risk for hepatocellular carcinoma (HCC), but only one was aware of all the clinical data. In total, 49 patients with 59 liver tumors were included. For the benign and malignant classification, the AI model outperformed both clinicians in terms of specificity (100% vs. 93.33%); still, the sensitivity was lower (74% vs. 93.18% vs. 90.91%). In the second stage of multiclass diagnosis, the automatic model achieved a diagnostic accuracy of 69.93% for HCC and 89.15% for liver metastases. Readers demonstrated greater diagnostic accuracy for HCC (83.05% and 79.66%) and liver metastases (94.92% and 96.61%) compared to the AI system; however, both were experienced sonographers. The AI model could potentially assist and guide less-experienced clinicians to discriminate malignant from benign liver tumors with high accuracy and specificity.

Artificial intelligence for the classification of focal liver lesions in ultrasound - a systematic review

Focal liver lesions are detected in about 15% of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas, or typical focal fatty changes. In the case of focal liver lesions which remain unclear on B-mode ultrasound, contrast-enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction between benign and malignant liver lesions. Artificial intelligence describes applications that try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the benign or malignant nature and entity of focal liver lesions on the basis of B-mode or CEUS data. In a structured search on Scopus, Web of Science, PubMed, and IEEE, we found 52 studies that met the inclusion criteria. Studies showed good diagnostic performance for both the classification as benign or malignant and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited.

Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review

Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (p = 0.004). Different AI methods have been adopted in the included articles correlated with 19 articles studying CT (41.30%), 20 studying US (43.47%), and 7 studying MRI (15.21%). No article has discussed the use of artificial intelligence in PET and X-ray technology. Our systematic approach has shown that previous works in HCC detection and characterization have assessed the comparability of conventional interpretation with machine learning using US, CT, and MRI. The distribution of the imaging techniques in our analysis reflects the usefulness and evolution of medical imaging for the diagnosis of HCC. Moreover, our results highlight an imminent need for data sharing in collaborative data repositories to minimize unnecessary repetition and wastage of resources.

Early hemodynamics of hepatocellular carcinoma using contrast-enhanced ultrasound with Sonazoid: focus on the pure arterial and early portal phases

To clarify the early hemodynamics of hepatocellular carcinoma (HCC), we defined the early portal phase of contrast-enhanced ultrasound (CEUS) and examined the reliability of this modality for determining HCC differentiation. Starting in 2007, we performed Sonazoid CEUS in 146 pathologically confirmed hepatic nodules; 118 HCC (8 poorly [Pd], 73 moderately [Md] and 37 well-differentiated [Wd]) and 28 benign nodules. We focused on the pure arterial and early portal phases up to 45 seconds after Sonazoid injection, and then the subsequent phase up to 30 minutes. We calculated covariance-adjusted sensitivities for nodule enhancement combinations of these three phases. Nodule enhancements were divided into hypo, iso and hyper. A positive predictive value of 100% was obtained for the following patterns: iso-iso-hypo, hypo-iso-iso, and hypo-hypo-hypo for Wd, hyper-iso-hypo and hyper-hypo-hypo for Md, hypo-hyper-hypo for Pd, and hyper-hyper-hyper for benign nodules. In Wd HCC (early HCC), there were seven enhancement patterns, thought to be characterized by various hemodynamic changes from early to advanced HCC. Two patterns allowing a diagnosis of Wd HCC were hypo in the pure arterial phase. Subsequent iso-enhancement in the early portal phase indicated a portal blood supply. Decreased enhancement in the early portal phase allows a diagnosis of Md HCC. However, gradual enhancement observed from the pure arterial to the early portal phase allows a diagnosis of Pd HCC. Therefore, even in the early portal phase, hemodynamic changes were visible not only in Wd but also in Md and Pd HCC. In conclusion, with division of the early phase hemodynamics into pure arterial and early portal phases, CEUS can provide information useful for determining the likely degree of HCC differentiation and for distinguishing early stage HCC from benign nodules.

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

OBJECTIVES

To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.

METHODS

A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.

RESULTS

The DLS demonstrated a 92% accuracy, a 92% sensitivity (Sn), and a 98% specificity (Sp). Test set performance in a single run of random unseen cases showed an average 90% Sn and 98% Sp. The average Sn/Sp on these same cases for radiologists was 82.5%/96.5%. Results showed a 90% Sn for classifying hepatocellular carcinoma (HCC) compared to 60%/70% for radiologists. For HCC classification, the true positive and false positive rates were 93.5% and 1.6%, respectively, with a receiver operating characteristic area under the curve of 0.992. Computation time per lesion was 5.6 ms.

CONCLUSION

This preliminary deep learning study demonstrated feasibility for classifying lesions with typical imaging features from six common hepatic lesion types, motivating future studies with larger multi-institutional datasets and more complex imaging appearances.

KEY POINTS

• Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists. • Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.

Automated diagnosis of focal liver lesions using bidirectional empirical mode decomposition features

Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.

Computer-aided diagnosis for estimating the malignancy grade of hepatocellular carcinoma using contrast-enhanced ultrasound: an ROC observer study

BACKGROUND & AIMS

We are developing a computer-aided diagnosis (CAD) system for estimating the malignancy grade of hepatocellular carcinoma (HCC) using contrast-enhanced ultrasound (CEUS). In this study, observers estimated the malignancy grade of HCC with and without the cues provided by CAD.

MATERIALS AND METHODS

Institutional review board approval was obtained and informed consent was waived. A total of 232 histologically confirmed HCCs were studied: 76 well-differentiated HCC (w-HCC), 133 moderately differentiated HCC (m-HCC), and 23 poorly differentiated HCC (p-HCC). In this observer study, CEUS vascular images acquired using the maximum intensity projection technique were displayed together with static B-mode and Kupffer-phase (defined as 10 min after injection) images. Five hepatologists independently assigned confidence ratings for the malignancy grade of each HCC. Each hepatologist first read each case without CAD and then immediately afterwards with CAD. The observers' rating data were evaluated by multireader multicase receiver operating characteristic (ROC) analysis.

RESULTS

The overall sensitivity of our CAD system for discrimination between three histological differentiation grades of HCC was 87.5% (203/232). For discrimination between w-HCC and m/p-HCC, the mean area under the ROC curve (AUC) for the five observers was significantly improved from 0.779 ± 0.074 to 0.872 ± 0.090 with CAD (P = 0.0069). For discrimination between m-HCC and p-HCC, the mean AUC was also significantly improved from 0.713 ± 0.107 to 0.863 ± 0.101 with CAD (P = 0.0321).

CONCLUSION

The use of our CAD system can significantly improve the diagnostic performance of hepatologists in discriminating between three histological differentiation grades of HCC using CEUS.