Comments and illustrations of the WFUMB CEUS liver guidelines: Rare benign focal liver lesion, part I.

Improved detection and characterization of common focal liver lesions (FLL) are the main topics of the World Federation for Ultrasound in Medicine and Biology (WFUMB) guidelines on the use of contrast-enhanced ultrasound (CEUS). On stateof-the-art CEUS imaging, to create a library of rare FLL, especially concerning their atypical imaging characteristics, might be helpful for improving clinical diagnostic efficiency. In this review, we aim to summarize the ultrasound and CEUS features of rare benign FLL. Currently there are limited reports and images published.

Point shear wave speed measurement in differentiating benign and malignant focal liver lesions.

AIM: To investigate the value of ElastPQ measurement for differential diagnosis of benign and malignant focal liver lesions (FLLs) by using histologic results as a reference standard. MATERIAL AND METHODS: A total of 154 patients were included. ElastPQ measurement was performed for each lesion in which the shear wave speed (SWS) was measured. The difference in SWS and SWS ratio of FLL to surrounding liver were evaluated, and the cut off value was investigated. Receiver operating characteristic (ROC) curve was plotted to evaluate the diagnostic performance. Histology as a gold standard was obtained by surgery in all patients. RESULTS: A total of 154 lesions including 129 (83.7 %) malignant FLLs and 25 (16.3 %) benign ones were analysed. The SWS of malignant and benign FLLs was significantly different, 2.77±0.68 m/s and 1.57±0.55 m/s (p<0.05). The SWS ratio of each FLL to surrounding liver parenchyma was 2.23±0.49 for malignant and 1.14±0.36 for benign FLLs (p<0.05). The cut off value for differential diagnosis was 2.06 m/s for SWS and 1.67 for SWS ratio. CONCLUSIONS:  ElastPQ measurement provides reliable quantitative stiffness information of FLLs and may be helpful in the differential diagnosis between malignant and benign FLLs.

Ultrasound evaluation of mediastinal lymphadenopathy in patients with sarcoidosis.

AIM: Mediastinal lymphadenopathy is a typical feature of pulmonary sarcoidosis and an important parameter for diagnosis and follow-up. The present feasibility study is the first to elucidate the role of transthoracic mediastinal ultrasonography (US) for evaluation and staging of lymphadenopathy in patients with sarcoidosis. MATERIAL AND METHOD: Fifty patients with sarcoidosis were subjected to high-definition mediastinal US. The sonographic lymph node status was compared with the radiologic staging - the prevailing gold standard. RESULTS: Mediastinal regions and landmarks could reliably be assessed by ultrasound in 45/50 (90%) of sarcoidosis patients. Lymphadenopathy was sonographically documented in 29/50 (58%) of the patients (sensitivity 89%, specificity 76%, PPV 86%, NPV 81%, accuracy 84%). There was a marked concordance between US confirmation of lymphadenopathy and radiologic staging (k=0.67, p<0.001). CONCLUSIONS: Transthoracic US qualifies for the demonstration of the mediastinal regions and lymphadenopathy in patients with sarcoidosis. The procedure is facilitated by frequent and distinct mediastinal lymph node enlargement due to sarcoidosis. Prospective studies are required to find out whether mediastinal US adds value to conventional radiologic staging and provides a clinically advantage, particularly in the follow-up of patients with sarcoidosis.

Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses.

BACKGROUND & AIMS: By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis. METHODS: We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns. RESULTS: The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%-92.42%) and 84.27% testing accuracy (95% CI, 83.09%-85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%-0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85. CONCLUSIONS: Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses.