Computed tomography angiography of liver alveococcosis: a case report.

BACKGROUND: Alveococcosis, helminthiasis caused by the larvae of Alveococcus multilocularis, is characterized by the formation of parasitic nodes in the liver. This clinical case is a rare occurrence of liver alveococcosis in Uzbekistan. CASE PRESENTATION: We present a case of a 33-year-old Asian woman from Uzbekistan who complained of discomfort in the epigastric region and right hypochondrium, along with general weakness. She had been experiencing symptoms for 5 months when a routine ultrasound examination revealed a structural formation in the liver. Patient was investigated by using contrast enhanced computed tomography (CT) and diagnosed with liver alveococcosis with multiple lesions. Patient underwent diagnostic laparotomy with obtaining gross specimen, biomaterial was examined microscopically and found that there were small fragments of fibrous tissue determined together with small groups of cystic formations and walls consisted of chitin. Moreover, contrast enhanced CT allowed us to differentiate liver alveococcosis from cavernous hemangioma, hepatocellular carcinoma, and liver metastases from unknown source. CONCLUSION: Contrast enhanced CT plays a major role in differentially diagnosing liver alveococcosis and makes it the first line method of choice for the consideration of the future treatment and surgical interventions.

Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.

PURPOSE: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests. MATERIALS AND METHODS: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO). RESULTS: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19). CONCLUSION: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.