Assessment of drainage vein of focal nodular hyperplasia using contrast-enhanced ultrasound.

PURPOSE: Identification of drainage vessels is useful for differential diagnosis of hepatic tumors. Direct drainage to the hepatic vein has been reported to occur in focal nodular hyperplasia (FNH), but studies evaluating the drainage veins of FNH are limited. We aimed to investigate the detection rate of the FNH drainage vein and the factors related to visualization of the drainage vein on contrast-enhanced ultrasound (CEUS). METHODS: Fifty consecutive patients with 50 FNH lesions were retrospectively evaluated in this study. We calculated and compared the detection rate of the FNH drainage vein on CEUS, contrast-enhanced magnetic resonance imaging (CEMRI), and contrast-enhanced computed tomography (CECT), and identified the factors correlated with visualization of the FNH drainage vein on CEUS by using multivariate logistic regression analyses. RESULTS: Visualization of the drainage vein was confirmed in 31 of 50 lesions (62%) using CEUS, three of 44 lesions (6.8%) using CEMRI, and one of 18 lesions (5.6%) using CECT. The detection rate of the FNH drainage vein on CEUS was significantly higher than that on CEMRI and CECT (p < 0.001). Multivariate analysis identified lesion size (≥ 25 mm) and detection of the spoke-wheel pattern on Doppler US as independent factors for drainage vein detection in FNH. CONCLUSION: Our study showed that rapid FNH drainage to the hepatic vein was observed at a relatively high rate on CEUS, suggesting that CEUS focusing on detection of drainage veins is important for diagnosing FNH.

Robustness assessment of an automated AI-based white blood cell morphometric analysis system using different smear preparation methods.

INTRODUCTION: Numerous AI-based systems are being developed to evaluate peripheral blood (PB) smears, but the feasibility of these systems on different smear preparation methods has not been fully understood. In this study, we assessed the impact of different smear preparation methods on the robustness of the deep learning system (DLS). METHODS: We collected 193 PB samples from patients, preparing a pair of smears for each sample using two systems: (1) SP50 smears, prepared by the DLS recommended fully automated slide preparation with double fan drying and staining (May-Grunwald Giemsa, M-G) system using SP50 (Sysmex) and (2) SP1000i smears, prepared by automated smear preparation with single fan drying by SP1000i (Sysmex) and manually stained with M-G. Digital images of PB cells were captured using DI-60 (Sysmex), and the DLS performed cell classification. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the performance of the DLS. RESULTS: The specificity and NPV for all cell types were 97.4%-100% in both smear sets. The average sensitivity and PPV were 88.9% and 90.1% on SP50 smears, and 87.0% and 83.2% on SP1000i smears, respectively. The lower performance on SP1000i smears was attributed to the intra-lineage misclassification of neutrophil precursors and inter-lineage misclassification of lymphocytes. CONCLUSION: The DLS demonstrated consistent performance in specificity and NPV for smears prepared by a system different from the recommended method. Our results suggest that applying an automated smear preparation system optimized for the DLS system may be important.