Diagnostic Performance of Superb Microvascular Imaging in Differentiating Benign and Malignant Axillary Lymph Nodes.

ABSTRACT: The aim was to evaluate the effectiveness of superb microvascular imaging (SMI) in axillary lymph nodes (LNs).Benign and malignant LNs diagnosed via histopathological examination constituted the study subgroups. In addition to grayscale findings for morphological evaluation, vascular patterns and appearance of internal vessels were analyzed by both power Doppler ultrasound (PDUS) and SMI. The number of vascular branches was counted, and a vascularity index (VI) was calculated by SMI.Fifty-two LNs with suspicious findings in terms of metastasis (33 malignant and 19 benign) were evaluated. Diagnostic accuracy according to vascular patterns was 82% for PDUS and 92% for SMI. In the presence of asymmetric cortical thickening, there was a significant difference between benign and malignant LNs in the number of vascular branches of both thin and thick cortical sides ( P < 0.01). Mean VI was significantly higher in the malignant group ( P < 0.05). In differentiating malignancy, when a cutoff VI value was set to 9%, sensitivity was 69.7%, and specificity was 63.2%.Evaluating the vascularity of axillary LNs by SMI is a useful tool in determining the potential of axillary metastasis, especially in the absence of typical sonographic findings. Superb microvascular imaging can beneficially be used to select the most suspicious LN and suspicious area of the LN to sample.

Comparison and ensemble of 2D and 3D approaches for COVID-19 detection in CT images.

Detecting COVID-19 in computed tomography (CT) or radiography images has been proposed as a supplement to the RT-PCR test. We compare slice-based (2D) and volume-based (3D) approaches to this problem and propose a deep learning ensemble, called IST-CovNet, combining the best 2D and 3D systems with novel preprocessing and attention modules and the use of a bidirectional Long Short-Term Memory model for combining slice-level decisions. The proposed ensemble obtains 90.80% accuracy and 0.95 AUC score overall on the newly collected IST-C dataset in detecting COVID-19 among normal controls and other types of lung pathologies; and 93.69% accuracy and 0.99 AUC score on the publicly available MosMedData dataset that consists of COVID-19 scans and normal controls only. The system also obtains state-of-art results (90.16% accuracy and 0.94 AUC) on the COVID-CT-MD dataset which is only used for testing. The system is deployed at Istanbul University Cerrahpasa School of Medicine where it is used to automatically screen CT scans of patients, while waiting for RT-PCR tests or radiologist evaluation.

A new perspective on imaging of olfactory dysfunction: Does size matter?

PURPOSE: This study assesses the diagnostic utility of olfactory nerve and bulb morphologies in addition to volumetric analysis in classification of different olfactory dysfunction etiologies. METHODS: 106 patients presenting with olfactory loss and 17 control subjects were included. Based on detailed anamnesis, smell test and ear-nose-throat examination; patients were categorized into four groups as post-viral, post-traumatic, idiopathic, and obstructive olfactory dysfunction. Olfactory region was imaged with paranasal sinus CT and MRI dedicated to olfactory nerve. Olfactory bulb volume and olfactory sulcus depths were calculated on MRI. The olfactory bulb was assessed for morphology, contour lobulations and T2-signal intensity; and olfactory nerve for uniformity and clumping. RESULTS: Volumetric analysis showed decreased olfactory bulb volume in idiopathic and obstructive group compared to control subjects. Olfactory sulci were shallower in post-viral, post-traumatic, idiopathic, and obstructive group compared to the control group. In post-viral group; olfactory bulbs had lobulated contour and focal T2-hyperintense regions in 67 % of cases, and olfactory nerves had a clumped and thickened appearance in 66 % of cases. In idiopathic group, olfactory bulbs were rectangular shaped with minimally deformed contours, and olfactory nerves were thin and hard to delineate. No specific olfactory bulb or nerve pattern was identified in obstructive and post-traumatic groups, however closed olfactory cleft and siderotic frontobasal changes were helpful clues in obstructive and post-traumatic groups, respectively. CONCLUSION: In addition to olfactory cleft patency, olfactory sulcus depth and olfactory bulb volume; bulb and nerve morphologies may provide diagnostic information on different etiologies of olfactory dysfunction.