Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.

Purpose/objectives: Previous deep learning (DL) algorithms for brain metastasis (BM) detection and segmentation have not been commonly used in clinics because they produce false-positive findings, require multiple sequences, and do not reflect physiological properties such as necrosis. The aim of this study was to develop a more clinically favorable DL algorithm (RLK-Unet) using a single sequence reflecting necrosis and apply it to automated treatment response assessment.

Methods And Materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm.

Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.

Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity.

Ethylenediaminetetraacetic acid-dependent pseudothrombocytopenia associated with neuroendocrine carcinoma: A case report.

Ethylenediaminetetraacetic acid-dependent pseudothrombocytopenia (EDTA-PTCP) is an in vitro phenomenon of EDTA-induced platelet aggregation at room temperature. This phenomenon consists of platelet clumping due to anti-platelet antibodies in blood anticoagulated with EDTA. It has been reported in patients with various diseases, including sepsis, multiple myeloma, acute myocardial infarction and breast cancer.