Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.

Objective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.

MR imaging of hepatocellular carcinoma: prospective intraindividual head-to-head comparison of the contrast agents gadoxetic acid and gadoteric acid.

The routine use of dynamic-contrast-enhanced MRI (DCE-MRI) of the liver using hepatocyte-specific contrast agent (HSCA) as the standard of care for the study of focal liver lesions is not widely accepted and opponents invoke the risk of a loss in near 100% specificity of extracellular contrast agents (ECA) and the need for prospective head-to-head comparative studies evaluating the diagnostic performance of both contrast agents. The Purpose of this prospective intraindividual study was to conduct a quantitative and qualitative head-to-head comparison of DCE-MRI using HSCA and ECA in patients with liver cirrhosis and HCC. Twenty-three patients with liver cirrhosis and proven HCC underwent two 3 T-MR examinations, one with ECA (gadoteric acid) and the other with HSCA (gadoxetic acid).

Pneumatosis intestinalis and porto-mesenteric venous gas: a multicenter study.

Background: Estimating the prognosis of patients with pneumatosis intestinalis (PI) and porto-mesenteric venous gas (PMVG) can be challenging. The purpose of this study was to refine prognostication to improve decision making in daily clinical routine.

Methods: A total of 290 patients with confirmed PI were included in the final analysis.

Quantitative volumetric assessment of baseline enhancing tumor volume as an imaging biomarker predicts overall survival in patients with glioblastoma.

Background: Glioblastoma multiforme (GBM) is the commonest malignant primary brain tumor and still has one of the worst prognoses among cancers in general. There is a need for non-invasive methods to predict individual prognosis in patients with GBM.

Purpose: To evaluate quantitative volumetric tissue assessment of enhancing tumor volume on cranial magnetic resonance imaging (MRI) as an imaging biomarker for predicting overall survival (OS) in patients with GBM.