Automated assessment of ischemic core on non-contrast computed tomography: a multicenter comparative analysis with CT perfusion.

Background: Application of machine learning (ML) algorithms has shown promising results in estimating ischemic core volumes using non-contrast CT (NCCT).

Objective: To assess the performance of the e-Stroke Suite software (Brainomix) in assessing ischemic core volumes on NCCT compared with CT perfusion (CTP) in patients with acute ischemic stroke.

Methods: In this retrospective multicenter study, patients with anterior circulation large vessel occlusions who underwent pretreatment NCCT and CTP, successful reperfusion (modified Thrombolysis in Cerbral Infarction ≥2b), and post-treatment MRI, were included from three stroke centers.

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

Background: Narrowing of the lumbar spinal canal, or lumbar stenosis (LS), may cause debilitating radicular pain or muscle weakness. It is the most frequent indication for spinal surgery in the elderly population. Modern diagnosis relies on magnetic resonance imaging and its inherently subjective interpretation.

Upper lumbar spine far lateral disc herniations masquerading as peripheral nerve sheath tumors: illustrative cases.

Background: Migratory disc herniations can mimic neoplasms clinically and on imaging. Far lateral lumbar disc herniations usually compress the exiting nerve root and can be challenging to distinguish from a nerve sheath tumor due to the proximity of the nerve and characteristics on magnetic resonance imaging (MRI). These lesions can occasionally present in the upper lumbar spine region at the L1-2 and L2-3 levels.

Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign.

Purpose: The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+.

Methods: In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included.

MR Angiography in Assessment of Collaterals in Patients with Acute Ischemic Stroke: A Comparative Analysis with Digital Subtraction Angiography.

Collateral status has prognostic and treatment implications in acute ischemic stroke (AIS) patients. Unlike CTA, grading collaterals on MRA is not well studied. We aimed to evaluate the accuracy of assessing collaterals on pretreatment MRA in AIS patients against DSA.

Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain.

State-of-the-art diagnosis of radiculopathy relies on "highly subjective" radiologist interpretation of magnetic resonance imaging of the lower back. Currently, the treatment of lumbar radiculopathy and associated lower back pain lacks coherence due to an absence of reliable, objective diagnostic biomarkers. Using emerging machine learning techniques, the subjectivity of interpretation may be replaced by the objectivity of automated analysis.

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

Background: The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative.

Objective: To develop a machine-learning-based algorithm which accurately identifies patients as candidates for consultation with a spine surgeon, using only magnetic resonance imaging (MRI).

Methods: We trained a deep U-Net machine learning model to delineate spinal canals on axial slices of 100 normal lumbar MRI scans which were previously delineated by expert radiologists and neurosurgeons.

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.

Manual delineation of anatomy on existing images is the basis of developing deep learning algorithms for medical image segmentation. However, manual segmentation is tedious. It is also expensive because clinician effort is necessary to ensure correctness of delineation.

24/7/365 Neuroradiologist Coverage Improves Resident Perception of Educational Experience, Referring Physician Satisfaction, and Turnaround Time.

Purpose: To quantitatively and qualitatively assess the impact of attending neuroradiology coverage on radiology resident perceptions of the on-call experience, referring physician satisfaction, and final report turnaround times.

Materials And Methods: 24/7/365 attending neuroradiologist coverage began in October 2016 at our institution. In March 2017, an online survey of referring physicians, (emergency medicine, neurosurgery, and stroke neurology) and radiology residents was administered at a large academic medical center.

A Story in Black and White: Radiologic Evaluation in the Multidisciplinary Setting.

Strong engagement from expert radiologists is essential in ensuring the optimal function of a multidisciplinary group focused on the treatment of head and neck cancer. Active participation in multidisciplinary conference can be among the most rewarding roles for radiologists. Despite many benefits to radiologist involvement in multidisciplinary teams, there are obstacles and challenges that can prevent full participation.

Spleen size in cirrhosis of different etiologies.

Objectives: The purpose of this study was to evaluate the impact of cirrhosis etiology on spleen size as measured by sonography and computed tomography (CT).

Methods: The spleen images of 139 patients with cirrhosis secondary to alcohol abuse, hepatitis C, or nonalcoholic steatohepatitis were reviewed retrospectively. The maximum diagonal spleen length on a single sonogram and maximum spleen diameter on axial, coronal, or sagittal CT, whichever was largest, was compared among the etiologic groups.