Table of Contents
Perspectives
Practice Perspectives
- Imaging of Patients with Suspected Large-Vessel Occlusion at Primary Stroke Centers: Available Modalities and a Suggested Approach
Endovascular thrombectomy has proven efficacy for a wide range of patients with large-vessel occlusion stroke and in selected cases up to 24 hours from onset. While primary stroke centers have increased the proportion of patients withstroke receiving thrombolytic therapy, delays can be encountereduntil patients with LVO are identified and transferred from the primary stroke center to acomprehensive stroke center. Therefore, any extra steps need to be carefullyweighed. The use of CTA (especially multiphase) at the primary stroke center levelhas many advantages in expediting the transfer of appropriate patients to a comprehensive center.
General Contents
- 3T MRI Whole-Brain Microscopy Discrimination of Subcortical Anatomy, Part 1: Brain Stem
The authors applied an optimized TSE T2 sequence to washed postmortem brain samples to reveal exquisite and reproducible brain stem anatomic MR imaging contrast comparable with histologic atlases. Direct TSE MR imaging sequence discrimination of brain stem anatomy can help validate other MR imaging contrasts, such as diffusion tractography, or serve as a structural template for extracting quantitative MR imaging data in future postmortem investigations.
- Disorder in Pixel-Level Edge Directions on T1WI Is Associated with the Degree of Radiation Necrosis in Primary and Metastatic Brain Tumors: Preliminary Findings
The authors sought to investigate whether co-occurrence of local anisotropic gradient orientations (COLLAGE) measurements from posttreatment gadolinium-contrast T1WI could distinguish varying extents of cerebral radiation necrosis and recurrent tumor classes in a lesion across primary and metastatic brain tumors. On 75 gadolinium-contrast T1WI studies obtained from patients with primary and metastatic brain tumors and nasopharyngeal carcinoma, the extent of cerebral radiation necrosis and recurrent tumor in every brain lesion was histopathologically defined by a neuropathologist as the following: 1) “pure” cerebral radiation necrosis; 2) “mixed” pathology with coexistence of cerebral radiation necrosis and recurrent tumors; 3) “predominant” (>80%) cerebral radiation necrosis; 4) predominant (>80%) recurrent tumor; and 5) pure tumor. COLLAGE features were extracted from the expert-annotated ROIs on MR imaging. COLLAGE features exhibited decreased skewness for patients with pure and predominant cerebral radiation necrosis and were statistically significantly different from those in patients with predominant recurrent tumors, which had highly skewed COLLAGE values.
- Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning
The authors evaluated tumor cell density using a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. They collected 82 image-recorded biopsy samples, from 18 patients with primary GBM. With multivariate modeling, transfer learning improved performance (r = 0.88) compared with one-model-fits-all (r = 0.39). They conclude that transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
- Neuroimaging-Based Classification Algorithm for Predicting 1p/19q-Codeletion Status in IDH-Mutant Lower Grade Gliomas
One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor or cyst texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lowergrade gliomas; and 2)a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation ofthe classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers.
- Endovascular Treatment of Unruptured MCA Bifurcation Aneurysms Regardless of Aneurysm Morphology: Short- and Long-Term Follow-Up
Between May 2008 and July 2017, endovascular treatment of 1184 aneurysms in 827 patients was performed in a single institution. Twenty-four percent of these aneurysms were located at the MCA, and 150 unruptured MCA bifurcation aneurysms treated with coiling, stent-assistedcoiling, or endovascular flow diverter (WEB device) were identified for this retrospective data analysis. The procedure-associated good clinical outcome was 89.9%, and the mortality rate was 2.7%. Short-term follow-up good clinical outcome/mortality rates were 91.3%/0.7%. At discharge, 137 patients had an mRS of 0–2 (91.3%) and 13 had an mRS of 3–6 (8.7%). The authors conclude that regardless of the architecture of MCA bifurcation aneurysms, endovascular treatment can be performed with low morbidity/mortality rates.
