Faculty Bibliography

A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast⁻enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men; mean age, 58 ± 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS > 5 were classified as having highly developed macrovascular network; 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volume_CEL, increased rCBF_CEL, and poor survival; nVS correlated negatively with survival (r = -0.286; p = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma.

OBJECTIVE:Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION/CONCLUSIONS:We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.

PURPOSE/OBJECTIVE:The neuroimaging diagnosis of diffuse gliomas can be challenging owing to their variable clinical and radiologic presentation. The purpose of this study was to identify factors that are associated with imaging errors in the diagnosis of diffuse gliomas. METHODS:A retrospective case-control analysis was undertaken. 18 misdiagnosed diffuse gliomas on initial neuroimaging (cases) and 108 accurately diagnosed diffuse gliomas on initial neuroimaging (controls) were collected. Clinical, pathological, and imaging metrics were tabulated for each patient. The tabulated metrics were compared between cases and controls to determine factors associated with misdiagnosis. RESULTS:Cases of misdiagnosed diffuse glioma (vs controls) were more likely to undergo initial triage as a stroke workup [OR 14.429 (95% CI 4.345, 47.915), p < 0.0001], were less likely to enhance [OR 0.283 (95% CI 0.098, 0.812), p = 0.02], were smaller (mean diameter 4.4 vs 6.0 cm, p = 0.0008), produced less midline shift (median midline shift 0.0 vs 2.0 mm, p = 0.003), were less likely to demonstrate necrosis [OR 0.156 (95% CI 0.034-0.713), p = 0.008], and were less likely to have IV contrast administered on the initial MRI [OR 0.100 (95% CI 0.020, 0.494), p = 0.008]. CONCLUSION/CONCLUSIONS:Several clinical and radiologic metrics are associated with diffuse gliomas that are missed or misdiagnosed on the initial neuroimaging study. Knowledge of these associations may aid in avoiding misinterpretation and accurately diagnosing such cases in clinical practice.

BACKGROUND AND PURPOSE/OBJECTIVE:Few studies have shown MR imaging features and ADC correlating with molecular markers and survival in patients with glioma. Our purpose was to correlate MR imaging features and ADC with molecular subtyping and survival in adult diffuse gliomas. MATERIALS AND METHODS/METHODS:promoter methylation, and overall survival. RESULTS:wild-type glioma. Other MR imaging features were not statistically significant predictors of survival. CONCLUSIONS:wild-type gliomas.

PURPOSE/OBJECTIVE:Recent advances in sodium brain MRI have allowed for increased signal-to-noise ratio, faster imaging, and the ability of differentiating intracellular from extracellular sodium concentration, opening a new window of opportunity for clinical application. In gliomas, there are significant alterations in sodium metabolism, including increase in the total sodium concentration and extracellular volume fraction. The purpose of this study is to assess the feasibility of using sodium MRI quantitative measurements to evaluate gliomas. METHODS:), apparent intracellular sodium concentration (aISC), and apparent total sodium concentration (aTSC). Measurements were made within the contralateral normal-appearing putamen, contralateral normal-appearing white matter (NAWM), and solid tumor regions (area of T2-FLAIR abnormality, excluding highly likely areas of edema, cysts, or necrosis). Paired samples t test were performed comparing NAWM and putamen and between NAWM and solid tumor. RESULTS:(p = 0.19). CONCLUSION/CONCLUSIONS:Quantitative sodium measurements can be done in glioma patients and also has provided further evidence that total sodium and extracellular volume fraction are increased in gliomas.

BACKGROUND AND PURPOSE/OBJECTIVE:The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MATERIALS AND METHODS/METHODS:) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. RESULTS:promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. CONCLUSIONS:Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training.

Physiological and pathological processes that increase or decrease the central nervous system's need for nutrients and oxygen via changes in local blood supply act primarily at the level of the neurovascular unit (NVU). The NVU consists of endothelial cells, associated blood-brain barrier tight junctions, basal lamina, pericytes, and parenchymal cells, including astrocytes, neurons, and interneurons. Knowledge of the NVU is essential for interpretation of central nervous system physiology and pathology as revealed by conventional and advanced imaging techniques. This article reviews current strategies for interrogating the NVU, focusing on vascular permeability, blood volume, and functional imaging, as assessed by ferumoxytol an iron oxide nanoparticle.