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Limiting the spread of the disease is key to controlling the COVID-19 pandemic. This includes identifying people who have been exposed to COVID-19, minimizing patient contact, and enforcing strict hygiene measures. To prevent healthcare systems from becoming overburdened, elective and non-urgent medical procedures and treatments have been postponed, and primary health care has broadened to include virtual appointments via telemedicine. Although telemedicine precludes the physical examination of a patient, it allows collection of a range of information prior to a patient's admission, and may therefore be used in preoperative assessment. This new tool can be used to evaluate the severity and progression of the main disease, other comorbidities, and the urgency of the surgical treatment as well as preferencing anesthetic procedures. It can also be used for effective screening and triaging of patients with suspected or established COVID-19, thereby protecting other patients, clinicians and communities alike.

We describe the case of a 57 year old female with renal cell cancer and metastatic disease to the spine. The patient presented to our institution with progressive lower extremity weakness and a T9 pathologic fracture with bony and tumor retropulsion into the canal. In addition, there was spinal cord compression from a chronic appearing T8-9 herniated disc fragment. The patient was consented for surgery. In this operative video, we demonstrate the intradural discectomy following a T7-T11 posterior instrumented fusion, decompression, and transpedicular tumor resection. The dorsal and ventral dural closure was reinforced with compressed gelfoam and dural sealant.

Glioblastoma (GBM) is a rare malignancy in children. The United States Surveillance, Epidemiology, and End Results (SEER) database allows large-scale analyses of clinical characteristics and prognostic features. We used it to study patients aged20 years with histologically confirmed GBM (2000-2010) and examined the relationship between patient demographics, tumor characteristics, patterns of treatment, and outcomes. The primary outcome was disease-specific survival. 302 subjects were identified, with median age 11 years. Median follow-up was 32 months (95% CI 27-39). 34.4% had gross total resection (GTR). 61% underwent radiation after surgery (17% of subjects3 years, 67% of those aged 4-19 years). Median survival and 2-year survival rates were 20 months and 46.9%, respectively. In multivariate analyses, age, tumor location, extent of resection, and year of diagnosis were significantly associated with the primary outcome. Compared to those aged 0-4 years, subjects aged 5-9 years and 10-14 years had higher risk of mortality. Infratentorial tumor location (HR 2.0, 95% CI 1.2-3.3, p = 0.007) and subtotal resection (HR 2.04, 95% CI 1.4-3.0, p 0.001) were associated with increased mortality. Later year of diagnosis was significantly associated with decreased risk of death (HR 0.93, 95% CI 0.9-0.99, p = 0.031). There was no association between sex, race, region, or tumor size and the primary outcome. Repeat analyses examining all-cause mortality identified the same risk factors as for CNS cancer-specific mortality. Younger age, supratentorial location, GTR, and later year of diagnosis were associated with improved survival.

The new World Health Organization classification of brain tumors depends on combining the histologic light microscopy features of central nervous system (CNS) tumors with canonical genetic alterations. This integrated diagnosis is redrawing the pedigree chart of brain tumors with rearrangement of tumor groups on the basis of geno-phenotypical behaviors into meaningful groups. Multiple radiogenomic studies provide a bridge between imaging features and tumor microenvironment. An overlap that can be integrated within the genophenotypical classification of CNS tumors for a better understanding of different clinically relevant entities.

The role of radiomics in the diagnosis, monitoring, and therapy planning of brain tumors is becoming increasingly clear. Incorporation of quantitative approaches in radiology, in combination with increased computer power, offers unique insights into macroscopic tumor characteristics and their direct association with the underlying pathophysiology. This article presents the most recent findings in radiomics and radiogenomics with respect to identifying potential imaging biomarkers with prognostic value that can lead to individualized therapy. In addition, a brief introduction to the concept of big data and its significance in medicine is presented.

Summary Purpose The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. Methods The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive ( http://cancerimagingarchive.net ) of 102 GBM patients using the VASARI scoring system. The patients’ clinical and genetic data were obtained from the TCGA website ( http://www.cancergenome.nih.gov/ ). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. Results The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679 ± 0.068, Akaike's information criterion 566.7, P Conclusion A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

Glioblastoma (GBM) is the most common and most aggressive primary malignant tumor of the central nervous system. Recently, researchers concluded that the "one-size-fits-all" approach for treatment of GBM is no longer valid and research should be directed toward more personalized and patient-tailored treatment protocols. Identification of the molecular and genomic pathways underlying GBM is essential for achieving this personalized and targeted therapeutic approach. Imaging genomics represents a new era as a noninvasive surrogate for genomic and molecular profile identification. This article discusses the basics of imaging genomics of GBM, its role in treatment decision-making, and its future potential in noninvasive genomic identification.

Background Entrapped temporal horn syndrome secondary to obstructive neoplastic lesions is most frequently treated by surgical excision of the offending lesion. Here we describe an alternate approach involving temporal horn to prepontine cistern shunting followed by radiosurgery of the offending lesion. Methods A 41-year-old woman with a history of meningiomatosis presented with progressive, incapacitating headache. Magnetic resonance imaging (MRI) showed growth of a right trigone meningioma, causing entrapment of the right temporal horn. A ventricular catheter was placed using frame-based stereotaxy and image fusion computed tomography/MRI to connect the entrapped lateral ventricle to the prepontine cistern. The patient reported complete resolution of her symptoms after the procedure. Results Postoperative MRI revealed decompression of the temporal horn. The trigonal meningioma was treated with stereotactic radiosurgery. The patient remained asymptomatic at the 2-year follow-up. Conclusions Trapped temporal horn syndrome secondary to obstructive neoplastic lesions can be treated by internal shunting followed by radiosurgery.