IntroductionPrimary brain and CNS tumor incidence is approximately 19 per 100,000 individuals per year in the United States compared with seven per 100,000 individuals worldwide. 1,2 Worldwide this accounts for 2% of all primary tumors and 7% of years of life lost from cancer before the age of 70 years. 1,2 Glioblastoma multiforme (GBM) is also the most aggressive brain tumor with poor prognosis; patients with GBM have a median survival time of about 14 months. [3][4][5][6][7][8][9][10] GBM metastases outside the CNS are rare, so therapeutic experience with these types of tumors is limited. 11-15 Normally the brain is immunologically and anatomically separated from the body by the blood brain barrier. Herein, we present the cases of three patients with GBM with extra-CNS metastasis. The variety of metastasis locations demonstrated in these cases helps to illustrate the various mechanism and corresponding risk factors that allow GBM to escape the CNS. Case ReportsPatient 1. A 51-year-old man presented with a general seizure. Magnetic resonance imaging (MRI) revealed a lesion in the left parieto-occipital lobe (Figs 1A and 1B). Coronal T1-weighted postcontrast ( Fig 1A) and axial T2-weighted cranial MRI (Fig 1B) demonstrated an enhancing left parietal mass involving small venules from the superior sagittal sinus (Figs 1A and 1B, arrows). The patient was diagnosed with a mixed diffuse glioma and underwent subtotal resection of the tumor and postoperative radiochemotherapy. The tumor progressed, and 5 years after diagnosis, the patient underwent contrast-enhanced fluorescence imaging-guided tumor reresection with a subsequent diagnosis of GBM. Two years later, follow-up MRI revealed recurrent tumor. After completing radiochemotherapy, the patient was hospitalized for progressive dyspnea. Chest X-ray revealed a pleural effusion requiring thoracic drainage. Axial contrastenhanced computed tomography demonstrated an ill-defined 4-cm mass in the left lower lung lobe (Fig 1C, arrow) and pleural metastases (Figs 1C and 1E, arrowheads) with infiltration of the chest wall and destruction of a ventrolateral left rib (Fig 1E, asterisks).The pleural lesion was biopsied, and histologic examination of the biopsy sample (Fig 1D, hematoxylin and eosin staining, ϫ200 original magnification) revealed a malignant astrocytic glioma.The patient underwent additional radiochemotherapy with concomitant temozolomide. He completed radiotherapy but died 4 weeks later.Patient 2. A 24-year-old man who had undergone resection of a left temporal GBM involving the greater wing of the sphenoid bone with invasion of the middle cranial fossa at an outside facility presented with anxiety and headaches. Postoperative MRI revealed on axial T1w postcontrast images a dural thickening and extraconal or-

Timely localization of a bleeding source can improve the efficacy of trauma management, and improvements in the technology of computed tomography (CT) have expedited the work-up of the traumatized patient. The classic pattern of active extravasation (ie, administered contrast agent that has escaped from injured arteries, veins, or urinary tract) at dual phase CT is a jet or focal area of hyperattenuation within a hematoma that fades into an enlarged, enhanced hematoma on delayed images. This finding indicates significant bleeding and must be quickly communicated to the clinician, since potentially lifesaving surgical or endovascular repair may be necessary. Active extravasation can be associated with other injuries to arteries, such as a hematoma or a pseudoaneurysm. Both active extravasation and pseudoaneurysm (unlike bone fragments and dense foreign bodies) change in appearance on delayed images, compared with their characteristics on arterial images. Other clues to the location of vessel injury include lack of vascular enhancement (caused by occlusion or spasm), vessel irregularity, size change (such as occurs with pseudoaneurysm), and an intimal flap (which signifies dissection). The sentinel clot sign is an important clue for locating the bleeding source when other more localizing findings of vessel injury are not present. Timely diagnosis, differentiation of vascular injuries from other findings of trauma, signs of depleted intravascular volume, and localization of vascular injury are important to convey to interventional radiologists or surgeons to improve trauma management.

Background. Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard. Methods. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain. Results. Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, K trans ). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R 2 = 0.75). A less accurate predictive result (RMS error 5.4%, R 2 = 0.50) was found using conventional imaging only. Conclusion. Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients. Key Points1. Proliferative activity in gliomas can be predicted with MRI to guide biopsy and therapy.2. Machine learning of clinical imaging data can be used to predict quantitative pathological markers. Gates et al. Guiding the first biopsy in glioma patientsNeuro-Oncology glioma patients. Further clinical trials are justified to verify and build on these findings.

BACKGROUND AND PURPOSE: Dynamic contrast-enhanced perfusion MR imaging has proved useful in determining whether a contrastenhancing lesion is secondary to recurrent glial tumor or is treatment-related. In this article, we explore the best method for dynamic contrast-enhanced data analysis.

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