The Current and Future Treatment of Brain Metastases

Brain metastases are the most common intracranial malignancy, accounting for significant morbidity and mortality in oncology patients. The current treatment paradigm for brain metastasis depends on the patient’s overall health status, the primary tumor pathology, and the number and location of brain lesions. Herein, we review the modern management options for these tumors, including surgical resection, radiotherapy, and chemotherapy. Recent operative advances, such as fluorescence, confocal microscopy, and brachytherapy, are highlighted. With an increased understanding of the pathophysiology of brain metastasis come increased future therapeutic options. Therapy targeted to specific tumor molecular pathways, such as those involved in blood-brain barrier transgression, cell-cell adhesion, and angiogenesis, are also reviewed. A personalized plan for each patient, based on molecular characterizations of the tumor that are used to better target radiotherapy and chemotherapy, is undoubtedly the future of brain metastasis treatment

Improving utility of brain tumor confocal laser endomicroscopy: objective value assessment and diagnostic frame detection with convolutional neural networks

Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real-time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for the area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.Comment: SPIE Medical Imaging: Computer-Aided Diagnosis 201

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

A Quantitative Analysis of Published Skull Base Endoscopy Literature

Objectives Skull base endoscopy allows for minimal access approaches to the sinonasal contents and cranial base. Advances in endoscopic technique and applications have been published rapidly in recent decades. Setting: We utilized an Internet-based scholarly database (Web of Science, Thomson Reuters) to query broad-based phrases regarding skull base endoscopy literature. Participants: All skull base endoscopy publications. Main Outcome Measures: Standard bibliometrics outcomes. Results: We identified 4,082 relevant skull base endoscopy English-language articles published between 1973 and 2014. The 50 top-cited publications (n = 51, due to articles with equal citation counts) ranged in citation count from 397 to 88. Most of the articles were clinical case series or technique descriptions. Most (96% [49/51])were published in journals specific to either neurosurgery or otolaryngology. Conclusions: A relatively small number of institutions and individuals have published a large amount of the literature. Most of the publications consisted of case series and technical advances, with a lack of randomized trials

Mortality in Patients with Brainstem Cavernous Malformations

OBJECTIVE Brainstem cavernous malformations (BSCM)-associated mortality has been reported up to 20% in patients managed conservatively, whereas postoperative mortality rates range from 0 to 1.9%. Our aim was to analyze the actual risk and causes of BSCM-associated mortality in patients managed conservatively and surgically based on our own patient cohort and a systematic literature review. METHODS Observational, retrospective single-center study encompassing all patients with BSCM that presented to our institution between 2006 and 2018. In addition, a systematic review was performed on all studies encompassing patients with BSCM managed conservatively and surgically. RESULTS Of 118 patients, 54 were treated conservatively (961.0 person years follow-up in total). No BSCM-associated mortality was observed in our conservatively as well as surgically managed patient cohort. Our systematic literature review and analysis revealed an overall BSCM-associated mortality rate of 2.3% (95% CI: 1.6-3.3) in 22 studies comprising 1,251 patients managed conservatively and of 1.3% (95% CI: 0.9-1.7) in 99 studies comprising 3,275 patients with BSCM treated surgically. CONCLUSION The BSCM-associated mortality rate in patients managed conservatively is almost as low as in patients treated surgically and much lower than in frequently cited reports, most probably due to the good selection nowadays in regard to surgery

Microvascular Anastomosis Under 3D Exoscope or Endoscope Magnification: A Proof-Of-Concept Study

Background: Extracranial-intracranial bypass is a challenging procedure that requires special microsurgical skills and an operative microscope. The exoscope is a tool for neurosurgical visualization that provides view on a heads-up display similar to an endoscope, but positioned external to the operating field, like a microscope. The authors carried out a proof-of-concept study evaluating the feasibility and effectiveness of performing microvascular bypass using various new exoscopic tools. Methods: We evaluated microsurgical procedures using a three-dimensional (3D) endoscope, hands-free robotic automated positioning two-dimensional (2D) exoscope, and an ocular-free 3D exoscope, including surgical gauze knot tying, surgical glove cutting, placental vessel anastomoses, and rat vessel anastomoses. Image quality, effectiveness, and feasibility of each technique were compared among different visualization tools and to a standard operative microscope. Results: 3D endoscopy produced relatively unsatisfactory resolution imaging. It was shown to be sufficient for knot tying and anastomosis of a placental artery, but was not suitable for anastomosis in rats. The 2D exoscope provided higher resolution imaging, but was not adequate for all maneuvers because of lack of depth perception. The 3D exoscope was shown to be functional to complete all maneuvers because of its depth perception and higher resolution. Conclusion: Depth perception and high resolution at highest magnification are required for microvascular bypass procedures. Execution of standard microanastomosis techniques was unsuccessful using 2D imaging modalities because of depth-perception-related constraints. Microvascular anastomosis is feasible under 3D exoscopic visualization; however, at highest magnification, the depth perception is inferior to that provided by a standard operative microscope, which impedes the procedure

Sulforhodamine 101 selectively labels human astrocytoma cells in an animal model of glioblastoma

AbstractSulforhodamine 101 (SR101) is a useful tool for immediate staining of astrocytes. We hypothesized that if the selectivity of SR101was maintained in astrocytoma cells, it could prove useful for glioma research. Cultured astrocytoma cells and acute slices from orthotopic human glioma (n=9) and lymphoma (n=6) xenografts were incubated with SR101 and imaged with confocal microscopy. A subset of slices (n=18) were counter-immunostained with glial fibrillary acidic protein and CD20 for stereological assessment of SR101 co-localization. SR101 differentiated astrocytic tumor cells from lymphoma cells. In acute slices, SR101 labeled 86.50% (±1.86; p<0.0001) of astrocytoma cells and 2.19% (±0.47; p<0.0001) of lymphoma cells. SR101-labeled astrocytoma cells had a distinct morphology when compared with in vivo astrocytes. Immediate imaging of human astrocytoma cells in vitro and in ex vivo rodent xenograft tissue labeled with SR101 can identify astrocytic tumor cells and help visualize the tumor margin. These features are useful in studying astrocytoma in the laboratory and may have clinical applications