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

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

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 positive correlation between serum amyloid β levels and depressive symptoms among community-dwelling elderly individuals in Japan

Koji Tsuruga,1 Norio Sugawara,1 Norio Yasui-Furukori,1 Ippei Takahashi,2 Shoko Tsuchimine,1 Ayako Kaneda,1 Shigeyuki Nakaji,2 Kazuhiko Nakamura1 1Department of Neuropsychiatry, 2Department of Social Medicine, Hirosaki University School of Medicine, Hirosaki, Japan Background: Amyloid beta (Aβ) levels have been associated with an increased risk of Alzheimer’s disease (AD). As depression is common before the onset of AD, serum Aß levels could be associated with depressive symptoms. The aim of this study was to investigate whether serum Aβ levels are associated with depressive symptoms and/or cognitive function in community-dwelling elderly individuals. Methods: We examined the association between serum Aβ levels and depression among 419 Japanese community-dwelling elderly individuals aged 60 years and over. Subjects were divided into two subgroups: younger elderly between 60 and 69 years old and older elderly over 69 years old. The Mini-Mental State Examination (MMSE) was used to assess cognitive function, and symptoms of depression were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D). The ability to perform activities of daily living was evaluated using the Tokyo Metropolitan Institute of Gerontology Index of Competence. Serum Aβ levels were measured with a human amyloid beta enzyme-linked immunosorbent assay kit. Results: After controlling for potential confounding variables, a multiple linear regression analysis showed that increased levels of serum Aβ40 and Aβ42 were associated with higher CES-D scores in the older elderly subgroup. Under the same condition, multiple regression showed that serum Aβ levels were not associated with MMSE scores among the total subjects, younger elderly, or older elderly. Conclusion: Serum Aβ levels were associated with depressive symptoms in community-dwelling elderly individuals. The present study indicates the possibility that serum Aβ may be involved in the development of late-onset depression. Keywords: Alzheimer’s disease, depression, dementia, Japanes

Dietary fiber showed no preventive effect against colon and rectal cancers in Japanese with low fat intake: an analysis from the results of nutrition surveys from 23 Japanese prefectures

BACKGROUND: Since Fuchs' report in 1999, the reported protective effect of dietary fiber from colorectal carcinogenesis has led many researchers to question its real benefit. The aim of this study is to evaluate the association between diet, especially dietary fiber and fat and colorectal cancer in Japan. METHODS: A multiple regression analysis (using the stepwise variable selection method) was performed using the standardized mortality ratios (SMRs) of colon and rectal cancer in 23 Japanese prefectures as objective variables and dietary fiber, nutrients and food groups as explanatory variables. RESULTS: As for colon cancer, the standardized partial correlation coefficients were positively significant for fat (1,13, P = 0.000), seaweeds (0.41, P = 0.026) and beans (0.45, P = 0.017) and were negatively significant for vitamin A (-0.63, P = 0.003), vitamin C (-0.42, P = 0.019) and yellow-green vegetables (-0.37, P = 0.046). For rectal cancer, the standardized partial correlation coefficient in fat (0.60, P = 0.002) was positively significant. Dietary fiber was not found to have a significant relationship with either colon or rectal cancers. CONCLUSIONS: This study failed to show any protective effect of dietary fiber in subjects with a low fat intake (Japanese) in this analysis, which supports Fuchs' findings in subjects with a high fat intake (US Americans)

Integration of Machine Learning and Mechanistic Models Accurately Predicts Variation in Cell Density of Glioblastoma Using Multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p \u3c 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy

Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival

INTRODUCTION: Contrast-enhanced MRI (CE-MRI) represents the current mainstay for monitoring treatment response in glioblastoma multiforme (GBM), based on the premise that enlarging lesions reflect increasing tumor burden, treatment failure, and poor prognosis. Unfortunately, irradiating such tumors can induce changes in CE-MRI that mimic tumor recurrence, so called post treatment radiation effect (PTRE), and in fact, both PTRE and tumor re-growth can occur together. Because PTRE represents treatment success, the relative histologic fraction of tumor growth versus PTRE affects survival. Studies suggest that Perfusion MRI (pMRI)–based measures of relative cerebral blood volume (rCBV) can noninvasively estimate histologic tumor fraction to predict clinical outcome. There are several proposed pMRI-based analytic methods, although none have been correlated with overall survival (OS). This study compares how well histologic tumor fraction and OS correlate with several pMRI-based metrics. METHODS: We recruited previously treated patients with GBM undergoing surgical re-resection for suspected tumor recurrence and calculated preoperative pMRI-based metrics within CE-MRI enhancing lesions: rCBV mean, mode, maximum, width, and a new thresholding metric called pMRI–fractional tumor burden (pMRI-FTB). We correlated all pMRI-based metrics with histologic tumor fraction and OS. RESULTS: Among 25 recurrent patients with GBM, histologic tumor fraction correlated most strongly with pMRI-FTB (r = 0.82; P < .0001), which was the only imaging metric that correlated with OS (P<.02). CONCLUSION: The pMRI-FTB metric reliably estimates histologic tumor fraction (i.e., tumor burden) and correlates with OS in the context of recurrent GBM. This technique may offer a promising biomarker of tumor progression and clinical outcome for future clinical trials