Radiosurgery for brain metastases: specific indications for Gamma Knife in lieu of Linac in a single center using both methods

INTRODUCTION: Radiosurgery (RS) is gaining increasing acceptance in the upfront management of brain metastases (BM). It was initially used in so-called radioresistant metastases (melanoma, renal cell, sarcoma) because it allowed delivering higher dose to the tumor. Now, RS is also used for BM of other cancers. The risk of high incidence of new BM questions the need for associated whole-brain radiotherapy (WBRT). Recent evidence suggests that RS alone allows avoiding cognitive impairment related to WBRT, and the latter should be upheld for salvage therapy. Thus the increase use of RS for single and multiple BM raises new technical challenges for treatment delivery and dosimetry. We present our single institution experience focusing on the criteria that led to patients' selection for RS treatment with Gamma Knife (GK) in lieu of Linac. METHODS: Leksell Gamma Knife Perfexion (Elekta, Sweden) was installed in July 2010. Currently, the Swiss federal health care supports the costs of RS for BM with Linac but not with GK. Therefore, in our center, we always consider first the possibility to use Linac for this indication, and only select patients for GK in specific situations. All cases of BM treated with GK were retrospectively reviewed for criteria yielding to GK indication, clinical information, and treatment data. Further work in progress includes a posteriori dosimetry comparison with our Linac planning system (Brainscan V.5.3, Brainlab, Germany). RESULTS: From July 2010 to March 2012, 20 patients had RS for BM with GK (7 patients with single BM, and 13 with multiple BM). During the same period, 31 had Linac-based RS. Primary tumor was melanoma in 9, lung in 7, renal in 2, and gastrointestinal tract in 2 patients. In single BM, the reason for choosing of GK was the anatomical location close to, or in highly functional areas (1 motor cortex, 1 thalamic, 1 ventricular, 1 mesio-temporal, 3 deep cerebellar close to the brainstem), especially since most of these tumors were intended to be treated with high-dose RS (24 Gy at margin) because of their histology (3 melanomas, 1 renal cell). In multiple BM, the reason for choosing GK in relation with the anatomical location of the lesions was either technical (limitations of Linac movements, especially in lower posterior fossa locations) or closeness of multiple lesions to highly functional areas (typically, multiple posterior fossa BM close to the brainstem), precluding optimal dosimetry with Linac. Again, this was made more critical for multiple BM needing high-dose RS (6 melanoma, 2 hypernephroma). CONCLUSION: Radiosurgery for BM may represent some technical challenge in relation with the anatomical location and multiplicity of the lesions. These considerations may be accentuated for so-called radioresistant BM, when higher dose RS in needed. In our experience, Leksell Gamma Knife Perfexion proves to be useful in addressing these challenges for the treatment of BM.

Reproducibility of species lists, visual cover estimates and frequency methods for recording high mountain vegetation

Question: When multiple observers record the same spatial units of alpine vegetation, how much variation is there in the records and what are the consequences of this variation for monitoring schemes to detect change? Location: One test summit in Switzerland (Alps) and one test summit in Scotland (Cairngorm Mountains). Method: Eight observers used the GLORIA protocols for species composition and visual cover estimates in percent on large summit sections (>100 m2) and species composition and frequency in nested quadrats (1 m2). Results: The multiple records from the same spatial unit for species composition and species cover showed considerable variation in the two countries. Estimates of pseudoturnover of composition and coefficients of variation of cover estimates for vascular plant species in 1m x 1m quadrats showed less variation than in previously published reports whereas our results in larger sections were broadly in line with previous reports. In Scotland, estimates for bryophytes and lichens were more variable than for vascular plants. Conclusions: Statistical power calculations indicated that, unless large numbers of plots were used, changes in cover or frequency were only likely to be detected for abundant species (exceeding 10% cover) or if relative changes were large (50% or more). Lower variation could be reached with the point methods and with larger numbers of small plots. However, as summits often strongly differ from each other, supplementary summits cannot be considered as a way of increasing statistical power without introducing a supplementary component of variance into the analysis and hence the power calculations.

Methods for Determining Frequency- and Region-Dependent Relationships Between Estimated LFPs and BOLD Responses in Humans

The relationship between electrophysiological and functional magnetic resonance imaging (fMRI) signals remains poorly understood. To date, studies have required invasive methods and have been limited to single functional regions and thus cannot account for possible variations across brain regions. Here we present a method that uses fMRI data and singe-trial electroencephalography (EEG) analyses to assess the spatial and spectral dependencies between the blood-oxygenation-level-dependent (BOLD) responses and the noninvasively estimated local field potentials (eLFPs) over a wide range of frequencies (0-256 Hz) throughout the entire brain volume. This method was applied in a study where human subjects completed separate fMRI and EEG sessions while performing a passive visual task. Intracranial LFPs were estimated from the scalp-recorded data using the ELECTRA source model. We compared statistical images from BOLD signals with statistical images of each frequency of the eLFPs. In agreement with previous studies in animals, we found a significant correspondence between LFP and BOLD statistical images in the gamma band (44-78 Hz) within primary visual cortices. In addition, significant correspondence was observed at low frequencies (<14 Hz) and also at very high frequencies (>100 Hz). Effects within extrastriate visual areas showed a different correspondence that not only included those frequency ranges observed in primary cortices but also additional frequencies. Results therefore suggest that the relationship between electrophysiological and hemodynamic signals thus might vary both as a function of frequency and anatomical region.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Alternate Methods of Bridge Strengthening, HR-302, 1989

The need for upgrading a large number of understrength and obsolete bridges in the United States has been well documented in the literature. Through the performance of several Iowa DOT projects, the concept of strengthening bridges (simple and continuous spans) by post-tensioning has been developed. The purpose of this project was to investigate two additional strengthening alternatives that may be more efficient than post-tensioning in certain situations. The research program for each strengthening scheme included a literature review, laboratory testing of the strengthening scheme, and a finite-element analysis of the scheme. For clarity the two strengthening schemes are presented separately. In Part 1 of this report, the strengthening of existing steel stringers in composite steel beam concrete-deck bridges by providing partial end restraint was shown to be feasible. Part 2 of this report summarizes the research that was undertaken to strengthen the negative moment regions of continuous, composite bridges. Two schemes were investigated: post-compression of stringers and superimposed trusses within the stringers.

Estimating vadose zone hydraulic properties using ground penetrating radar: The impact of prior information

A number of geophysical methods, such as ground-penetrating radar (GPR), have the potential to provide valuable information on hydrological properties in the unsaturated zone. In particular, the stochastic inversion of such data within a coupled geophysical-hydrological framework may allow for the effective estimation of vadose zone hydraulic parameters and their corresponding uncertainties. A critical issue in stochastic inversion is choosing prior parameter probability distributions from which potential model configurations are drawn and tested against observed data. A well chosen prior should reflect as honestly as possible the initial state of knowledge regarding the parameters and be neither overly specific nor too conservative. In a Bayesian context, combining the prior with available data yields a posterior state of knowledge about the parameters, which can then be used statistically for predictions and risk assessment. Here we investigate the influence of prior information regarding the van Genuchten-Mualem (VGM) parameters, which describe vadose zone hydraulic properties, on the stochastic inversion of crosshole GPR data collected under steady state, natural-loading conditions. We do this using a Bayesian Markov chain Monte Carlo (MCMC) inversion approach, considering first noninformative uniform prior distributions and then more informative priors derived from soil property databases. For the informative priors, we further explore the effect of including information regarding parameter correlation. Analysis of both synthetic and field data indicates that the geophysical data alone contain valuable information regarding the VGM parameters. However, significantly better results are obtained when we combine these data with a realistic, informative prior.

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data

As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches - with the help of Bayesian networks - for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).

Comparing simulation methods for modelling the errors of stand inventory data

Abstract