MRI-based contrast clearance analysis shows high differentiation accuracy between radiation-induced reactions and progressive disease after cranial radiotherapy.

BACKGROUND: Pseudoprogression (PsP) or radiation necrosis (RN) may frequently occur after cranial radiotherapy and show a similar imaging pattern compared with progressive disease (PD). We aimed to evaluate the diagnostic accuracy of magnetic resonance imaging-based contrast clearance analysis (CCA) in this clinical setting. PATIENTS AND METHODS: Patients with equivocal imaging findings after cranial radiotherapy were consecutively included into this monocentric prospective study. CCA was carried out by software-based automated subtraction of imaging features in late versus early T1-weighted sequences after contrast agent application. Two experienced neuroradiologists evaluated CCA with respect to PsP/RN and PD being blinded for histological findings. The radiological assessment was compared with the histopathological results, and its accuracy was calculated statistically. RESULTS: A total of 33 patients were included; 16 (48.5%) were treated because of a primary brain tumor (BT), and 17 (51.1%) because of a secondary BT. In one patient, CCA was technically infeasible. The accuracy of CCA in predicting the histological result was 0.84 [95% confidence interval (CI) 0.67-0.95; one-sided P = 0.051; n = 32]. Sensitivity and specificity of CCA were 0.93 (95% CI 0.66-1.00) and 0.78 (95% CI 0.52-0.94), respectively. The accuracy in patients with secondary BTs was 0.94 (95% CI 0.71-1.00) and nonsignificantly higher compared with patients with primary BT with an accuracy of 0.73 (95% CI 0.45-0.92), P = 0.16. CONCLUSIONS: In this study, CCA was a highly accurate, easy, and helpful method for distinguishing PsP or RN from PD after cranial radiotherapy, especially in patients with secondary tumors after radiosurgical treatment.

Delivery room desaturations and bradycardia in the early postnatal period of healthy term neonates - a prospective observational study.

OBJECTIVE: It has been suggested that desaturations and bradycardia precede acute life-threatening events (ALTE) and that ALTE is more common in the delivery room than later in life. However, frequency, duration and severity of desaturations in the first hours of life and additional risk factors have not readily been studied. METHODS: Term neonates (n = 100) were monitored for the first two hours after birth by pulse oximetry. The impact of maternal and perinatal factors on the frequency and severity of desaturations (<85%) and bradycardia (<80/min) was evaluated. RESULTS: Desaturations were detected in 30%, prolonged desaturations in 25% of infants. Desaturations were observed significantly more often in infants born by planned Cesarean section (pCs) compared to other modes of delivery (pCs 20/49; others 10/51; p = .029). Desaturations were also more frequent in infants diagnosed with neonatal infection (NI) or infants born to a mother with gestational diabetes (GDM), although not significantly. No bradycardia <80/min was detected. CONCLUSIONS: In our collective 4% of healthy term neonates had prolonged, clinically relevant desaturations in the first hours after birth. The mode of delivery and maternal risk factors may increase the risk for these events. However, our cohort was too small to detect any ALTE or SIDS and determine potential risk factors for these events. Our data lay ground for a large-scale prospective trial to investigate whether the mode of delivery could be an indication for general pulse oximetry monitoring of newborn in the delivery room.

Effects of the Cardio First AngelTM on chest compression performance.

BACKGROUND: Survival rates of out-of-hospital cardiac arrest remain poor. Bystander cardiopulmonary resuscitation (CPR) is crucial for survival and feedback devices could improve its quality. OBJECTIVE: We investigated the quality of chest compression when using the Cardio First AngelTM (CFA) feedback device compared to standard basic life support (BLS). The analysis focused on laymen. METHODS: Laymen without (n= 43) and with (n= 96) explanation of the device, medical students (n= 128) and medical staff (n= 27) performed 60 seconds of standard versus assisted chest compression using the CFA on a resuscitation manikin. Compression frequency, depth and position were analyzed according to current guidelines. RESULTS: Laymen showed significantly better success rates regarding correct compression depth when using the CFA (23.3% vs. 55.8%, p= 0.004 and 25.0% vs. 52.1%, p< 0.001, laymen without and with explanation of the device, respectively). Medical students likewise improved (22.7% vs. 42.2%, p= 0.004). Hand positioning was 100% correct in all groups with the device. Improvement in frequency yielded by the CFA was more pronounced for probands with fears of contact (p= 0.02). The benefit of using the device did not differ significantly in laymen with or without explanation. CONCLUSIONS: Chest compression as performed by laymen was significantly improved with regard to compression depth when using the CFA for guidance and feedback. With the device, no cases of incorrect hand positioning occurred in any group.

Critical review of reporting of the data analysis step in metabolomics.

INTRODUCTION: We present the first study to critically appraise the quality of reporting of the data analysis step in metabolomics studies since the publication of minimum reporting guidelines in 2007. OBJECTIVES: The aim of this study was to assess the standard of reporting of the data analysis step in metabolomics biomarker discovery studies and to investigate whether the level of detail supplied allows basic understanding of the steps employed and/or reuse of the protocol. For the purposes of this review we define the data analysis step to include the data pretreatment step and the actual data analysis step, which covers algorithm selection, univariate analysis and multivariate analysis. METHOD: We reviewed the literature to identify metabolomic studies of biomarker discovery that were published between January 2008 and December 2014. Studies were examined for completeness in reporting the various steps of the data pretreatment phase and data analysis phase and also for clarity of the workflow of these sections. RESULTS: We analysed 27 papers, published anytime in 2008 until the end of 2014 in the area or biomarker discovery in serum metabolomics. The results of this review showed that the data analysis step in metabolomics biomarker discovery studies is plagued by unclear and incomplete reporting. Major omissions and lack of logical flow render the data analysis' workflows in these studies impossible to follow and therefore replicate or even imitate. CONCLUSIONS: While we await the holy grail of computational reproducibility in data analysis to become standard, we propose that, at a minimum, the data analysis section of metabolomics studies should be readable and interpretable without omissions such that a data analysis workflow diagram could be extrapolated from the study and therefore the data analysis protocol could be reused by the reader. That inconsistent and patchy reporting obfuscates reproducibility is a given. However even basic understanding and reuses of protocols are hampered by the low level of detail supplied in the data analysis sections of the studies that we reviewed.

Molecular differences between chronic and aggressive periodontitis.

The 2 major forms of periodontitis, chronic (CP) and aggressive (AgP), do not display sufficiently distinct histopathological characteristics or microbiological/immunological features. We used molecular profiling to explore biological differences between CP and AgP and subsequently carried out supervised classification using machine-learning algorithms including an internal validation. We used whole-genome gene expression profiles from 310 'healthy' or 'diseased' gingival tissue biopsies from 120 systemically healthy non-smokers, 65 with CP and 55 with AgP, each contributing with ≥ 2 'diseased' gingival papillae (n = 241; with bleeding-on-probing, probing depth ≥ 4 mm, and clinical attachment loss ≥ 3 mm), and, when available, a 'healthy' papilla (n = 69; no bleeding-on-probing, probing depth ≤ 4 mm, and clinical attachment loss ≤ 4 mm). Our analyses revealed limited differences between the gingival tissue transcriptional profiles of AgP and CP, with genes related to immune responses, apoptosis, and signal transduction overexpressed in AgP, and genes related to epithelial integrity and metabolism overexpressed in CP. Different classifying algorithms discriminated CP from AgP with an area under the curve ranging from 0.63 to 0.99. The small differences in gene expression and the highly variable classifier performance suggest limited dissimilarities between established AgP and CP lesions. Future analyses may facilitate the development of a novel, 'intrinsic' classification of periodontitis based on molecular profiling.

Application of microarray analysis on computer cluster and cloud platforms.

BACKGROUND: Analysis of recent high-dimensional biological data tends to be computationally intensive as many common approaches such as resampling or permutation tests require the basic statistical analysis to be repeated many times. A crucial advantage of these methods is that they can be easily parallelized due to the computational independence of the resampling or permutation iterations, which has induced many statistics departments to establish their own computer clusters. An alternative is to rent computing resources in the cloud, e.g. at Amazon Web Services. OBJECTIVES: In this article we analyze whether a selection of statistical projects, recently implemented at our department, can be efficiently realized on these cloud resources. Moreover, we illustrate an opportunity to combine computer cluster and cloud resources. METHODS: In order to compare the efficiency of computer cluster and cloud implementations and their respective parallelizations we use microarray analysis procedures and compare their runtimes on the different platforms. RESULTS: Amazon Web Services provide various instance types which meet the particular needs of the different statistical projects we analyzed in this paper. Moreover, the network capacity is sufficient and the parallelization is comparable in efficiency to standard computer cluster implementations. CONCLUSION: Our results suggest that many statistical projects can be efficiently realized on cloud resources. It is important to mention, however, that workflows can change substantially as a result of a shift from computer cluster to cloud computing.

CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data.

BACKGROUND: For the last eight years, microarray-based classification has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called "p >> n" setting where the number of predictors p by far exceeds the number of observations n, hence the term "ill-posed-problem". Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers. RESULTS: In this article, we introduce a new Bioconductor package called CMA (standing for "Classification for MicroArrays") for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches. CONCLUSION: CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at (http://bioconductor.org/packages/2.3/bioc/html/CMA.html).

Evaluating microarray-based classifiers: an overview.

For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy.