Claire Keeble

Preoperative neutrophil:lymphocyte and platelet:lymphocyte ratios predict endometrial cancer survival.

Background:Variations in systemic inflammatory response biomarker levels have been associated with adverse clinical outcome in various malignancies. This study determined the prognostic significance of preoperative neutrophil:lymphocyte (NLR), platelet:lymphocyte (PLR) and monocyte:lymphocyte (MLR) ratios in endometrial cancer.Methods:Clinicopathological and 5-year follow-up data were obtained for a retrospective series of surgically treated endometrial cancer patients (n=605). Prognostic significance was determined for overall (OS) and cancer-specific survival (CSS) using Cox proportional hazards models and Kaplan–Meier analysis. Receiver–operator characteristic and log-rank functions were used to optimise cut-offs. NLR, PLR and MLR associations with clinicopathological variables were determined using non-parametric tests.Results:Applying cut-offs of ⩾2.4 (NLR), ⩾240 (PLR) and ⩾0.19 (MLR), NLR and PLR (but not MLR) had independent prognostic significance. Combining NLR and PLR scores stratified patients into low (NLR-low and PLR-low), intermediate (NLR-high or PLR-high) and high risk (NLR-high and PLR-high) groups: multivariable hazard ratio (HR) 2.51; P<0.001 (OS); HR 2.26; P<0.01 (CSS) for high vs low risk patients. Increased NLR and PLR were most strongly associated with advanced stage (P<0.001), whereas increased MLR was strongly associated with older age (P<0.001).Conclusion:Both NLR and PLR are independent prognostic indicators for endometrial cancer, which can be combined to provide additional patient stratification.

Participation Bias Assessment in Three High-Impact Journals

Studies into participation bias have examined participation trends, where it occurs, the factors affecting it, and methods to try to reduce it. However, some authors only discuss participation bias at the end of the study, some acknowledge it and apply a method to try to reduce it, while others ignore it or dismiss it as negligible. Issues of three high-impact epidemiology journals were examined; 81 articles were read and reviewed for potential participation bias. Categories were used to classify the approach taken to participation bias and the results recorded. Of the 81 articles considered, 42 (51%) were eligible and could have suffered from participation bias. It was found that 57% of these articles ignored the effects of participation bias, while 17% only considered it briefly in the discussion. Few articles (22%) attempted to reduce the participation bias, with over half of these using unsuitable methods (55%). This review highlights how participation bias is often not considered and hence the conclusions drawn from these studies may not be correct.

Is there agreement on what makes a good ultrasound image

With the introduction of national guidelines for ultrasound screening it might be assumed that there is agreement in the key features required in assessing image quality. The aim of this study was to determine whether it is possible that personal preferences may influence what is regarded as an acceptable image. Two image sets, one vascular and one obstetric, were taken to the British Medical Ultrasound Society ‘Ultrasound 2012’ conference for delegates to rate whether images were acceptable or not. Data collection took place on days one and two, with the results presented on day three of the conference. Images from a variety of ultrasound machines were used and ranked in order of the odds of producing an acceptable image using logistic regression. Agreement between observers was investigated and three images per person were repeated to look at agreement within observers. Audience feedback was used to record the reasons why images were regarded as acceptable or not. Eighty-two participants reviewed each of the two image sets. The machine rankings revealed that some machines were up to 12 times more likely to produce an acceptable image than other machines. Agreement amongst experts or non-experts was found, but disagreement between the subgroups of experts and non-experts. Agreement within observers was around 80% and similar results were found in each of the image sets. Despite image quality assessment, personal preferences and expertise may still affect judgement, and guidelines may not ensure agreement.

Methods for the analysis of ordinal response data in medical image quality assessment

The assessment of image quality in medical imaging often requires observers to rate images for some metric or detectability task. These subjective results are used in optimization, radiation dose reduction or system comparison studies and may be compared to objective measures from a computer vision algorithm performing the same task. One popular scoring approach is to use a Likert scale, then assign consecutive numbers to the categories. The mean of these response values is then taken and used for comparison with the objective or second subjective response. Agreement is often assessed using correlation coefficients. We highlight a number of weaknesses in this common approach, including inappropriate analyses of ordinal data and the inability to properly account for correlations caused by repeated images or observers. We suggest alternative data collection and analysis techniques such as amendments to the scale and multilevel proportional odds models. We detail the suitability of each approach depending upon the data structure and demonstrate each method using a medical imaging example. Whilst others have raised some of these issues, we evaluated the entire study from data collection to analysis, suggested sources for software and further reading, and provided a checklist plus flowchart for use with any ordinal data. We hope that raised awareness of the limitations of the current approaches will encourage greater method consideration and the utilization of a more appropriate analysis. More accurate comparisons between measures in medical imaging will lead to a more robust contribution to the imaging literature and ultimately improved patient care.

Context sensitive cardiac x-ray imaging: a machine vision approach to x-ray dose control

Abstract. Modern cardiac x-ray imaging systems regulate their radiation output based on the thickness of the patient to maintain an acceptable signal at the input of the x-ray detector. This approach does not account for the context of the examination or the content of the image displayed. We have developed a machine vision algorithm that detects iodine-filled blood vessels and fits an idealized vessel model with the key parameters of contrast, diameter, and linear attenuation coefficient. The spatio-temporal distribution of the linear attenuation coefficient samples, when appropriately arranged, can be described by a simple linear relationship, despite the complexity of scene information. The algorithm was tested on static anthropomorphic chest phantom images under different radiographic factors and 60 dynamic clinical image sequences. It was found to be robust and sensitive to changes in vessel contrast resulting from variations in system parameters. The machine vision algorithm has the potential of extracting real-time context sensitive information that may be used for augmenting existing dose control strategies.

Comprehensive assessment of patient image quality and radiation dose in latest generation cardiac x-ray equipment for percutaneous coronary interventions.

Abstract. This study aimed to determine whether a reduction in radiation dose was found for percutaneous coronary interventional (PCI) patients using a cardiac interventional x-ray system with state-of-the-art image enhancement and x-ray optimization, compared to the current generation x-ray system, and to determine the corresponding impact on clinical image quality. Patient procedure dose area product (DAP) and fluoroscopy duration of 131 PCI patient cases from each x-ray system were compared using a Wilcoxon test on median values. Significant reductions in patient dose (p≪0.001) were found for the new system with no significant change in fluoroscopy duration (p=0.2); procedure DAP reduced by 64%, fluoroscopy DAP by 51%, and “cine” acquisition DAP by 76%. The image quality of 15 patient angiograms from each x-ray system (30 total) was scored by 75 clinical professionals on a continuous scale for the ability to determine the presence and severity of stenotic lesions; image quality scores were analyzed using a two-sample t-test. Image quality was reduced by 9% (p≪0.01) for the new x-ray system. This demonstrates a substantial reduction in patient dose, from acquisition more than fluoroscopy imaging, with slightly reduced image quality, for the new x-ray system compared to the current generation system.

Mapping the methylation status of the miR-145 promoter in saphenous vein smooth muscle cells from individuals with type 2 diabetes

Type 2 diabetes mellitus prevalence is growing globally, and the leading cause of mortality in these patients is cardiovascular disease. Epigenetic mechanisms such as microRNAs (miRs) and DNA methylation may contribute to complications of type 2 diabetes mellitus. We discovered an aberrant type 2 diabetes mellitus–smooth muscle cell phenotype driven by persistent up-regulation of miR-145. This study aimed to determine whether elevated expression was due to changes in methylation at the miR-145 promoter. Smooth muscle cells were cultured from saphenous veins of 22 non-diabetic and 22 type 2 diabetes mellitus donors. DNA was extracted, bisulphite treated and pyrosequencing used to interrogate methylation at 11 CpG sites within the miR-145 promoter. Inter-patient variation was high irrespective of type 2 diabetes mellitus. Differential methylation trends were apparent between non-diabetic and type 2 diabetes mellitus–smooth muscle cells at most sites but were not statistically significant. Methylation at CpGs −112 and −106 was consistently lower than all other sites explored in non-diabetic and type 2 diabetes mellitus–smooth muscle cells. Finally, miR-145 expression per se was not correlated with methylation levels observed at any site. The persistent up-regulation of miR-145 observed in type 2 diabetes mellitus–smooth muscle cells is not related to methylation at the miR-145 promoter. Crucially, miR-145 methylation is highly variable between patients, serving as a cautionary note for future studies of this region in primary human cell types.

Agreement between objective and subjective assessment of image quality in ultrasound abdominal aortic aneurism screening

OBJECTIVE To investigate agreement between objective and subjective assessment of image quality of ultrasound scanners used for abdominal aortic aneurysm (AAA) screening. METHODS Nine ultrasound scanners were used to acquire longitudinal and transverse images of the abdominal aorta. 100 images were acquired per scanner from which 5 longitudinal and 5 transverse images were randomly selected. 33 practitioners scored 90 images blinded to the scanner type and subject characteristics and were required to state whether or not the images were of adequate diagnostic quality. Odds ratios were used to rank the subjective image quality of the scanners. For objective testing, three standard test objects were used to assess penetration and resolution and used to rank the scanners. RESULTS The subjective diagnostic image quality was ten times greater for the highest ranked scanner than for the lowest ranked scanner. It was greater at depths of <5.0 cm (odds ratio, 6.69; 95% confidence interval, 3.56, 12.57) than at depths of 15.1-20.0 cm. There was a larger range of odds ratios for transverse images than for longitudinal images. No relationship was seen between subjective scanner rankings and test object scores. CONCLUSION Large variation was seen in the image quality when evaluated both subjectively and objectively. OBJECTIVE scores did not predict subjective scanner rankings. Further work is needed to investigate the utility of both subjective and objective image quality measurements. ADVANCES IN KNOWLEDGE Ratings of clinical image quality and image quality measured using test objects did not agree, even in the limited scenario of AAA screening.

The R primer

This book provides a good introduction to R, using a clear layout and detailed, reproducible examples. An ideal tool for any new R user. R is a software that is frequently used by statisticians, for performing calculations or graphical summaries, which can be rather daunting initially. The book is intended for use by newcomers to R. It logically begins with detailed steps for how to read data into R from a variety of sources. Each chapter is divided into sections which are enticingly short, yet sufficiently detailed. Throughout the book there are simple, annotated examples which could easily be applied for practise and then extended for the reader’s specific needs. The book nicely flows, introducing topics in such a way that the reader can simply work through the chapters, grasping the basics of R for future use and long-term development. However, each section is also self-contained, enabling the more experienced reader to merely jump to the required section to gain the skills required to run one analysis for a given problem. A wide range of topics are covered, making the book suitable for a variety of readers, from undergraduate students to professionals new to R. The topics may be similar to those found in a typical statistics undergraduate degree. The same structure is used throughout the book, where a problem is presented and a solution suggested, along with a reproducible example. This results in an easy to digest, visually appealing layout. The book is written using statements, which ensure fast, clear solutions to the problems covered, without being too descriptive. However, there are minor grammatical errors which appear unusual, but which thankfully do not detract from the usefulness of the book. The author aims to provide a user-friendly introduction for newcomers to R, covering frequently encountered problems using examples. It does not aim to cover all areas of R, but instead provides the basics which can be used to develop skills. It usefully refers to extensions or other similar sections in the book, which can be used to further learning. Additional support is also available on the corresponding website. These aims are well met in the book, making it useful for anyone encountering R for the first time or, alternatively, someone wishing to gain the basics of an unfamiliar topic. This book may be a suitable purchase for a newcomer requiring a range of R skills, who benefits from learning through examples. However, it is not intended to provide any statistical theory, since this is assumed already, nor R programming, which can be viewed as more advanced. It may be also recommended to libraries since it could be used by a range of different professionals from a variety of backgrounds, who may wish to read the entire book or simply use one section to aid their analysis. Overall, an extremely helpful introduction to a very useful statistical package.

Adaptation of chain event graphs for use with case-Control studies in epidemiology

Abstract Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditional analyses report usually only the odds ratio for given exposures and the binary disease status. Chain event graphs are a graphical representation of a statistical model derived from event trees which have been developed in artificial intelligence and statistics, and only recently introduced to the epidemiology literature. They are a modern Bayesian technique which enable prior knowledge to be incorporated into the data analysis using the agglomerative hierarchical clustering algorithm, used to form a suitable chain event graph. Additionally, they can account for missing data and be used to explore missingness mechanisms. Here we adapt the chain event graph framework to suit scenarios often encountered in case-control studies, to strengthen this study design which is time and financially efficient. We demonstrate eight adaptations to the graphs, which consist of two suitable for full case-control study analysis, four which can be used in interim analyses to explore biases, and two which aim to improve the ease and accuracy of analyses. The adaptations are illustrated with complete, reproducible, fully-interpreted examples, including the event tree and chain event graph. Chain event graphs are used here for the first time to summarise non-participation, data collection techniques, data reliability, and disease severity in case-control studies. We demonstrate how these features of a case-control study can be incorporated into the analysis to provide further insight, which can help to identify potential biases and lead to more accurate study results.