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ObjectiveOrgan radiation dose from a CT scan, calculated by CT dosimetry software, can be combined with cancer risk data to estimate cancer incidence resulting from CT exposure. We aim to determine to what extent the use of improved anatomical representation of the adult human body "phantom" in CT dosimetry software impacts estimates of radiation dose and cancer incidence, to inform comparison of past and future research.MethodsWe collected 20 adult cases for each of three CT protocols (abdomen/pelvis, chest and head) from each of five public hospitals (random sample) (January-April inclusive 2010) and three private clinics (self-report). Organ equivalent and effective dose were calculated using both ImPACT (mathematical phantom) and NCICT (voxelised phantom) software. Bland-Altman plots demonstrate agreement and Passing-Bablok regression reports systematic, proportional or random differences between results. We modelled the estimated lifetime attributable risk of cancer from a single exposure for each protocol, using age-sex specific risk-coefficients from the Biologic Effects of Ionizing Radiation VII report.ResultsFor the majority of organs used in epidemiological studies of cancer incidence, the NCICT software (voxelised) provided higher dose estimates. Across the lifespan NCICT resulted in cancer estimates 2.9%-6.6% and 14.8%-16.3% higher in males and females (abdomen/pelvis) and 7.6%-19.7% and 12.9%-26.5% higher in males and females respectively (chest protocol). For the head protocol overall cancer estimates were lower for NCICT, but with greatest disparity, >30% at times.ConclusionWhen the results of previous studies estimating CT dose and cancer incidence are compared to more recent, or future, studies the dosimetry software must be considered. Any change in radiation dose or cancer risk may be attributable to the software and phantom used, rather than-or in addition to-changes in scanning practice. Studies using dosimetry software to estimate radiation dose should describe software comprehensively to facilitate comparison with past and future research.

OBJECTIVE:Dysregulation of the Hedgehog (Hh) pathway has been described in a variety of cancers, including cervical cancer, a disease which shares a common aetiology with vulval squamous cell carcinoma (VSCC). Here, we investigate a large number of primary VSCC cases for evidence of Hedgehog pathway activation and examine the implications of pathway activity on clinical outcomes in a cohort of patients with primary VSCC. METHODS:Archival histology blocks containing VSCC and histologically normal adjacent epithelium were retrieved from a cohort of 91 patients who underwent treatment for primary VSCC. Immunohistochemistry staining was undertaken to assess for the expression of key Hh pathway components (SHH, PTCH1, GLI1). A competing risks statistical model was used to evaluate the implications of the levels of key Hh pathway components on clinical outcomes. RESULTS:We show that 92% of primary VSCC cases over-expressed one or more components of the Hh signalling pathway when compared to the adjacent normal epithelium. While expression of SHH and GLI1 did not correlate with any clinicopathological criteria, over- or under-expression of PTCH1 was associated with a reduced or increased risk of developing a local disease recurrence, respectively. In VSCC arising on a background of Lichen Sclerosus, the risk of local recurrence was potentiated in cases where PTCH1 was under-expressed. CONCLUSIONS:Our findings reveal, for the first time, that the Hh pathway is activated in VSCC and that PTCH1 expression can be used as a biomarker to stratify patients and inform clinicians of the risk of their local recurrence, particularly in cases of VSCC associated with LS.

Cutaneous mast cell tumours are one of the most common canine cancers. Approximately 25% of the tumours metastasise. Activating c-kit mutations are present in about 20% of tumours, but metastases occur in the absence of mutations. Tumour metastasis is associated with significantly diminished survival in spite of adjuvant chemotherapy. Available prognostic tests do not reliably predict whether a tumour will metastasise. In this study we compared the global expression profiles of 20 primary cutaneous mast cell tumours that metastasised with those of 20 primary tumours that did not metastasise. The objective was to identify genes associated with mast cell tumour metastatic progression that may represent targets for therapeutic intervention and biomarkers for prediction of tumour metastasis. Canine Gene 1.1 ST Arrays were employed for genome-wide expression analysis of formalin-fixed, paraffin-embedded biopsies of mast cell tumours borne by dogs that either died due to confirmed mast cell tumour metastasis, or were still alive more than 1000 days post-surgery. Decreased gene expression in the metastasising tumours appears to be associated with a loss of cell polarity, reduced cell-cell and cell-ECM adhesion, and increased cell deformability and motility. Dysregulated gene expression may also promote extracellular matrix and base membrane degradation, suppression of cell cycle arrest and apoptosis, and angiogenesis. Down-regulation of gene expression in the metastasising tumours may be achieved at least in part by small nucleolar RNA-derived RNA and microRNA-effected gene silencing. Employing cross-validation, a linear discriminant analysis-based classifier featuring 19 genes that displayed two-fold differences in expression between metastasising and non-metastasising tumours was estimated to classify metastasising and non-metastasising tumours with accuracies of 90-100% and 70-100%, respectively. The differential expression of 9 of the discriminator genes was confirmed by quantitative reverse transcription-PCR.

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

To evaluate the effect of introduction of iterative reconstruction as a mandated software upgrade on radiation dosimetry in routine clinical practice over a range of computed tomography examinations.Random samples of scanning data were extracted from a centralised Picture Archiving Communication System pertaining to 10 commonly performed computed tomography examination types undertaken at two hospitals in Western Australia, before and after the introduction of iterative reconstruction. Changes in the mean dose length product and effective dose were evaluated along with estimations of associated changes to annual cancer incidence.We observed statistically significant reductions in the effective radiation dose for head computed tomography (22-27%) consistent with those reported in the literature. In contrast the reductions observed for non-contrast chest (37-47%); chest pulmonary embolism study (28%), chest/abdominal/pelvic study (16%) and thoracic spine (39%) computed tomography. Statistically significant reductions in radiation dose were not identified in angiographic computed tomography. Dose reductions translated to substantial lowering of the lifetime attributable risk, especially for younger females, and estimated numbers of incident cancers.Reduction of CT dose is a priority Iterative reconstruction algorithms have the potential to significantly assist with dose reduction across a range of protocols. However, this reduction in dose is achieved via reductions in image noise. Fully realising the potential dose reduction of iterative reconstruction requires the adjustment of image factors and forgoing the noise reduction potential of the iterative algorithm. Our study has demonstrated a reduction in radiation dose for some scanning protocols, but not to the extent experimental studies had previously shown or in all protocols expected, raising questions about the extent to which iterative reconstruction achieves dose reduction in real world clinical practice.

OBJECTIVE: Choosing to undertake a CT scan relies on balancing risk versus benefit, however risks associated with CT scanning have generally been limited to broad anatomical locations, which do not provided adequate information to evaluate risk against benefit. Our study aimed to determine differences in radiation dose and risk estimates associated with modern CT scanning examinations when computed for clinical protocols compared with those using anatomical area. METHODS: Technical data were extracted from a tertiary hospital Picture Archiving Communication System for random samples of 20-40 CT examinations per adult clinical CT protocol. Organ and whole body radiation dose were calculated using ImPACT Monte Carlo simulation software and cancer incidence and mortality estimated using BEIR VII age and gender specific lifetime attributable risk weights. RESULTS: Thirty four unique CT protocols were identified by our study. When grouped according to anatomic area the radiation dose varied substantially, particularly for abdominal protocols. The total estimated number of incident cancers and cancer related deaths using the mean dose of anatomical area were 86 and 69 respectively. Using more specific protocol doses the estimates rose to 214 and 138 incident cancers and cancer related deaths, at least doubling the burden estimated. CONCLUSIONS: Modern CT scanning produces a greater diversity of effective doses than much of the literature describes; where a lack of focus on actual scanning protocols has produced estimates that do not reflect the range and complexity of modern CT practice. To allow clinicians, patients and policy makers to make informed risk versus benefit decisions the individual and population level risks associated with modern CT practices are essential.

Conservation planners often wish to predict how species distributions will change in response to environmental changes. Species distribution models (SDMs) are the primary tool for making such predictions. Many methods are widely used; however, they all make simplifying assumptions, and predictions can therefore be subject to high uncertainty. With global change well underway, field records of observed range shifts are increasingly being used for testing SDM transferability. We used an unprecedented distribution dataset documenting recent range changes of British vascular plants, birds, and butterflies to test whether correlative SDMs based on climate change provide useful approximations of potential distribution shifts. We modelled past species distributions from climate using nine single techniques and a consensus approach, and projected the geographical extent of these models to a more recent time period based on climate change; we then compared model predictions with recent observed distributions in order to estimate the temporal transferability and prediction accuracy of our models. We also evaluated the relative effect of methodological and taxonomic variation on the performance of SDMs. Models showed good transferability in time when assessed using widespread metrics of accuracy. However, models had low accuracy to predict where occupancy status changed between time periods, especially for declining species. Model performance varied greatly among species within major taxa, but there was also considerable variation among modelling frameworks. Past climatic associations of British species distributions retain a high explanatory power when transferred to recent time--due to their accuracy to predict large areas retained by species--but fail to capture relevant predictors of change. We strongly emphasize the need for caution when using SDMs to predict shifts in species distributions: high explanatory power on temporally-independent records--as assessed using widespread metrics--need not indicate a model's ability to predict the future.