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National professional capabilities for Australian Diagnostic Radiography (DR) graduates require computed tomography (CT) scanning competence. Clinical placements have varied success in achiveing CT learning experiences and dedicated simulation at university can aid development of clinical skills. This study explored DR undergraduate students' attitudes, confidence and experiences of using a remote access simulation facility equipped with a live radiation commercial scanner. A pre- and post-clinical placement survey, with Likert scales and open-ended questions, was employed. Students in their third year of study undertook formal simulation CT learning using the NETRAD CT facility, prior to a four week off-campus clinical placement. Results indicate students (pre n = 21; post n = 23) had mixed levels of satisfaction when using NETRAD CT even though learning outcomes and relevance was clear. Comparison of pre- and post-matched statements reveals a significant decrease in students' perception of confidence in their skills post-clinical placement, reduced satisfaction with remote learning and increased preference for hands-on experiences. Open-ended responses were polarised to either positive simulation themes of convenience and repeatability, alongside negative themes with remote access and lack of facilitation. While students considered CT simulation to be educationally engaging, the technology-rich learning environment presented challenges and believed interactive simulation is inferior to real clinical experiences.

Background: Orofacial clefts are the most common craniofacial congenital malformation in humans. Approximately 30% of clefts arise as part of a syndrome or sequence, characterised by co-existing structural and functional anomalies. Many syndromes are thought to be undiagnosed, although the presence of multiple anomalies may indicate the presence of a syndrome or sequence. Aim: To determine the extent to which the presence of additional structural and functional anomalies can help to identify those children with an undiagnosed syndrome. Methods: Secondary data analysis was performed using data from 1701 children born with an orofacial cleft, collected as part of a longitudinal cohort study, the Cleft Collective. Data were collected between 2013 and 2023 across the United Kingdom. The prevalence of structural and functional anomalies and syndromes were explored using descriptive statistics. Logistic regression was used to determine the extent to which anomalies can predict syndromic status. Results: A syndrome and/or sequence was reported in 20.5% children. Among children who reported five or more anomalies, the prevalence of a diagnosed syndrome was 81.5%. When adjusting for cleft subtype and sex, in 27 out of 32 anomalies tested, strong evidence was found to suggest increased odds of having a syndrome if the specific anomaly was present compared to if the anomaly was absent (p-values ranged between 1.4 × 10−30 and 0.002). Conclusions: Children born with a cleft who present with two or more anomalies are much more likely to have a syndrome than those with fewer anomalies and should be prioritised for genetic screening and counselling. [ABSTRACT FROM AUTHOR]

Background: Various vitamins and minerals have been implicated in the aetiology of depression. Objective: To estimate the effects of micronutrient exposures on major depressive disorder (MDD) and recurrent depression (rMDD) using Mendelian randomisation (MR), a method using genetic data to estimate causal effects given certain assumptions. Methods: We undertook a comprehensive bidirectional MR study of multiple micronutrient exposures on MDD and rMDD. Summary statistics were obtained from the Psychiatric Genomics Consortium (PGC) genome-wide association studies (GWASs) of MDD (cases = 116,209; controls = 314,566) and rMDD (cases = 17,451; controls = 62,482). Results: None of the micronutrients with available genetic instruments were strongly associated with MDD or rMDD using traditional MR methods. However, using methods to increase analytical power by accounting for genetically correlated variants (e.g., cIVW) highlighted five micronutrients with possible causal effects. Point estimates for rMDD were the largest magnitude, with three micronutrients suggestive of a protective effect: serum iron (ORcIVW 0.90 per SD increase; 95% CI 0.85–0.95; p = 0.0003); erythrocyte copper (ORcIVW 0.97; 95% CI 0.95–0.99; p = 0.0004); and 25(OH) vitamin D (ORcIVW 0.81; 0.66–0.99; p = 0.04). Apparent adverse effects of increased selenium on the risk of MDD (ORcIVW 1.03; 95% CI 1.02–1.05; p = 0.0003) and rMDD (ORcIVW 1.08; 95% CI 1.00–1.08; p = 0.06), and serum magnesium on rMDD (ORcIVW 1.21; 1.01–1.44; p = 0.04); were less consistent between methods and may be driven by pleiotropy. Conclusions: Our results suggest weak evidence for a protective effect of iron, copper and 25(OH)D on major depressive outcomes, with mixed evidence for selenium and magnesium. There was no evidence to support a causal effect of any other micronutrients on MDD or rMDD, although genetic instruments were lacking, with insufficient power to detect small but important effects. Future micronutrient supplementation trials should ensure ample statistical power given modest causal effect estimates and consider potential risks of supplementation, as some micronutrient effect estimates suggested potential harm in excess. [ABSTRACT FROM AUTHOR]

Objectives: This study explored the familiarity, perceptions and confidence of Australian radiology clinicians involved in reading screening mammograms, regarding artificial intelligence (AI) applications in breast cancer detection. Methods: Sixty-five radiologists, breast physicians and radiology trainees participated in an online survey that consisted of 23 multiple choice questions asking about their experience and familiarity with AI products. Furthermore, the survey asked about their confidence in using AI outputs and their preference for AI modes applied in a breast screening context. Participants' responses to questions were compared using Pearson's χ 2 test. Bonferroni-adjusted significance tests were used for pairwise comparisons. Results: Fifty-five percent of respondents had experience with AI in their workplaces, with automatic density measurement powered by machine learning being the most familiar AI product (69.4%). The top AI outputs with the highest ranks of perceived confidence were 'Displaying suspicious areas on mammograms with the percentage of cancer possibility' (67.8%) and 'Automatic mammogram classification (normal, benign, cancer, uncertain)' (64.6%). Radiology and breast physicians preferred using AI as second-reader mode (75.4% saying 'somewhat happy' to 'extremely happy') over triage (47.7%), pre-screening and first-reader modes (both with 26.2%) (P < 0.001). Conclusion: The majority of screen readers expressed increased confidence in utilising AI for highlighting suspicious areas on mammograms and for automatically classifying mammograms. They considered AI as an optimal second-reader mode being the most ideal use in a screening program. The findings provide valuable insights into the familiarities and expectations of radiologists and breast clinicians for the AI products that can enhance the effectiveness of the breast cancer screening programs, benefitting both healthcare professionals and patients alike. What is known about the topic? Artificial intelligence (AI) holds promise in providing computer-aided detection in health care, however, current research suggests that standalone AI applications in clinical practice fall short of matching the accuracy of a single radiologist. What does this paper add? The study showed a significant preference among clinicians for using AI as a supplementary tool, serving as a second-reader. Such an integrated approach, where AI aids in flagging suspicious areas on mammograms or offers automatic classification, reflects the ideal cooperation between breast screening readers and AI systems. What are the implications for practitioners? These insights shed light on clinicians' familiarity with and expectations of AI tools that can boost the effectiveness of breast screening programs. [ABSTRACT FROM AUTHOR]

Background Colorectal cancer (CRC) is the third-most-common cancer worldwide and its rates are increasing. Elevated body mass index (BMI) is an established risk factor for CRC, although the molecular mechanisms behind this association remain unclear. Using the Mendelian randomization (MR) framework, we aimed to investigate the mediating effects of putative biomarkers and other CRC risk factors in the association between BMI and CRC. Methods We selected as mediators biomarkers of established cancer-related mechanisms and other CRC risk factors for which a plausible association with obesity exists, such as inflammatory biomarkers, glucose homeostasis traits, lipids, adipokines, insulin-like growth factor 1 (IGF1), sex hormones, 25-hydroxy-vitamin D, smoking, physical activity (PA) and alcohol consumption. We used inverse-variance weighted MR in the main univariable analyses and performed sensitivity analyses (weighted-median, MR–Egger, Contamination Mixture). We used multivariable MR for the mediation analyses. Results Genetically predicted BMI was positively associated with CRC risk [odds ratio per SD (5 kg/m2) = 1.17, 95% CI: 1.08–1.24, P -value = 1.4 × 10−5] and robustly associated with nearly all potential mediators. Genetically predicted IGF1, fasting insulin, low-density lipoprotein cholesterol, smoking, PA and alcohol were associated with CRC risk. Evidence for attenuation was found for IGF1 [explained 7% (95% CI: 2–13%) of the association], smoking (31%, 4–57%) and PA (7%, 2–11%). There was little evidence for pleiotropy, although smoking was bidirectionally associated with BMI and instruments were weak for PA. Conclusions The effect of BMI on CRC risk is possibly partly mediated through plasma IGF1, whereas the attenuation of the BMI–CRC association by smoking and PA may reflect confounding and shared underlying mechanisms rather than mediation. [ABSTRACT FROM AUTHOR]

Meta-analyses combine the results of multiple studies of a common question. Approaches based on effect size estimates from each study are generally regarded as the most informative. However, these methods can only be used if comparable effect sizes can be computed from each study, and this may not be the case due to variation in how the studies were done or limitations in how their results were reported. Other methods, such as vote counting, are then used to summarize the results of these studies, but most of these methods are limited in that they do not provide any indication of the magnitude of effect. We propose a novel plot, the albatross plot, which requires only a 1-sided P value and a total sample size from each study (or equivalently a 2-sided P value, direction of effect and total sample size). The plot allows an approximate examination of underlying effect sizes and the potential to identify sources of heterogeneity across studies. This is achieved by drawing contours showing the range of effect sizes that might lead to each P value for given sample sizes, under simple study designs. We provide examples of albatross plots using data from previous meta-analyses, allowing for comparison of results, and an example from when a meta-analysis was not possible.

Simple Summary: Breast cancer is one of the leading causes of cancer-related death in women. The early detection of breast cancer with screening mammograms plays a pivotal role in reducing mortality rates. Although population-based double-reading screening mammograms have reduced mortality by over 31% in women with breast cancer in Europe, continuing this program is difficult due to the shortage of radiologists. Artificial intelligence (AI) is an emerging technology that has provided promising results in medical imaging for disease detection. This study investigates the performance of AI models on an Australian mammographic database, demonstrating how transfer learning from a USA mammographic database to an Australian one, contrast enhancement on mammographic images and the quality of training data according to radiologists' concordance can improve breast cancer diagnosis. Our proposed methodology offers a more efficacious approach for AI to contribute to radiologists' decision making when interpreting mammography images. This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models. [ABSTRACT FROM AUTHOR]

Objective Radiologists can detect the gist of abnormal based on their rapid initial impression on a mammogram (ie, global gist signal [GGS]). This study explores (1) whether global radiomic (ie, computer-extracted) features can predict the GGS; and if so, (ii) what features are the most important drivers of the signals. Methods The GGS of cases in two extreme conditions was considered: when observers detect a very strong gist (high-gist) and when the gist of abnormal was not/poorly perceived (low-gist). Gist signals/scores from 13 observers reading 4191 craniocaudal mammograms were collected. As gist is a noisy signal, the gist scores from all observers were averaged and assigned to each image. The high-gist and low-gist categories contained all images in the fourth and first quartiles, respectively. One hundred thirty handcrafted global radiomic features (GRFs) per mammogram were extracted and utilized to construct eight separate machine learning random forest classifiers (All , Normal , Cancer , Prior-1 , Prior-2 , Missed , Prior-Visible , and Prior-Invisible) for characterizing high-gist from low-gist images. The models were trained and validated using the 10-fold cross-validation approach. The models' performances were evaluated by the area under receiver operating characteristic curve (AUC). Important features for each model were identified through a scree test. Results The Prior-Visible model achieved the highest AUC of 0.84 followed by the Prior-Invisible (0.83), Normal (0.82), Prior-1 (0.81), All (0.79), Prior-2 (0.77), Missed (0.75), and Cancer model (0.69). Cluster shade , standard deviation , skewness , kurtosis , and range were identified to be the most important features. Conclusions Our findings suggest that GRFs can accurately classify high- from low-gist images. Advances in knowledge Global mammographic radiomic features can accurately predict high- from low-gist images with five features identified to be valuable in describing high-gist images. These are critical in providing better understanding of the mammographic image characteristics that drive the strength of the GGSs which could be exploited to advance breast cancer (BC) screening and risk prediction, enabling early detection and treatment of BC thereby further reducing BC-related deaths. [ABSTRACT FROM AUTHOR]

Background: Sedentary behaviours have been associated with increased risks of some common cancers in epidemiological studies; however, it is unclear if these associations are causal. Methods: We used univariable and multivariable two-sample Mendelian randomisation (MR) to examine potential causal relationships between sedentary behaviours and risks of breast, colorectal and prostate cancer. Genetic variants associated with self-reported leisure television watching and computer use were identified from a recent genome-wide association study (GWAS). Data related to cancer risk were obtained from cancer GWAS consortia. A series of sensitivity analyses were applied to examine the robustness of the results to the presence of confounding. Results: A 1-standard deviation (SD: 1.5h/day) increment in hours of television watching increased risk of breast cancer (OR per 1-SD: 1.15, 95% confidence interval [CI]: 1.05–1.26) and colorectal cancer (OR per 1-SD: 1.32, 95% CI: 1.16– 1.49) while there was little evidence of an association for prostate cancer risk (OR per 1-SD: 0.94, 95% CI: 0.84–1.06). After adjusting for years of education, the effect estimates for television watching were attenuated (breast cancer, OR per 1-SD: 1.08, 95% CI: 0.92–1.27; colorectal cancer, OR per 1-SD: 1.08, 95% CI: 0.90–1.31). Post hoc analyses showed that years of education might have a possible confounding and mediating role in the association between television watching with breast and colorectal cancer. Consistent results were observed for each cancer site according to sex (colorectal cancer), anatomical subsites and cancer subtypes. There was little evidence of associations between genetically predicted computer use and cancer risk. Conclusions: Our univariable analysis identified some positive associations between hours of television watching and risks of breast and colorectal cancer. However, further adjustment for additional lifestyle factors especially years of education attenuated these results. Future studies using objective measures of exposure can provide new insights into the possible role of sedentary behaviour in cancer development. [ABSTRACT FROM AUTHOR]

Objectives: To determine the UK prevalence of behavioral problems in 5-year-old children born with isolated or syndromic cleft lip and/or palate (CL/P) compared to the general population and identify potentially associated factors. Design: Observational study using questionnaire data from the Cleft Collective 5-Year-Old Cohort study and three general population samples. Main Outcome Measure: The Strengths and Difficulties Questionnaire (SDQ). Participants: Mothers of children (age: 4.9-6.8 years) born with CL/P (n = 325). UK general population cohorts for SDQ scores were: Millennium Cohort Study (MCS) (n = 12 511), Office of National Statistics (ONS) normative school-age SDQ data (n = 5855), and Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 9386). Results: By maternal report, 14.2% of children born with CL/P were above clinical cut-off for behavioral problems, which was more likely than in general population samples: 7.5% of MCS (OR = 2.05 [1.49-2.82], P < 0.001), 9.8% of ONS (OR = 1.52 [1.10-2.09], P = 0.008), and 6.6% of ALSPAC (OR = 2.34 [1.70-3.24], P < 0.001). Children in the Cleft Collective had higher odds for hyperactivity, emotional and peer problems, and less prosocial behaviors. Maternal stress, lower maternal health-related quality of life and family functioning, receiving government income support, and maternal smoking showed evidence of association (OR range: 4.41-10.13) with behavioral problems, along with maternal relationship status, younger age, and lower education (OR range: 2.34-3.73). Conclusions: Findings suggest elevated levels of behavioral problems in children born with CL/P compared to the general population with several associated maternal factors similar to the general population. [ABSTRACT FROM AUTHOR]

Background: Atopic dermatitis (AD) accounts for a large proportion of the burden of skin disease, with a prevalence of around 10% among adults worldwide. In addition, systematic reviews and meta‐analyses have found that AD is associated with cancer risk at several sites; if found to be causal this could highlight potential treatment targets to reduce cancer risk. Objectives: To assess the potential causative link between AD and 14 site‐specific cancers in a two‐sample randomization study. Methods: From the largest genome‐wide association study (GWAS) of AD (10,788 cases and 30,047 non‐cases), genetic variants highly associated (p < 5 × 10−8) with AD in the European population were selected as instrumental variables (IVs). Data from large cancer consortia, as well as the UK Biobank study (n = 442,239) and the FinnGen study (n = 218,792), were employed to assess genetic associations with 14 site‐specific cancers and overall cancer. A set of complementary approaches and sensitivity analyses were carried out to examine the robustness of our results. In addition, associations for the same cancer site from different data sources were combined using meta‐analyses. Results: We discovered no strong causal evidence of AD on the risk of overall cancer, with effect estimates close to zero. After the Benjamini–Hochberg correction, the inverse‐variance weighted method indicated no association between AD and overall cancer risk in both the UK Biobank (OR, 1.00; 95% CI, 0.94–1.06; FDR, 0.98) and FinnGen studies (OR, 0.96; 95% CI, 0.92–1.02; FDR, 0.68). No strong evidence of an association was found between genetically predicted AD and the risk of any site‐specific cancers. Conclusions: Our MR investigation does not support a causal effect of AD on cancer risk. This finding has important implications for the prevention and management of both AD and cancer, as it reduces the concern of potential adverse effects of AD on cancer outcomes. [ABSTRACT FROM AUTHOR]

Mercury has high industrial utility and is present in many products, and environmental contamination and occupational exposure are widespread. There are numerous biological systems involved in the absorption, metabolism, and excretion of Hg, and it is possible that some systems may be impacted by genetic variation. If so, genotype may affect tissue concentrations of Hg and subsequent toxic effects. Genome-wide association testing was performed on blood Hg samples from pregnant women of the Avon Longitudinal Study of Parents and Children (n = 2893) and children of the Human Early Life Exposome (n = 1042). Directly-genotyped single-nucleotide polymorphisms (SNPs) were imputed to the Haplotype Reference Consortium r1.1 panel of whole genotypes and modelled againstlog-transformed Hg. Heritability was estimated using linkage disequilibrium score regression. The heritability of Hg was estimated as 24.0% (95% CI: 16.9% to 46.4%) in pregnant women, but could not be determined in children. There were 16 SNPs associated with Hg in pregnant women above a suggestive p-value threshold (p < 1 × 10−5), and 21 for children. However, no SNP passed this threshold in both studies, and none were genome-wide significant (p < 5 × 10−8). SNP-Hg associations were highly discordant between women and children, and this may reflect differences in metabolism, a gene–age interaction, or dose–response effects. Several suggestive variants had plausible links to Hg metabolism, such as rs146099921 in metal transporter SLC39A14, and two variants (rs28618224, rs7154700) in potassium voltage-gated channel genes. The findings would benefit from external validation, as suggestive results may contain both true associations and false positives. [ABSTRACT FROM AUTHOR]

Background: The risk of childhood behavioural and psychiatric diseases could be substantially reduced if modifiable risk factors for these disorders were identified. The critical period for many of these exposures is likely to be in utero as this is the time when brain development is most rapid. However, due to confounding and other limitations of traditional epidemiological studies, identification of causal risk factors has proved challenging and on the whole research in this area has not been fruitful. Scope: In this review, we highlight several alternative approaches including; comparisons across settings, the use of negative controls and natural experiments, which includes migration studies, studies of individuals conceived using in vitro fertilisation and not least Mendelian randomisation. We have illustrated these approaches using examples of behavioural and psychiatric disorders. Conclusion: By having these approaches outlined together in one review, researchers can consider which of these methods would be most suitable for their study question. We have particularly focussed on Mendelian randomisation, as this is a relatively novel concept, in doing so, we have illustrated the concept and discussed the implementation and the limitations of this approach.

Infection prevention and control (IPC) practices are key to preventing and controlling the spread of pathogens in medical imaging departments (MIDs). The objective of this scoping review was to synthesise information about current research in MID regarding IPC and to use the Systems Engineering Initiative for Patient Safety (SEIPS) model to identify the work system factors ('persons', 'organisation', 'tools and technology', 'tasks' and 'environment') influencing the practice of IPC, in order to better understand challenges and facilitators that affect IPC in MID. Predefined search terms and medical subject headings relating to IPC in the medical imaging setting were used to search 3 databases. A total of 46 publications met the inclusion criteria, which combined, encompassed all five SEIPS domains influencing IPC. The literature supports the interrelated nature of the five SEIPS domains, and influence to one another. Hand hygiene was a major focus of publications. Mechanisms of infection in contrast-enhanced computed tomography were most reported, with human error, lack of education, and issues associated with devices and processes mechanisms found to influence IPC breaches. A systems approach, such as the SEIPS model, is useful for understanding barriers and hence opportunities for improvement of IPC in the medical imaging setting. Future studies should address individuals' decision-making processes in the medical imaging setting, and a greater focus should be placed into the procedural steps, education and tools used for contrast media administration. Critical relevance statement: A systems approach, such as the Systems Engineering Initiative for Patient Safety model, is useful for understanding barriers and hence opportunities for improvement of IPC in the medical imaging setting. Key points: IPC in the medical imaging setting would benefit from a systems approach. The role of education and monitoring of IPC compliance requires further research. Geographical location is a key variable in IPC research in medical imaging. [ABSTRACT FROM AUTHOR]