Predictors of radiation dose for uterine artery embolisation are angiography system-dependent.

This study sought to achieve radiation dose reductions for patients receiving uterine artery embolisation (UAE) by evaluating radiation dose measurements for the preceding generation (Allura) and upgraded (Azurion) angiography system. Previous UAE regression models in the literature could not be applied to this centre's practice due to being based on different angiography systems and radiation dose predictor variables. The aims of this study were to establish whether radiation dose is reduced with the upgraded angiography system and to develop a regression model to determine predictors of radiation dose specific to the upgraded angiography system. A comparison between Group I (Allura,n= 95) and Group II (Azurion,n= 95) demonstrated a significant reduction in kerma-area product (KAP) and Ka, r (reference air kerma) by 63% (143.2 Gy cm2vs 52.9 Gy cm2;P< 0.001,d= 0.8) and 67% (0.6 Gy vs 0.2 Gy;P< 0.001,d= 0.8), respectively. The multivariable linear regression (MLR) model identified the UAE radiation dose predictors for KAP on the upgraded angiography system as total fluoroscopy dose, Ka, r, and total uterus volume. The predictive accuracy of the MLR model was assessed using a Bland-Altman plot. The mean difference was 0.39 Gy cm2and the limits of agreement were +28.49 and -27.71 Gy cm2, and thus illustrated no proportional bias. The resultant MLR model was considered system-dependent and validated the upgraded angiography system and its advance capabilities to significantly reduce radiation dose. Interventional radiologist and interventional radiographer familiarisation of the system's features and the implementation of the newly established MLR model would further facilitate dose optimisation for all centres performing UAE procedures using the upgraded angiography system.

Increasing iterative reconstruction strength at low tube voltage in coronary CT angiography protocols using 3D-printed and Catphan® 500 phantoms.

PURPOSE: The purpose of this study was to investigate the effect of increasing iterative reconstruction (IR) algorithm strength at different tube voltages in coronary computed tomography angiography (CCTA) protocols using a three-dimensional (3D)-printed and Catphan® 500 phantoms. METHODS: A 3D-printed cardiac insert and Catphan 500 phantoms were scanned using CCTA protocols at 120 and 100 kVp tube voltages. All CT acquisitions were reconstructed using filtered back projection (FBP) and Adaptive Statistical Iterative Reconstruction (ASIR) algorithm at 40% and 60% strengths. Image quality characteristics such as image noise, signal-noise ratio (SNR), contrast-noise ratio (CNR), high spatial resolution, and low contrast resolution were analyzed. RESULTS: There was no significant difference (P > 0.05) between 120 and 100 kVp measures for image noise for FBP vs ASIR 60% (16.6 ± 3.8 vs 16.7 ± 4.8), SNR of ASIR 40% vs ASIR 60% (27.3 ± 5.4 vs 26.4 ± 4.8), and CNR of FBP vs ASIR 40% (31.3 ± 3.9 vs 30.1 ± 4.3), respectively. Based on the Modulation Transfer Function (MTF) analysis, there was a minimal change of image quality for each tube voltage but increases when higher strengths of ASIR were used. The best measure of low contrast detectability was observed at ASIR 60% at 120 kVp. CONCLUSIONS: Changing the IR strength has yielded different image quality noise characteristics. In this study, the use of 100 kVp and ASIR 60% yielded comparable image quality noise characteristics to the standard CCTA protocols using 120 kVp of ASIR 40%. A combination of 3D-printed and Catphan® 500 phantoms could be used to perform CT dose optimization protocols.

Does Expectation of Abnormality Affect the Search Pattern of Radiologists When Looking for Pulmonary Nodules?

This experiment investigated whether there might be an effect on the visual search strategy of radiologists during image interpretation of the same adult chest radiographs when given different clinical information. Each of 17 experienced radiologists was asked to interpret a set of 57 (10 abnormal) posteroanterior chest images to identify the presence of pulmonary lesions using differing clinical information (leading to unknown, low and high expectations of prevalence). Eye position metrics (search time, dwell time and time to first fixation) were compared for normal and abnormal images, as well as between conditions. For all images, there was a significantly longer search time at high prevalence expectation compared to low prevalence expectation (W = 75.19, P = <0.0001). Mann-Whitney analysis of the abnormal images demonstrated that the dwell time on correctly identified lesions was significantly shorter at low prevalence expectation compared to both unknown (U = 364.5, P = 0.02) and high prevalence expectation (U = 397.0, P = 0.0002). Visual search patterns of radiologists appear to be affected by changing a priori information where such information fosters an expectation of abnormality.

Evidence-based practice in radiography: A strategy for shifting our culture.

Evidence-based practice (EBP) has a vital role to play in improving outcomes for patients, organisations and individual practitioners. Unfortunately, within diagnostic radiography, literature consistently demonstrates that positive EBP is not the norm. This editorial discusses a strategy for fostering cultural change within the profession to improve EBP.

Exploring software navigation tools for liver tumour angiography: a scoping review.

INTRODUCTION: Liver cancer presents a growing global health concern, necessitating advanced approaches for intervention. This review investigates the use and effectiveness of software navigation in interventional radiology for liver tumour procedures. METHODS: In accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted of the literature published between 2013 and 2023 sourcing articles through MEDLINE, Scopus, CINAHL and Embase. Eligible studies focused on liver cancer, utilised cone-beam computed tomography (CBCT), and employed software for intervention. Twenty-one articles were deemed eligible for data extraction and analysis. RESULTS: Categorised by type, software applications yielded diverse benefits. Feeder detection software significantly enhanced vessel identification, reducing non-target embolisation by up to 43%. Motion correction software demonstrated a 20% enhancement in image quality, effectively mitigating breathing-induced motion artefacts. Liver perfusion software facilitated efficient tumour targeting while simultaneously reducing the occurrence of side effects. Needle guide software enabled precise radiofrequency ablation needle placement. Additionally, these software applications provided detailed anatomical simulations. Overall, software integration resulted in shorter procedures, reduced radiation exposure and decreased contrast media usage. CONCLUSION: This scoping review highlights the innovative yet relatively underexplored role of software navigation for liver tumour procedures. The integration of software applications not only enhances procedural efficiency but also bolsters operator confidence, and contributes to improved patient outcomes. Despite the current lack of uniformity and standardisation, these software-driven advancements hold significant promise for transforming liver tumour interventions. To realise these benefits, further research is needed to explore the clinical impact and optimal utilisation of software navigation tools in interventional radiology.

Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.

OBJECTIVES: The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS: A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS: Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION: Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE: Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.

Optimizing mammography interpretation education: leveraging deep learning for cohort-specific error detection to enhance radiologist training.

Purpose: Accurate interpretation of mammograms presents challenges. Tailoring mammography training to reader profiles holds the promise of an effective strategy to reduce these errors. This proof-of-concept study investigated the feasibility of employing convolutional neural networks (CNNs) with transfer learning to categorize regions associated with false-positive (FP) errors within screening mammograms into categories of "low" or "high" likelihood of being a false-positive detection for radiologists sharing similar geographic characteristics. Approach: Mammography test sets assessed by two geographically distant cohorts of radiologists (cohorts A and B) were collected. FP patches within these mammograms were segmented and categorized as "difficult" or "easy" based on the number of readers committing FP errors. Patches outside 1.5 times the interquartile range above the upper quartile were labeled as difficult, whereas the remaining patches were labeled as easy. Using transfer learning, a patch-wise CNN model for binary patch classification was developed utilizing ResNet as the feature extractor, with modified fully connected layers for the target task. Model performance was assessed using 10-fold cross-validation. Results: Compared with other architectures, the transferred ResNet-50 achieved the highest performance, obtaining receiver operating characteristics area under the curve values of 0.933 ( ± 0.012 ) and 0.975 ( ± 0.011 ) on the validation sets for cohorts A and B, respectively. Conclusions: The findings highlight the feasibility of employing CNN-based transfer learning to predict the difficulty levels of local FP patches in screening mammograms for specific radiologist cohort with similar geographic characteristics.

Radiomic Analysis of Cohort-Specific Diagnostic Errors in Reading Dense Mammograms Using Artificial Intelligence.

OBJECTIVES: This study aims to investigate radiologists' interpretation errors when reading dense screening mammograms using a radiomics-based artificial intelligence approach. METHODS: Thirty-six radiologists from China and Australia read 60 dense mammograms. For each cohort, we identified normal areas that looked suspicious of cancer and the malignant areas containing cancers. Then radiomic features were extracted from these identified areas and random forest models were trained to recognize the areas that were most frequently linked to diagnostic errors within each cohort. The performance of the model and discriminatory power of significant radiomic features were assessed. RESULTS: We found that in the Chinese cohort, the AUC values for predicting false positives were 0.864 (CC) and 0.829 (MLO), while in the Australian cohort, they were 0.652 (CC) and 0.747 (MLO). For false negatives, the AUC values in the Chinese cohort were 0.677 (CC) and 0.673 (MLO), and in the Australian cohort, they were 0.600 (CC) and 0.505 (MLO). In both cohorts, regions with higher Gabor and maximum response filter outputs were more prone to false positives, while areas with significant intensity changes and coarse textures were more likely to yield false negatives. CONCLUSIONS: This cohort-based pipeline proves effective in identifying common errors for specific reader cohorts based on image-derived radiomic features. ADVANCES IN KNOWLEDGE: This study demonstrates that radiomics-based AI can effectively identify and predict radiologists' interpretation errors in dense mammograms, with distinct radiomic features linked to false positives and false negatives in Chinese and Australian cohorts.

Use of reporting templates for chest radiographs in a coronavirus disease 2019 context: measuring concordance of radiologists with three international templates.

Purpose: Reporting templates for chest radiographs (CXRs) for patients presenting or being clinically managed for severe acute respiratory syndrome coronavirus 2 [coronavirus disease 2019 (COVID-19)] has attracted advocacy from international radiology societies. We aim to explore the effectiveness and useability of three international templates through the concordance of, and between, radiologists reporting on the presence and severity of COVID-19 on CXRs. Approach: Seventy CXRs were obtained from a referral hospital, 50 from patients with COVID-19 (30 rated "classic" COVID-19 appearance and 20 "indeterminate") and 10 "normal" and 10 "alternative pathology" CXRs. The recruited radiologists were assigned to three test sets with the same CXRs but with different template orders. Each radiologist read their test set three times and assigned a classification to the CXR using the Royal Australian New Zealand College of Radiology (RANZCR), British Society of Thoracic Imaging (BSTI), and Modified COVID-19 Reporting and Data System (Dutch; mCO-RADS) templates. Inter-reader variability and intra-reader variability were measured using Fleiss' kappa coefficient. Results: Twelve Australian radiologists participated. The BSTI template had the highest inter-reader agreement (0.46; "moderate" agreement), followed by RANZCR (0.45) and mCO-RADS (0.32). Concordance was driven by strong agreement in "normal" and "alternative" classifications and was lowest for "indeterminate." General consistency was observed across classifications and templates, with intra-reader variability ranging from "good" to "very good" for COVID-19 CXRs (0.61), "normal" CXRs (0.76), and "alternative" (0.68). Conclusions: Reporting templates may be useful in reducing variation among radiology reports, with intra-reader variability showing promise. Feasibility and implementation require a wider approach including referring and treating doctors plus the development of training packages for radiologists specific to the template being used.

Screening mammography: test set data can reasonably describe actual clinical reporting.

PURPOSE: To establish the extent to which test set reading can represent actual clinical reporting in screening mammography. MATERIALS AND METHODS: Institutional ethics approval was granted, and informed consent was obtained from each participating screen reader. The need for informed consent with respect to the use of patient materials was waived. Two hundred mammographic examinations were selected from examinations reported by 10 individual expert screen readers, resulting in 10 reader-specific test sets. Data generated from actual clinical reports were compared with three test set conditions: clinical test set reading with prior images, laboratory test set reading with prior images, and laboratory test set reading without prior images. A further set of five expert screen readers was asked to interpret a common set of images in two identical test set conditions to establish a baseline for intraobserver variability. Confidence scores (from 1 to 4) were assigned to the respective decisions made by readers. Region-of-interest (ROI) figures of merit (FOMs) and side-specific sensitivity and specificity were described for the actual clinical reporting of each reader-specific test set and were compared with those for the three test set conditions. Agreement between pairs of readings was performed by using the Kendall coefficient of concordance. RESULTS: Moderate or acceptable levels of agreement were evident (W = 0.69-0.73, P < .01) when describing group performance between actual clinical reporting and test set conditions that were reasonably close to the established baseline (W = 0.77, P < .01) and were lowest when prior images were excluded. Higher median values for ROI FOMs were demonstrated for the test set conditions than for the actual clinical reporting values; this was possibly linked to changes in sensitivity. CONCLUSION: Reasonable levels of agreement between actual clinical reporting and test set conditions can be achieved, although inflated sensitivity may be evident with test set conditions.

Markers of good performance in mammography depend on number of annual readings.

PURPOSE: To explore relationships between reader performance and reader characteristics in mammography for specific radiologist groupings on the basis of annual number of readings. MATERIALS AND METHODS: The institutional review board approved the study and waived the need for patient consent to use all images. Readers gave informed consent. One hundred sixteen radiologists independently reviewed 60 mammographic cases: 20 cases with cancer and 40 cases with normal findings. Readers located any visualized cancer, and levels of confidence were scored from 1 to 5. A jackknifing free response operating characteristic (JAFROC) method was used, and figures of merit along with sensitivity and specificity were correlated with reader characteristics by using Spearman techniques and standard multiple regressions. RESULTS: Reader performance was positively correlated with number of years since qualification as a radiologist (P ≤ .01), number of years reading mammograms (P ≤ .03), and number of readings per year (P ≤ .0001). The number of years since qualification as a radiologist (P ≤ .004) and number of years of reading mammograms (P ≤ .002) were negatively related to JAFROC values for radiologists with annual volumes of less than 1000 mammographic readings. For individuals with more than 5000 mammographic readings per year, JAFROC values were positively related to the number of years that the reader was qualified as a radiologist (P ≤ .01), number of years of reading mammograms (P ≤ .002), and number of hours per week of reading mammograms (P ≤ .003). Number of mammographic readings per year was positively related with JAFROC scores for readers with an annual volume between 1000 and 5000 readings (P ≤ .03). Differences in JAFROC scores appear to be more related to specificity than location sensitivity, with the former demonstrating significant relationships with four of the five characteristics analyzed, whereas no relationships were shown for the latter. CONCLUSION: Radiologists' determinants of performance are associated with annual reading volumes. Ability to recognize normal images is a discriminating factor in individuals with a high volume of mammographic readings.

Breast screen new South wales generally demonstrates good radiologic viewing conditions.

This study measured reading workstation monitors and the viewing environment currently available within BreastScreen New South Wales (BSNSW) centres to determine levels of adherence to national and international guidelines. Thirteen workstations from four BSNSW service centres were assessed using the American Association of Physicists in Medicine Task Group 18 Quality Control test pattern. Reading workstation monitor performance and ambient light levels when interpreting screening mammographic images were assessed using spectroradiometer CS-2000 and chroma meter CL-200. Overall, radiologic monitors within BSNSW were operating at good acceptable levels. Some non-adherence to published guidelines included the percentage difference in maximum luminance between pairs of primary monitors at individual workstations (61.5 % or 30.8 % of workstations depending on specific guidelines), maximum luminance (23.1 % of workstations), luminance non-uniformity (11.5 % of workstations) and minimum luminance (3.8 % of workstations). A number of ambient light measurements did not comply with the only available evidence-based guideline relevant to the methodology used in this study. Larger ambient light variations across sites are shown when monitors were switched off, suggesting that differences in ambient lighting between sites can be masked when a standard mammogram is displayed for photometric measurements. Overall, BSNSW demonstrated good adherence to available guidelines, although some non-compliance has been shown. Recently updated United Kingdom and Australian guidelines should help reduce confusion generated by the plethora and sometimes dated nature of currently available recommendations.

Assessing the implementation of COVID-19 structured reporting templates for chest radiography: a scoping review.

Objective: One of the common modalities used in imaging COVID-19 positive patients is chest radiography (CXR), and serves as a valuable imaging method to diagnose and monitor a patients' condition. Structured reporting templates are regularly used for the assessment of COVID-19 CXRs and are supported by international radiological societies. This review has investigated the use of structured templates for reporting COVID-19 CXRs. Methods: A scoping review was conducted on literature published between 2020 and 2022 using Medline, Embase, Scopus, Web of Science, and manual searches. An essential criterion for the inclusion of the articles was the use of reporting methods employing either a structured quantitative or qualitative reporting method. Thematic analyses of both reporting designs were then undertaken to evaluate utility and implementation. Results: Fifty articles were found with the quantitative reporting method used in 47 articles whilst 3 articles were found employing a qualitative design. Two quantitative reporting tools (Brixia and RALE) were used in 33 studies, with other studies using variations of these methods. Brixia and RALE both use a posteroanterior or supine CXR divided into sections, Brixia with six and RALE with four sections. Each section is scaled numerically depending on the level of infection. The qualitative templates relied on selecting the best descriptor of the presence of COVID-19 radiological appearances. Grey literature from 10 international professional radiology societies were also included in this review. The majority of the radiology societies recommend a qualitative template for reporting COVID-19 CXRs. Conclusion: Most studies employed quantitative reporting methods which contrasted with the structured qualitative reporting template advocated by most radiological societies. The reasons for this are not entirely clear. There is also a lack of research literature on both the implementation of the templates or comparing both template types, indicating that the use of structured radiology reporting types may be an underdeveloped clinical strategy and research methodology. Advances in knowledge: This scoping review is unique in that it has undertaken an examination of the utility of the quantitative and qualitative structured reporting templates for COVID-19 CXRs. Moreover, through this review, the material examined has allowed a comparison of both instruments, clearly showing the favoured style of structured reporting by clinicians. At the time of the database interrogation, there were no studies found had undertaken such examinations of both reporting instruments. Moreover, due to the enduring influence of COVID-19 on global health, this scoping review is timely in examining the most innovative structured reporting tools that could be used in the reporting of COVID-19 CXRs. This report could assist clinicians in decision-making regarding templated COVID-19 reports.

Assessing the knowledge of CT radiographers regarding how CT parameters affect patient dose and image quality.

PURPOSE: The objective of this study was to assess the current knowledge of CT radiographers regarding the optimization of CT parameters and their consequential effects on both patient dose and image quality. METHOD: A nationwide, cross sectional study was conducted from the 2nd of January 2023 to 1st of March 2023 to evaluate CT radiographers' knowledge in managing CT parameters in Jordan. Recruitment involved convenience sampling where radiographers were invited to participate and complete the questionnaire. Descriptive statistics were used to report the normalized knowledge scores. Student's t-test and ANOVA were used to investigate and compare the outcomes between different subgroups. A forward stepwise linear regression was used to investigate the influence of a number of technologist related factors on the knowledge score. RESULTS: Three hundred and fifty-seven radiographers participated in the study, with a mean knowledge score of 69.0%. Participants with an academic master's degree had a significantly higher score of 72.1% compared to the ones with a diploma degree, with a score of 66.8% (p = 0.026). No statistically significant difference was found between radiographers that received additional training and the ones that did not. Furthermore, when investigating the effects of academic education, working sector, additional training and years of experience, only education had a statistically significant impact on the knowledge score. CONCLUSION: The results demonstrate that radiographers have an overall good understanding of CT parameters, with academic education having a significant influence on their performance.

Investigating the impact of cognitive biases in radiologists' image interpretation: A scoping review.

RATIONALE AND OBJECTIVE: Image interpretation is a fundamental aspect of radiology. The treatment and management of patients relies on accurate and timely imaging diagnosis. However, errors in radiological reports can negatively impact on patient health outcomes. These misdiagnoses can be caused by several different errors, but cognitive biases account for 74 % of all image interpretation errors. There are many biases that can impact on a radiologist's perception and cognitive processes. Several recent narrative reviews have discussed these cognitive biases and have offered possible strategies to mitigate their effects. However, these strategies remain untested. Therefore, the purpose of this scoping review is to evaluate the current knowledge on the extent that cognitive biases impact on medical image interpretation. MATERIAL AND METHODS: Scopus and Medline Databases were searched using relevant keywords to identify papers published between 2012 and 2022. A subsequent hand search of the narrative reviews was also performed. All studies collected were screened and assessed against the inclusion and exclusion criteria. RESULTS: Twenty-four publications were included and categorised into five main themes: satisfaction of search, availability bias, hindsight bias, framing bias and other biases. From these studies, there were mixed results regarding the impact of cognitive biases, highlighting the need for further investigation in this area. Moreover, the limited and untested debiasing methods offered by a minority of the publications and narrative reviews also suggests the need for further research. The potential of role of artificial intelligence is also highlighted to further assist radiologists in identifying and mitigating these cognitive biases. CONCLUSION: Cognitive biases can impact radiologists' image interpretation, however the effectiveness of debiasing strategies remain largely untested.

The use of radiomics in magnetic resonance imaging for the pre-treatment characterisation of breast cancers: A scoping review.

Radiomics is an emerging field that aims to extract and analyse a comprehensive set of quantitative features from medical images. This scoping review is focused on MRI-based radiomic features for the molecular profiling of breast tumours and the implications of this work for predicting patient outcomes. A thorough systematic literature search and outcome extraction were performed to identify relevant studies published in MEDLINE/PubMed (National Centre for Biotechnology Information), EMBASE and Scopus from 2015 onwards. The following information was retrieved from each article: study purpose, study design, extracted radiomic features, machine learning technique(s), sample size/characteristics, statistical result(s) and implications on patient outcomes. Based on the study purpose, four key themes were identified in the included 63 studies: tumour subtype classification (n = 35), pathologically complete response (pCR) prediction (n = 15), lymph node metastasis (LNM) detection (n = 7) and recurrence rate prediction (n = 6). In all four themes, reported accuracies widely varied among the studies, for example, area under receiver characteristics curve (AUC) for detecting LNM ranged from 0.72 to 0.91 and the AUC for predicting pCR ranged from 0.71 to 0.99. In all four themes, combining radiomic features with clinical data improved the predictive models. Preliminary results of this study showed radiomics potential to characterise the whole tumour heterogeneity, with clear implications for individual-targeted treatment. However, radiomics is still in the pre-clinical phase, currently with an insufficient number of large multicentre studies and those existing studies are often limited by insufficient methodological transparency and standardised workflow. Consequently, the clinical translation of existing studies is currently limited.

RRIMS: Radiation Risk In Mammography Screening - model evaluation.

OBJECTIVE: The Radiation Risk In Mammography Screening (RRIMS) model was introduced as a novel tool to help females accurately calculate their lifetime mean glandular dose (MGD) and estimate their population-level risk of radiation-induced breast cancer incidence and mortality. METHODS: The model's accuracy was evaluated by comparing the received MGD of 317 women who had undergone a total of 733 visits across one to four rounds of screening. This was achieved by comparing the RRIMS predicted dose values with the same examination dose calculated manually by hand. Qualitative and quantitative statistical analyses were performed to assess the percentage difference (% diff) or agreement between the two values. RESULTS: Qualitative statistical analysis using the Bland-Altman plots demonstrated a statistically significant bias for the % diff between the manually calculated and RRIMS predicted dose values, where the mean (bias) was -2.02% with an upper and lower limit of agreement of 40.24% and -44.27%, respectively. Quantitative statistical analysis revealed an intraclass correlation coefficient (ICC, 3,1) of 0.64 (p-value < 0.001) and a Kendall's W of 0.83 (p-value < 0.001). CONCLUSION: The results indicate a statistically significant and reasonably good level of agreement between the manually calculated vs RRIMS predicted dose values. This work was focused on one of the major mammography equipment manufacturers that is Hologic, however there is potential for a multivendor applicability study of this model with future iterations. This will further improve upon this innovative dose and risk prediction tool that can empower healthcare professionals when making informed decisions and enhance patient care. ADVANCES IN KNOWLEDGE: This paper assesses the precision of the dose and risk model that our team has previously established. The results bring us one step closer to providing females and clinicians with a useful tool that can help explain and contextualise the benefits and risks associated with screening mammography.

Panoramic radiographs and dental patients with Down syndrome: A scoping review.

INTRODUCTION: Life expectancy for people with Down syndrome (DS) has increased to 60 years, although poor oral health affects their quality of life. Panoramic radiographs (PRs) are usually well-tolerated by people with DS and can provide valuable diagnostic data for treatment planning and research. Methods A scoping review of peer-reviewed articles published between 1971and 2021 was conducted in accordance with the PRISMA extension for scoping reviews to determine the scope of use of PRs for people with DS. RESULTS: 937 papers were screened, and 52 papers were included and charted into seven categories in this review. A high prevalence of tooth agenesis (TA) and other anomalies were reported in 45% of papers. Severe periodontal disease was considered characteristic of DS in the 1970s-80s and the benefit of time-consuming treatment was questioned. Since 2000 case reports illustrate that improved oral care, orthodontic treatment, and dental implants under local or general anaesthetic can improve the quality of life for people with DS. CONCLUSION: PRs play an important role in the diagnosis of anomalies, periodontal disease, and implant planning for patients with DS. This review highlights the gaps in research of caries, pathology, TMJ, systemic disease indicators, and guidelines for dentists. Systematic PR viewing, with a knowledge of characteristic features of DS, will assist diagnosis of pathology and improve comprehensive dental care treatment planning for children and adults with DS.

Incidence, mortality, survival, and disease burden of breast cancer in China compared to other developed countries.

Breast cancer was the most diagnosed malignant neoplasm and the second leading cause of cancer mortality among Chinese females in 2020. Increased risk factors and widespread adoption of westernized lifestyles have resulted in an upward trend in the occurrence of breast cancer. Up to date knowledge on the incidence, mortality, survival, and burden of breast cancer is essential for optimized cancer prevention and control. To better understand the status of breast cancer in China, this narrative literature review collected data from multiple sources, including studies obtained from the PubMed database and text references, national annual cancer report, government cancer database, Global Cancer Statistics 2020, and Global Burden of Disease study (2019). This review provides an overview of the incidence, mortality, and survival rates of breast cancer, as well as a summary of disability-adjusted life years associated with breast cancer in China from 1990 to 2019, with comparisons to Japan, South Korea, Australia and the United States.

Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study.

Mammography interpretation is challenging with high error rates. This study aims to reduce the errors in mammography reading by mapping diagnostic errors against global mammographic characteristics using a radiomics-based machine learning approach. A total of 36 radiologists from cohort A (n = 20) and cohort B (n = 16) read 60 high-density mammographic cases. Radiomic features were extracted from three regions of interest (ROIs), and random forest models were trained to predict diagnostic errors for each cohort. Performance was evaluated using sensitivity, specificity, accuracy, and AUC. The impact of ROI placement and normalization on prediction was investigated. Our approach successfully predicted both the false positive and false negative errors of both cohorts but did not consistently predict location errors. The errors produced by radiologists from cohort B were less predictable compared to those in cohort A. The performance of the models did not show significant improvement after feature normalization, despite the mammograms being produced by different vendors. Our novel radiomics-based machine learning pipeline focusing on global radiomic features could predict false positive and false negative errors. The proposed method can be used to develop group-tailored mammographic educational strategies to help improve future mammography reader performance.