An International Survey of Quality and Safety Programs in Radiology.

PURPOSE: The aim of this study was to determine the status of radiology quality improvement programs in a variety of selected nations worldwide. METHODS: A survey was developed by select members of the International Economics Committee of the American College of Radiology on quality programs and was distributed to committee members. Members responded on behalf of their country. The 51-question survey asked about 12 different quality initiatives which were grouped into 4 themes: departments, users, equipment, and outcomes. Respondents reported whether a designated type of quality initiative was used in their country and answered subsequent questions further characterizing it. RESULTS: The response rate was 100% and represented Australia, Canada, China, England, France, Germany, India, Israel, Japan, the Netherlands, Russia, and the United States. The most frequently reported quality initiatives were imaging appropriateness (91.7%) and disease registries (91.7%), followed by key performance indicators (83.3%) and morbidity and mortality rounds (83.3%). Peer review, equipment accreditation, radiation dose monitoring, and structured reporting were reported by 75.0% of respondents, followed by 58.3% of respondents for quality audits and critical incident reporting. The least frequently reported initiatives included Lean/Kaizen exercises and physician performance assessments, implemented by 25.0% of respondents. CONCLUSION: There is considerable diversity in the quality programs used throughout the world, despite some influence by national and international organizations, from whom further guidance could increase uniformity and optimize patient care in radiology.

Primary care referral for knee MRI in the United Kingdom: Association with demography and subsequent surgical intervention.

BACKGROUND: Since 2008 primary care physicians (GPs) in our region have been allowed open access to knee MRI scans. There are questions about whether this changes referral practice and if it is an effective use of resources. PURPOSE: To describe the change in demographics of patients referred for knee MRI following implementation of a new referral pathway. STUDY TYPE: Retrospective observational study. POPULATION: All primary care referrals between 2008 and 2015 for knee MRI from a population of 900,000. FIELD STRENGTH/SEQUENCE: Not applicable. ASSESSMENT: Demographic profile and number of knee MRI referrals and subsequent arthroscopies. STATISTICAL TESTS: Comparisons between urban and rural populations used the t-test. Test for normality used Shapiro-Wilks. Comparison between abnormal MRI proportions used a chi-squared test. RESULTS: There were 23,928 knee MRI referrals (10,695 from GPs) between 2000 and 2015. MRI knee referrals rose from 210 in 2008 to 2379 in 2015. The average age of the patient decreased from 46.8 (SD = 14.9) in 2008 to 41.3 (SD = 14.7) in 2015. Conversion to arthroscopy declined from 15.4% to 10.2%, but there was no significant change in abnormal scan proportion. Conversion rates showed no significant difference between rural (9.6%) and urban populations (10.5%). Referral rates were significantly higher in low socioeconomic status areas (47.3% vs. 34.6%). The median referral rate per 1000 patients was 13.8 (interquartile range = 8.4). Referral rates varied widely between practices. DATA CONCLUSION: Despite a large rise in knee MRI referrals from primary care, there has been no substantial change in the age profile, suggesting that there has been no increase in inappropriate referral of elderly patients in whom MRI is unlikely to influence management. A modest decrease in the conversion rate to arthroscopy may be reasonably offset against a decrease in secondary care referrals. Socioeconomic status of the target population must be considered when planning primary care knee MRI services. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 6 J. Magn. Reson. Imaging 2019.

A reporter-targeted approach to reducing the use of the term "superficial femoral vein" in ultrasound reports for deep vein thrombosis.

BACKGROUND: The term "superficial femoral vein" is felt to be potentially misleading and dangerous in the context of deep venous thrombosis (DVT) of the lower limb, with the preferred term now being "femoral vein." The purpose of this work was to identify a means of reducing its use in reports of lower limb sonographic scans for DVT. METHODS: A retrospective search of the reports for all lower limb sonographic scans for the detection of DVT in 2011 was performed using the Radiology Information System to identify the baseline rate of use of the term. Nontargeted and subsequently targeted e-mails were then sent to reporters of such sonographic scans over a 6-month follow-up period, and the results were reevaluated. RESULTS: The baseline rate of use of the term was 17% before any intervention. It decreased to 10% following nontargeted intervention, and to 6% following targeted e-mail intervention. There were no cases of patients coming to harm as a result of the use of the term during the study period. CONCLUSIONS: A simple and inexpensive system of e-mails to reporters resulted in a decline in the rate of use of the misleading term "superficial femoral vein."

The clinical and functional outcomes of ultrasound-guided vs landmark-guided injections for adults with shoulder pathology--a systematic review and meta-analysis.

OBJECTIVE: To compare the clinical and functional outcomes of US-guided (USG) vs landmark-guided (LMG) injection for the treatment of adults with shoulder pathology. METHOD: MEDLINE, AMED and Embase in addition to unpublished literature databases were searched from 1950 to August 2011. Studies were included if they were randomized or non-randomized controlled trials comparing USG vs LSG injections for the treatment of adults with shoulder pathology. Two reviewers independently performed data extraction and appraisal of the studies. Meta-analyses were performed where possible and when inappropriate a narrative review of the data was presented. RESULTS: Six papers including 307 patients were reviewed; 142 received LMG injections and 165 received USG injections. There was a statistically significant difference in favour of USG for pain at 6 weeks (standardized mean difference 1.03; 95% CI 0.12, 1.93; P = 0.03). There was no statistically significant difference between the injection methods with respect to shoulder function (standardized mean difference 0.33; 95% CI -0.59, 1.25; P = 0.48). There was a significant difference between interventions for shoulder abduction at 6 weeks in favour of the USG method (mean difference 2.81; 95% CI 0.67, 4.95; P = 0.01). No other movements showed a statistically significant difference. CONCLUSION: There is a statistically significant difference in pain and abduction between LMG and USG steroid injections for adults with shoulder pathology. However, these differences are small and may not represent clinically useful differences. The current evidence base is limited by a number of important methodological weaknesses, which should be considered when interpreting these findings. The cost-effectiveness of the intervention should be considered in the design of future studies.

International survey on residency programs in radiology: similarities and differences among 17 countries.

OBJECTIVE: With the initiative of the ACR International Economics Committee, a multinational survey was conducted to evaluate radiology residency programs around the world. METHODS: A 31-question survey was developed. It included: economic issues, program size and length, resident's activities during daytime and call, academic aspects including syllabus and examinations. Data was tabulated using the forementioned thematic framework and was qualitatively analyzed. RESULTS: Responses were received from all 17 countries that were invited to participate (France, Netherlands, Israel, UK, Russia, USA, Japan, India, Germany, Canada, Turkey, Croatia, Serbia, Italy, Ireland, Hungary, and Greece). Residency length varied between 2 and 5 years. The certificate of residency completion is provided by a local hospital [4/17 (23%)], University [6/17 (36%)], National Board [6/17 (36%)], and Ministry of Health [1/17 (6%)]. There was variability among the number of residency programs and residents per program ranging from 15 to 300 programs per nation with a 1-700 residents in each one respectively. Salaries varied significantly and ranged from 8000 to 75,000 USD equivalent. Exams are an integral part of training in all surveyed countries. Length of call varied between 5 and 26 h and the number of monthly calls ranged from 3 to 6. The future of radiology was judged as growing in [12/17 (70%)] countries and stagnant in [5/17 (30%)] countries. DISCUSSION: Radiology residency programs worldwide have many similarities. The differences are in the structure of the residency programs. Stagnation and uncertainties need to be addressed to ensure the continued development of the next generation of radiologists. SUMMARY STATEMENT: There are many similarities in the academic aims and approach to education and training of radiology residency programs worldwide. The differences are in the structure of the residency programs and payments to individual residents.

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

The changing NHS and imaging services: impact on musculoskeletal imaging.

OBJECTIVE: This article describes some of the major influences that have improved the way health services in England are delivered over the past 10 years and the impact this has had on imaging services. In particular, we will describe the impact of these changes on the delivery of musculoskeletal imaging services. CONCLUSION: The National Health Service (NHS) is reaping the benefits of these changes, with waiting times at the lowest ever recorded. The changes have been delivered with increased investment as well as service modernization and workforce change.

A statistical approach for breast density segmentation.

Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly.

Breast ultrasound region of interest detection and lesion localisation.

In current breast ultrasound computer aided diagnosis systems, the radiologist preselects a region of interest (ROI) as an input for computerised breast ultrasound image analysis. This task is time consuming and there is inconsistency among human experts. Researchers attempting to automate the process of obtaining the ROIs have been relying on image processing and conventional machine learning methods. We propose the use of a deep learning method for breast ultrasound ROI detection and lesion localisation. We use the most accurate object detection deep learning framework - Faster-RCNN with Inception-ResNet-v2 - as our deep learning network. Due to the lack of datasets, we use transfer learning and propose a new 3-channel artificial RGB method to improve the overall performance. We evaluate and compare the performance of our proposed methods on two datasets (namely, Dataset A and Dataset B), i.e. within individual datasets and composite dataset. We report the lesion detection results with two types of analysis: (1) detected point (centre of the segmented region or the detected bounding box) and (2) Intersection over Union (IoU). Our results demonstrate that the proposed methods achieved comparable results on detected point but with notable improvement on IoU. In addition, our proposed 3-channel artificial RGB method improves the recall of Dataset A. Finally, we outline some future directions for the research.

Tackling climate change close to home: mobile breast screening as a model.

OBJECTIVE: Health services contribute significantly to carbon dioxide (CO(2)) emissions and, while services in the UK are beginning to address this, the focus has been on reducing energy consumption rather than road transport, a major component of emissions. We aimed to compare the distances travelled by patients attending mobile breast screening clinics compared to the distance they would need to travel if screening services were centralized. METHODS: Anonymized postcode records were analysed to determine driving distances potentially saved through attendance at 20 mobile breast screening clinics rather than at two centralized locations. Based on assumptions for the typical car used, the CO(2) emissions were calculated for the current case of decentralized service through mobile clinics compared to a hypothetical case where only centralized services are available over one complete three-year cycle of breast screening invitations. RESULTS: The availability of mobile breast screening clinics for the 60,675 women who underwent screening over a three-year cycle led to a return journey distance savings of 1,429,908 km. Taking into account the CO(2) emissions of the tractor unit used for moving the mobile clinics around, this equates to approximately 75 tonnes of CO(2) saved in any one year. CONCLUSIONS: Decentralizing health care delivery can potentially provide substantial reductions in emissions at the same time as improving the patient experience. Thus, the 'care close to home' agenda can simultaneously improve health outcomes and the environment.

Classification of Microcalcification Clusters in Digital Mammograms Using a Stack Generalization Based Classifier.

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy ( 95.00 ± 0.57 %) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A z value equal to 0.97 ± 0.01 .

Breast ultrasound lesions recognition: end-to-end deep learning approaches.

Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. To assess the performance, we conduct fivefold cross validation using the following split: 70% for training data, 10% for validation data, and 20% testing data. The results showed that our proposed method performed better on benign lesions, with a top "mean Dice" score of 0.7626 with FCN-16s, when compared with the malignant lesions with a top mean Dice score of 0.5484 with FCN-8s. When considering the number of images with Dice score > 0.5 , 89.6% of the benign lesions were successfully segmented and correctly recognised, whereas 60.6% of the malignant lesions were successfully segmented and correctly recognized. We conclude the paper by addressing the future challenges of the work.

Eigendetection of masses considering false positive reduction and breast density information.

The purpose of this article is to present a novel algorithm for the detection of masses in mammographic computer-aided diagnosis systems. Four key points provide the novelty of our approach: (1) the use of eigenanalysis for describing variation in mass shape and size; (2) a Bayesian detection methodology providing a mathematical sound framework, flexible enough to include additional information; (3) the use of a two-dimensional principal components analysis approach to facilitate false positive reduction; and (4) the incorporation of breast density information, a parameter correlated with the performance of most mass detection algorithms and which is not considered in existing approaches. To study the performance of the system two experiments were carried out. The first is related to the ability of the system to detect masses, and thus, free-response receiver operating characteristic analysis was used, showing that the method is able to give high accuracy at a high specificity (80% detection at 1.40 false positives per image). Second, the ability of the system to highlight the pixels belonging to a mass is studied using receiver operating characteristic analysis, resulting in A(z) = 0.89 +/- 0.04. In addition, the robustness of the approach is demonstrated in an experiment where we used the Digital Database for Screening Mammography database for training and the Mammographic Image Analysis Society database for testing the algorithm.

Classification of micro-calcification in mammograms using scalable linear Fisher discriminant analysis.

Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature. Graphical Abstract Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis.

Deep learning in mammography and breast histology, an overview and future trends.

Recent improvements in biomedical image analysis using deep learning based neural networks could be exploited to enhance the performance of Computer Aided Diagnosis (CAD) systems. Considering the importance of breast cancer worldwide and the promising results reported by deep learning based methods in breast imaging, an overview of the recent state-of-the-art deep learning based CAD systems developed for mammography and breast histopathology images is presented. In this study, the relationship between mammography and histopathology phenotypes is described, which takes biological aspects into account. We propose a computer based breast cancer modelling approach: the Mammography-Histology-Phenotype-Linking-Model, which develops a mapping of features/phenotypes between mammographic abnormalities and their histopathological representation. Challenges are discussed along with the potential contribution of such a system to clinical decision making and treatment management.

Provision of out-of-hours services for acute upper gastrointestinal bleeding in England: results of the 2014-2015 BSG/NHS England national survey.

BACKGROUND: There has been a drive to raise the standard of management of acute upper gastrointestinal bleeding (AUGIB) in the UK, including three previous audits, sponsored by the British Society of Gastroenterology (BSG). OBJECTIVE: To review the results of the latest BSG/National Health Service (NHS) England national survey of endoscopy services in England between 2014 and 2015. METHOD: All NHS hospitals accepting acute admissions in England (168) were invited to respond to the survey. RESULTS: Overall, 142 hospitals (84%) returned data. 85% of hospitals used a validated risk assessment score at the time of patient's admission. While 80% of hospitals provided a 24/7 endoscopy service for unstable patients, and another 10% were in network to provide an acute service, only 60% performed an endoscopy within 24 hours for stable acute admissions or inpatients with AUGIB. 11% of hospitals operated an out-of-hours ad hoc rota. 43% felt that pressure from routine work affected their ability to offer a next-day oesophagogastroduodenoscopy service, while 20% of hospitals struggled to recruit endoscopists. 28% of units reported that the previous national audit performed in 2013 had a positive influence on service development. CONCLUSIONS: This survey has revealed significant deficiencies in provision of services for patients with AUGIB in England, without a significant increase in number of hospitals providing an emergency AUGIB service since the last national audit in 2013.

Associations among mammographic density, circulating sex hormones, and polymorphisms in sex hormone metabolism genes in postmenopausal women.

Mammographic density and serum sex hormone levels are important risk factors for breast cancer, but their associations with one another are unclear. We studied these phenotypes, together with single nucleotide polymorphisms (SNP) in genes related to sex hormone metabolism, in a cross-sectional study of 1,413 postmenopausal women from the European Prospective Investigation into Cancer and Nutrition-Norfolk. All women were >1 year postmenopausal and had not taken hormone replacement therapy for >3 months before sampling. Serum levels of 7 sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG), androstenedione, 17-OH-progesterone, estrone, and estrone sulfate] and 15 SNPs in the CYP17, CYP19, EDH17B2, SHBG, COMT, and CYP1B1 genes were studied. Mammograms nearest in time to the blood sampling were identified through the national breast screening program and visually assessed by three radiologists using the Boyd six-category and Wolfe four-category scales. We found a weak positive association between mammographic density and SHBG levels (P = 0.09) but no association with any other hormones. None of the SNPs, including those shown previously to be associated with estradiol or SHBG, showed significant associations with density. We conclude that mammographic density is largely independent of postmenopausal steroid hormone levels, indicating that these risk factors have, to a large extent, an independent etiology and suggesting that they may be independent predictors of breast cancer risk.

How predictive is breast arterial calcification of cardiovascular disease and risk factors when found at screening mammography?

OBJECTIVE: The purpose of this study was to examine the relationship between breast arterial calcification (BAC), commonly found on mammography, and cardiovascular disease and its risk factors. SUBJECTS AND METHODS: The study population, nested within the European Prospective Investigation of Cancer-Norfolk (EPIC-Norfolk) cohort study, consisted of 1,590 women older than 55 years, not taking hormone replacement therapy, and with available screening mammograms. Mammograms were coded by three radiologists for presence or absence of BAC. History of coronary heart disease (CHD), stroke, and diabetes and risk factors for cardiovascular disease (including smoking status, body mass index [BMI], blood pressure, diabetes, and glycosylated hemoglobin [HbA1c]) were independently measured from health examinations in the EPIC study. RESULTS: The prevalence of BAC was 16.0%. Women with BAC were significantly older than those without it. BAC was associated with prevalent CHD, but not stroke. The odds ratio of having CHD was 2.54 (95% confidence interval, 1.03-6.30). The sensitivity and specificity were 32.4% and 85.5%, respectively. Except for smoking, which showed an inverse association, there was no consistent significant association of BAC with cardiovascular disease risk factors including BMI, diabetes, HbA1c, or lipids. CONCLUSION: BAC found on mammograms was associated with prevalent CHD after adjustment for age, but with low sensitivity. BAC may provide additional information toward identifying cardiovascular disease risk among otherwise healthy women.