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.

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.

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.

Breast image pre-processing for mammographic tissue segmentation.

During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.

A Review on Automatic Mammographic Density and Parenchymal Segmentation.

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

Topological modeling and classification of mammographic microcalcification clusters.

GOAL: The presence of microcalcification clusters is a primary sign of breast cancer; however, it is difficult and time consuming for radiologists to classify microcalcifications as malignant or benign. In this paper, a novel method for the classification of microcalcification clusters in mammograms is proposed. METHODS: The topology/connectivity of individual microcalcifications is analyzed within a cluster using multiscale morphology. This is distinct from existing approaches that tend to concentrate on the morphology of individual microcalcifications and/or global (statistical) cluster features. A set of microcalcification graphs are generated to represent the topological structure of microcalcification clusters at different scales. Subsequently, graph theoretical features are extracted, which constitute the topological feature space for modeling and classifying microcalcification clusters. k-nearest-neighbors-based classifiers are employed for classifying microcalcification clusters. RESULTS: The validity of the proposed method is evaluated using two well-known digitized datasets (MIAS and DDSM) and a full-field digital dataset. High classification accuracies (up to 96%) and good ROC results (area under the ROC curve up to 0.96) are achieved. A full comparison with related publications is provided, which includes a direct comparison. CONCLUSION: The results indicate that the proposed approach is able to outperform the current state-of-the-art methods. Significance: This study shows that topology modeling is an important tool for microcalcification analysis not only because of the improved classification accuracy but also because the topological measures can be linked to clinical understanding.

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.

A review of automatic mass detection and segmentation in mammographic images.

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

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.

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.