Computer-assisted mammographic imaging

Computer-assisted mammography imaging comprises computer-based analysis of digitized images resulting in prompts aiding mammographic interpretation and computerized stereotactic localization devices which improve location accuracy. The commercial prompting systems available are designed to draw attention to mammographic abnormalities detected by algorithms based on symptomatic practise in North America. High sensitivity rates are important commercially but result in increased false prompt rates, which are known to distract radiologists. A national shortage of breast radiologists in the UK necessitates evaluation of such systems in a population breast screening programme to determine effectiveness in increasing cancer detection and feasibility of implementation

CAD in mammography: lesion-level versus case-level analysis of the effects of prompts on human decisions

Object: To understand decision processes in CAD-supported breast screening by analysing how prompts affect readers’ judgements of individual mammographic features (lesions). To this end we analysed hitherto unexamined details of reports completed by mammogram readers in an earlier evaluation of a CAD tool. Material and methods: Assessments of lesions were extracted from 5,839 reports for 59 cancer cases. Statistical analyses of these data focused on what features readers considered when recalling a cancer case and how readers reacted to CAD prompts. Results: About 13.5% of recall decisions were found to be caused by responses to features other than those indicating actual cancer. Effects of CAD: lesions were more likely to be examined if prompted; the presence of a prompt on a cancer increased the probability of both detection and recall especially for less accurate readers in subtler cases; lack of prompts made cancer features less likely to be detected; false prompts made non-cancer features more likely to be classified as cancer. Conclusion: The apparent lack of impact reported for CAD in some studies is plausibly due to CAD systematically affecting readers’ identification of individual features, in a beneficial way for certain combinations of readers and features and a damaging way for others. Mammogram readers do not ignore prompts. Methodologically, assessing CAD by numbers of recalled cancer cases may be misleading

A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Breast Cancer Now. Grant Number: 2015MayPR515National Institute for Health Research. Grant Numbers: IS‐BRC‐1215‐20007, NF‐SI‐0513‐10076Prevent Breast Cancer. Grant Numbers: GA09‐002, GA11‐002Cancer Research UK. Grant Numbers: C1287/A10118, C1287/A16563, C569/A16891National Institutes of Health. Grant Numbers: X01HG007492, U19 CA148065Canadian Institutes of Health Research. Grant Number: GPH‐129344Horizon 2020 Research and Innovation Programme. Grant Numbers: 634935, 633784European Union. Grant Number: HEALTH‐F2‐2009‐22317

Prediction of reader estimates of mammographic density using convolutional neural networks.

Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not yet captured by hand-crafted algorithms. With deep learning, it may be possible to encapsulate this knowledge in an automatic method. We have built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms. The CNNs are trained using whole-image mammograms, each labeled with the average VAS score of two independent readers. Each CNN learns a mapping between mammographic appearance and VAS score so that at test time, they can predict VAS score for an unseen image. Networks were trained using 67,520 mammographic images from 16,968 women and for model selection we used a dataset of 73,128 images. Two case-control sets of contralateral mammograms of screen detected cancers and prior images of women with cancers detected subsequently, matched to controls on age, menopausal status, parity, HRT and BMI, were used for evaluating performance on breast cancer prediction. In the case-control sets, odd ratios of cancer in the highest versus lowest quintile of percentage density were 2.49 (95% CI: 1.59 to 3.96) for screen-detected cancers and 4.16 (2.53 to 6.82) for priors, with matched concordance indices of 0.587 (0.542 to 0.627) and 0.616 (0.578 to 0.655), respectively. There was no significant difference between reader VAS and predicted VAS for the prior test set (likelihood ratio chi square, p = 0.134 ). Our fully automated method shows promising results for cancer risk prediction and is comparable with human performance.This paper presents independent research funded by NIHR under its Programme Grants for Applied Research programme (reference number RP-PG-0707-10031: “Improvement in risk prediction, early detection and prevention of breast cancer”) with additional funding from the Prevent Breast Cancer Appeal and supported by the NIHR Manchester Biomedical Research Centre Award No. IS-BRC-1215-20007

In-depth cardiovascular and pulmonary assessments in children with multisystem inflammatory syndrome after SARS-CoV-2 infection: A case series study

We assessed PET-CT myocardial blood flow (MBF) using N-13 ammonia, brachial flow-mediated dilation, and cardiopulmonary exercise test in five post-discarged MIS-C survivors. None of the patients (median age: 9, range: 7-18 years; 3 females; 2 males) had preexisting pediatric chronic conditions. At the follow-up visit, two patients exhibited severe perfusion defect developed in the left ventricular cavity, suggesting extensive myocardial ischemia (MBF <2.0) and one patient showed persistent mild pericardial effusion. Others two patients demonstrated endothelial dysfunction. Nevertheless, all patients had lower predicted values in the VO2peak, VO2VAT, OUES, and O2 Pulse (range: 35.2%–64.5%; 15.6%–38.2%; 1.0–1.3 L/min; 4–7 ml/beat), respectively. Our d suggested that previously health MIS-C patients had impaired MBF, endothelial dysfunction and lower cardiopulmonary capacity at follow-up analysis. Multidisciplinary further investigations should be conducted to reinforce these findings

Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study

BACKGROUND: Single reading with computer aided detection (CAD) is an alternative to double reading for detecting cancer in screening mammograms. The aim of this study is to investigate whether the use of a single reader with CAD is more cost-effective than double reading. METHODS: Based on data from the CADET II study, the cost-effectiveness of single reading with CAD versus double reading was measured in terms of cost per cancer detected. Cost (Pound (£), year 2007/08) of single reading with CAD versus double reading was estimated assuming a health and social service perspective and a 7 year time horizon. As the equipment cost varies according to the unit size a separate analysis was conducted for high, average and low volume screening units. One-way sensitivity analyses were performed by varying the reading time, equipment and assessment cost, recall rate and reader qualification. RESULTS: CAD is cost increasing for all sizes of screening unit. The introduction of CAD is cost-increasing compared to double reading because the cost of CAD equipment, staff training and the higher assessment cost associated with CAD are greater than the saving in reading costs. The introduction of single reading with CAD, in place of double reading, would produce an additional cost of £227 and £253 per 1,000 women screened in high and average volume units respectively. In low volume screening units, the high cost of purchasing the equipment will results in an additional cost of £590 per 1,000 women screened.One-way sensitivity analysis showed that the factors having the greatest effect on the cost-effectiveness of CAD with single reading compared with double reading were the reading time and the reader's professional qualification (radiologist versus advanced practitioner). CONCLUSIONS: Without improvements in CAD effectiveness (e.g. a decrease in the recall rate) CAD is unlikely to be a cost effective alternative to double reading for mammography screening in UK. This study provides updated estimates of CAD costs in a full-field digital system and assessment cost for women who are re-called after initial screening. However, the model is highly sensitive to various parameters e.g. reading time, reader qualification, and equipment cost

Structural, Metabolic, and Functional Brain Abnormalities as a Result of Prenatal Exposure to Drugs of Abuse: Evidence from Neuroimaging

Prenatal exposure to alcohol and stimulants negatively affects the developing trajectory of the central nervous system in many ways. Recent advances in neuroimaging methods have allowed researchers to study the structural, metabolic, and functional abnormalities resulting from prenatal exposure to drugs of abuse in living human subjects. Here we review the neuroimaging literature of prenatal exposure to alcohol, cocaine, and methamphetamine. Neuroimaging studies of prenatal alcohol exposure have reported differences in the structure and metabolism of many brain systems, including in frontal, parietal, and temporal regions, in the cerebellum and basal ganglia, as well as in the white matter tracts that connect these brain regions. Functional imaging studies have identified significant differences in brain activation related to various cognitive domains as a result of prenatal alcohol exposure. The published literature of prenatal exposure to cocaine and methamphetamine is much smaller, but evidence is beginning to emerge suggesting that exposure to stimulant drugs in utero may be particularly toxic to dopamine-rich basal ganglia regions. Although the interpretation of such findings is somewhat limited by the problem of polysubstance abuse and by the difficulty of obtaining precise exposure histories in retrospective studies, such investigations provide important insights into the effects of drugs of abuse on the structure, function, and metabolism of the developing human brain. These insights may ultimately help clinicians develop better diagnostic tools and devise appropriate therapeutic interventions to improve the condition of children with prenatal exposure to drugs of abuse