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 screendetected 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 ¼ 0.134). Our fully automated method shows promising results for cancer risk prediction and is comparable with human performance.
Nailfold capillaroscopy is an established qualitative technique in the assessment of patients displaying Raynaud's phenomenon. We describe a fully automated system for extracting quantitative biomarkers from capillaroscopy images, using a layered machine learning approach. On an unseen set of 455 images, the system detects and locates individual capillaries as well as human experts, and makes measurements of vessel morphology that reveal statistically significant differences between patients with (relatively benign) primary Raynaud's phenomenon, and those with potentially life-threatening systemic sclerosis.
ObjectivesDespite increasing interest in nailfold capillaroscopy, objective measures of capillary structure and blood flow have been little studied. We aimed to test the hypothesis that structural measurements, capillary flow, and a combined measure have the predictive power to separate patients with systemic sclerosis (SSc) from those with primary Raynaud's phenomenon (PRP) and healthy controls (HC).Methods50 patients with SSc, 12 with PRP, and 50 HC were imaged using a novel capillaroscopy system that generates high-quality nailfold images and provides fully-automated measurements of capillary structure and blood flow (capillary density, mean width, maximum width, shape score, derangement and mean flow velocity). Population statistics summarise the differences between the three groups. Areas under ROC curves (AZ) were used to measure classification accuracy when assigning individuals to SSc and HC/PRP groups.ResultsStatistically significant differences in group means were found between patients with SSc and both HC and patients with PRP, for all measurements, e.g. mean width (μm) ± SE: 15.0 ± 0.71, 12.7 ± 0.74 and 11.8 ± 0.23 for SSc, PRP and HC respectively. Combining the five structural measurements gave better classification (AZ = 0.919 ± 0.026) than the best single measurement (mean width, AZ = 0.874 ± 0.043), whilst adding flow further improved classification (AZ = 0.930 ± 0.024).ConclusionsStructural and blood flow measurements are both able to distinguish patients with SSc from those with PRP/HC. Importantly, these hold promise as clinical trial outcome measures for treatments aimed at improving finger blood flow or microvascular remodelling.
We review the exciting potential (and challenges) of quantitative nailfold capillaroscopy, focusing on its role in systemic sclerosis. Quantifying abnormality, including automated analysis of nailfold images, overcomes the subjectivity of qualitative/descriptive image interpretation. First we consider the rationale for quantitative analysis, including the potential for precise discrimination between normal and abnormal capillaries and for reliable measurement of disease progression and treatment response. We discuss nailfold image acquisition and interpretation, and describe how early work on semi-quantitative and quantitative analysis paved the way for semi-automated and automated analysis. Measurement of red blood cell velocity is described briefly. Finally we give a personal view on ‘next steps’. From a clinical perspective, increased uptake of nailfold capillaroscopy by general rheumatologists could be achieved via low-cost hand-held devices with cloud-based automated analysis. From a research perspective, automated analysis could facilitate large-scale prospective studies using capillaroscopic parameters as possible biomarkers of systemic sclerosis-spectrum disorders.
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