Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study.

BACKGROUND: Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer. METHODS: The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár's classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs). RESULTS: Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32-4.25 and 3.93; 1.88-8.20), Tabár's PIII and PIV (OR: 3.23; 1.20-8.75 and 4.40; 2.31-8.38), and the highest quartile of the texture score (3.04; 1.63-5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57-0-69), 0.65 (0.59-0-71) and 0.63 (0.57-0-69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone). CONCLUSION: Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies.

Artificial intelligence-powered insights into high-risk, non-obstructive coronary atherosclerosis: a case report.

Background: Advanced coronary plaque analysis by cardiac computed tomography (CT) has recently emerged as a promising technique for better prognostic stratification. However, this evaluation application in clinical practice is still uncertain. Case summary: In the present case, we described the clinical picture of a 44-year-old tennis player with ectopic ventricular beats in which cardiac CT enabled the identification of a non-obstructive but high-risk plaque on proximal left anterior descendent artery. The application of artificial intelligence (AI)-enhanced software enabled to better stratify the patients' risk. The present case describes how early identification of non-obstructive but high-risk coronary plaque evaluated by cardiac CT using AI-enhanced software enabled accurate and personalized risk assessment. Discussion: The main clinical message of this case report is that advanced plaque analysis by cardiac CT, especially when performed with AI-based software, may provide important prognostic information leading to a personalized preventive approach. Moreover, AI-based software may contribute to promote a routine evaluation of these important data already included in traditional cardiac CT.

Prospective deep learning-based quantitative assessment of coronary plaque by computed tomography angiography compared with intravascular ultrasound: the REVEALPLAQUE study.

AIMS: Coronary computed tomography angiography provides non-invasive assessment of coronary stenosis severity and flow impairment. Automated artificial intelligence (AI) analysis may assist in precise quantification and characterization of coronary atherosclerosis, enabling patient-specific risk determination and management strategies. This multicentre international study compared an automated deep learning-based method for segmenting coronary atherosclerosis in coronary computed tomography angiography (CCTA) against the reference standard of intravascular ultrasound (IVUS). METHODS AND RESULTS: The study included clinically stable patients with known coronary artery disease from 15 centres in the USA and Japan. An AI-enabled plaque analysis was utilized to quantify and characterize total plaque (TPV), vessel, lumen, calcified plaque (CP), non-calcified plaque (NCP), and low-attenuation plaque (LAP) volumes derived from CCTA and compared with IVUS measurements in a blinded, core laboratory-adjudicated fashion. In 237 patients, 432 lesions were assessed; mean lesion length was 24.5 mm, and mean IVUS-TPV was 186.0 mm3. AI-enabled plaque analysis on CCTA showed strong correlation and high accuracy when compared with IVUS; correlation coefficient, slope, and Y intercept for TPV were 0.91, 0.99, and 1.87, respectively; for CP volume 0.91, 1.05, and 5.32, respectively; and for NCP volume 0.87, 0.98, and 15.24, respectively. Bland-Altman analysis demonstrated strong agreement with little bias for these measurements. CONCLUSION: AI-enabled CCTA quantification and characterization of atherosclerosis demonstrated strong agreement with IVUS reference standard measurements. This tool may prove effective for accurate evaluation of coronary atherosclerotic burden and cardiovascular risk assessment.

Morphology-based non-rigid registration of coronary computed tomography and intravascular images through virtual catheter path optimization.

Coronary computed tomography angiography (CCTA) provides 3D information on obstructive coronary artery disease, but cannot fully visualize high-resolution features within the vessel wall. Intravascular imaging, in contrast, can spatially resolve atherosclerotic in cross sectional slices, but is limited in capturing 3D relationships between each slice. Co-registering CCTA and intravascular images enables a variety of clinical research applications but is time consuming and user-dependent. This is due to intravascular images suffering from non-rigid distortions arising from irregularities in the imaging catheter path. To address these issues, we present a morphology-based framework for the rigid and non-rigid matching of intravascular images to CCTA images. To do this, we find the optimal virtual catheter path that samples the coronary artery in CCTA image space to recapitulate the coronary artery morphology observed in the intravascular image. We validate our framework on a multi-center cohort of 40 patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our registration approach significantly outperforms other approaches for bifurcation alignment. By providing a differentiable framework for multi-modal vascular co-registration, our framework reduces the manual effort required to conduct large-scale multi-modal clinical studies and enables the development of machine learning-based co-registration approaches.

Atlas-ISTN: Joint segmentation, registration and atlas construction with image-and-spatial transformer networks.

Deep learning models for semantic segmentation are able to learn powerful representations for pixel-wise predictions, but are sensitive to noise at test time and may lead to implausible topologies. Image registration models on the other hand are able to warp known topologies to target images as a means of segmentation, but typically require large amounts of training data, and have not widely been benchmarked against pixel-wise segmentation models. We propose the Atlas Image-and-Spatial Transformer Network (Atlas-ISTN), a framework that jointly learns segmentation and registration on 2D and 3D image data, and constructs a population-derived atlas in the process. Atlas-ISTN learns to segment multiple structures of interest and to register the constructed atlas labelmap to an intermediate pixel-wise segmentation. Additionally, Atlas-ISTN allows for test time refinement of the model's parameters to optimize the alignment of the atlas labelmap to an intermediate pixel-wise segmentation. This process both mitigates for noise in the target image that can result in spurious pixel-wise predictions, as well as improves upon the one-pass prediction of the model. Benefits of the Atlas-ISTN framework are demonstrated qualitatively and quantitatively on 2D synthetic data and 3D cardiac computed tomography and brain magnetic resonance image data, out-performing both segmentation and registration baseline models. Atlas-ISTN also provides inter-subject correspondence of the structures of interest.

CT Angiographic and Plaque Predictors of Functionally Significant Coronary Disease and Outcome Using Machine Learning.

OBJECTIVES: The goal of this study was to investigate the association of stenosis and plaque features with myocardial ischemia and their prognostic implications. BACKGROUND: Various anatomic, functional, and morphological attributes of coronary artery disease (CAD) have been independently explored to define ischemia and prognosis. METHODS: A total of 1,013 vessels with fractional flow reserve (FFR) measurement and available coronary computed tomography angiography were analyzed. Stenosis and plaque features of the target lesion and vessel were evaluated by an independent core laboratory. Relevant features associated with low FFR (≤0.80) were identified by using machine learning, and their predictability of 5-year risk of vessel-oriented composite outcome, including cardiac death, target vessel myocardial infarction, or target vessel revascularization, were evaluated. RESULTS: The mean percent diameter stenosis and invasive FFR were 48.5 ± 17.4% and 0.81 ± 0.14, respectively. Machine learning interrogation identified 6 clusters for low FFR, and the most relevant feature from each cluster was minimum lumen area, percent atheroma volume, fibrofatty and necrotic core volume, plaque volume, proximal left anterior descending coronary artery lesion, and remodeling index (in order of importance). These 6 features showed predictability for low FFR (area under the receiver-operating characteristic curve: 0.797). The risk of 5-year vessel-oriented composite outcome increased with every increment of the number of 6 relevant features, and it had incremental prognostic value over percent diameter stenosis and FFR (area under the receiver-operating characteristic curve: 0.706 vs. 0.611; p = 0.031). CONCLUSIONS: Six functionally relevant features, including minimum lumen area, percent atheroma volume, fibrofatty and necrotic core volume, plaque volume, proximal left anterior descending coronary artery lesion, and remodeling index, help define the presence of myocardial ischemia and provide better prognostication in patients with CAD. (CCTA-FFR Registry for Risk Prediction; NCT04037163).

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors.

In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors, and this is free of discretization artifacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in the state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network-based image segmentation.

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network.

Segmentation of anatomical structures in medical images is often based on a voxel/pixel classification approach. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images that fosters categorization. We propose a novel system for voxel classification integrating three 2D CNNs, which have a one-to-one association with the xy, yz and zx planes of 3D image, respectively. We applied our method to the segmentation of tibial cartilage in low field knee MRI scans and tested it on 114 unseen scans. Although our method uses only 2D features at a single scale, it performs better than a state-of-the-art method using 3D multi-scale features. In the latter approach, the features and the classifier have been carefully adapted to the problem at hand. That we were able to get better results by a deep learning architecture that autonomously learns the features from the images is the main insight of this study.

A Bayesian framework for automated cardiovascular risk scoring on standard lumbar radiographs.

We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.