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Valid risk stratification for carotid atherosclerotic plaques represents a crucial public health issue toward preventing fatal cerebrovascular events. Although motion analysis (MA) provides useful information about arterial wall dynamics, the identification of motion-based risk markers remains a significant challenge. Considering that the ability of a motion estimator (ME) to handle changes in the appearance of motion targets has a major effect on accuracy in MA, we investigated the potential of adaptive block matching (ABM) MEs, which consider changes in image intensities over time. To assure the validity in MA, we optimized and evaluated the ABM MEs in the context of a specially designed in silico framework. ABMFIRF2, which takes advantage of the periodicity characterizing the arterial wall motion, was the most effective ABM algorithm, yielding a 47% accuracy increase with respect to the conventional block matching. The in vivo application of ABMFIRF2 revealed five potential risk markers: low movement amplitude of the normal part of the wall adjacent to the plaques in the radial (RMAPWL) and longitudinal (LMAPWL) directions, high radial motion amplitude of the plaque top surface (RMAPTS), and high relative movement, expressed in terms of radial strain (RSIPL) and longitudinal shear strain (LSSIPL), between plaque top and bottom surfaces. The in vivo results were reproduced by OFLK(WLS) and ABMKF-K2, MEs previously proposed by the authors and with remarkable in silico performances, thereby reinforcing the clinical values of the markers and the potential of those MEs. Future in vivo studies will elucidate with confidence the full potential of the markers. [ABSTRACT FROM AUTHOR]

Purpose: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints., Methods: We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using previously validated software that uses 3D reconstructed slices and raw 2D DBT data. We developed a deep learning (DL) model that segments dense and fatty breast tissue from background. We then applied this model to estimate %VBD and absolute dense volume (ADV) in cm 3 in a separate case-control sample (180 cases and 654 controls). We created two conditional logistic regression models, relating each model-derived density measurement to likelihood of contralateral breast cancer diagnosis, adjusted for age, BMI, family history, and menopausal status., Results: The DL model achieved unweighted and weighted Dice scores of 0.88 (standard deviation [SD] = 0.08) and 0.76 (SD = 0.15), respectively, on the held-out test set, demonstrating good agreement between the model and 3D reference segmentations. There was a significant association between the odds of breast cancer diagnosis and model-derived VBD (odds ratio [OR], 1.41 [95 % CI, 1.13 to 1.77]; P = .002), with an AUC of 0.65 (95% CI, 0.60 to 0.69). ADV was also significantly associated with breast cancer diagnosis (OR, 1.45 [95% CI, 1.22 to 1.73]; P < .001) with an AUC of 0.67 (95% CI, 0.62 to 0.71)., Conclusion: DL-derived density measures derived from 3D reconstructed DBT images are associated with breast cancer diagnosis.

Artificial intelligence (AI) applications for screening mammography are being marketed for clinical use in the interpretative domains of lesion detection and diagnosis, triage, and breast density assessment and in the noninterpretive domains of breast cancer risk assessment, image quality control, image acquisition, and dose reduction. Evidence in support of these nascent applications, particularly for lesion detection and diagnosis, is largely based on multireader studies with cancer-enriched datasets rather than rigorous clinical evaluation aligned with the application's specific intended clinical use. This article reviews commercial AI algorithms for screening mammography that are currently available for clinical practice, their use, and evidence supporting their performance. Clinical implementation considerations, such as workflow integration, governance, and ethical issues, are also described. In addition, the future of AI for screening mammography is discussed, including the development of interpretive and noninterpretive AI applications and strategic priorities for research and development.

Purpose: Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making., Methods: We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers., Results: Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes., Conclusions: Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns-a woman's left and right breasts. From 341 features, we identified "robust" features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS ® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross-validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.

Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests Dr. Emily Conant reports research grants and membership on the Scientific Advisory Boards of Hologic, Inc., and iCAD, Inc. The other nine authors have no conflict of interests., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant ( p < 0.001) overlap difference for spleen (Dice IML /Dice Manual = 0.91/0.87), breast tumors (Dice IML /Dice Manual = 0.84/0.82), and lung nodules (Dice IML /Dice Manual = 0.78/0.83). For intra-rater consistency, a significant ( p = 0.003) difference was found for spleen (Dice IML /Dice Manual = 0.91/0.89). For inter-rater consistency, significant ( p < 0.045) differences were found for spleen (Dice IML /Dice Manual = 0.91/0.87), breast (Dice IML /Dice Manual = 0.86/0.81), lung (Dice IML /Dice Manual = 0.85/0.89), the non-enhancing (Dice IML /Dice Manual = 0.79/0.67) and the enhancing (Dice IML /Dice Manual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

Background: Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 ( IDH1 and IDH2 ), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm., Methods: We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH -mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH , 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70-30% training-testing splits or 10-fold cross-validation within individual collections, (2) 70-30% training-testing splits within merged collections, and (3) training on one collection and testing on another., Results: These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH , and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset., Conclusions: Using machine learning algorithms, high accuracy was achieved in the prediction of IDH , 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses., (© The Author(s) 2021. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Motion extracted from the carotid artery wall provides unique information for vascular health evaluation. Carotid artery longitudinal wall motion corresponds to the multiphasic arterial wall excursion in the direction parallel to blood flow during the cardiac cycle. While this motion phenomenon has been well characterized, there is a general lack of awareness regarding its implications for vascular health assessment or even basic vascular physiology. In the last decade, novel estimation strategies and clinical investigations have greatly advanced our understanding of the bi-axial behavior of the carotid artery, necessitating an up-to-date review to summarize and classify the published literature in collaboration with technical and clinical experts in the field. Within this review, the state-of-the-art methodologies for carotid wall motion estimation are described, and the observed relationships between longitudinal motion-derived indices and vascular health are reported. The vast number of studies describing the longitudinal motion pattern in plaque-free arteries, with its putative application to cardiovascular disease prediction, point to the need for characterizing the added value and applicability of longitudinal motion beyond established biomarkers. To this aim, the main purpose of this review was to provide a strong base of theoretical knowledge, together with a curated set of practical guidelines and recommendations for longitudinal motion estimation in patients, to foster future discoveries in the field, toward the integration of longitudinal motion in basic science as well as clinical practice., (Copyright © 2020 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Convolutional neural networks (CNNs) have recently been popular for classification and segmentation through numerous network architectures offering a substantial performance improvement. Their value has been particularly appreciated in the domain of biomedical applications, where even a small improvement in the predicted segmented region (e.g., a malignancy) compared to the ground truth can potentially lead to better diagnosis or treatment planning. Here, we introduce a novel architecture, namely the Overall Convolutional Network (O-Net), which takes advantage of different pooling levels and convolutional layers to extract more deeper local and containing global context. Our quantitative results on 2D images from two distinct datasets show that O-Net can achieve a higher dice coefficient when compared to either a U-Net or a Pyramid Scene Parsing Net. We also look into the stability of results for training and validation sets which can show the robustness of model compared with new datasets. In addition to comparison to the decoder, we use different encoders including simple, VGG Net, and ResNet. The ResNet encoder could help to improve the results in most of the cases.

Asynchronous movement of the carotid atheromatous plaque from B-mode ultrasound has been previously reported, and associated with higher risk of stroke, but not quantitatively estimated. Based on the hypothesis that asynchronous plaque motion is associated with vulnerable plaque, in this study, synchronisation patterns of different tissue areas were estimated using cross-correlations of displacement waveforms. In 135 plaques (77 subjects), plaque radial deformation was synchronised by approximately 50% with the arterial diameter, and the mean phase shift was 0.4 s. Within the plaque, the mean phase shifts between the displacements of the top and bottom surfaces were 0.2 s and 0.3 s, in the radial and longitudinal directions, respectively, and the synchronisation about 80% in both directions. Classification of phase-shift-based features using Random Forests yielded Area-Under-the-Curve scores of 0.81, 0.79, 0.89 and 0.90 for echogenicity, symptomaticity, stenosis degree and plaque risk, respectively. Statistical analysis showed that echolucent, high-stenosis and high-risk plaques exhibited higher phase shifts between the radial displacements of their top and bottom surfaces. These findings are useful in the study of plaque kinematics.

Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients ( r ), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD ( r = 0.77-0.84) and Volpara VPD ( r = 0.85-0.90) ( P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography-computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features., (© 2020 The Authors. Published by Grapho Publications, LLC.)

In this paper, radiomic features are used to validate the textural realism of two anthropomorphic phantoms for digital mammography. One phantom was based off a computational breast model; it was 3D printed by CIRS (Computerized Imaging Reference Systems, Inc., Norfolk, VA) under license from the University of Pennsylvania. We investigate how the textural realism of this phantom compares against a phantom derived from an actual patient's mammogram ("Rachel", Gammex 169, Madison, WI). Images of each phantom were acquired at three kV in 1 kV increments using auto-time technique settings. Acquisitions at each technique setting were repeated twice, resulting in six images per phantom. In the raw ("FOR PROCESSING") images, 341 features were calculated; i.e. , gray-level histogram, co-occurrence, run length, fractal dimension, Gabor Wavelet, local binary pattern, Laws, and co-occurrence Laws features. Features were also calculated in a negative screening population. For each feature, the middle 95% of the clinical distribution was used to evaluate the textural realism of each phantom. A feature was considered realistic if all six measurements in the phantom were within the middle 95% of the clinical distribution. Otherwise, a feature was considered unrealistic. More features were actually found to be realistic by this definition in the CIRS phantom (305 out of 341 features or 89.44%) than in the phantom derived from a specific patient's mammogram (261 out of 341 features or 76.54%). We conclude that the texture is realistic overall in both phantoms.

Purpose: Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence., Experimental Design: Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility., Results: Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort ( P < 0.01). Kaplan-Meier curves showed statistically significant differences ( P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value ( c = 0.73) predicting RFS., Conclusions: Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment., (©2019 American Association for Cancer Research.)

Studies have shown that combining calculations of radiomic features with estimates of mammographic density results in an even better assessment of breast cancer risk than density alone. However, to ensure that risk assessment calculations are consistent across different imaging acquisition settings, it is important to identify features that are not overly sensitive to changes in these settings. In this study, digital mammography (DM) images of an anthropomorphic phantom ("Rachel", Gammex 169, Madison, WI) were acquired at various technique settings. We varied kV and mAs, which control contrast and noise, respectively. DM images in women with negative screening exams were also analyzed. Radiomic features were calculated in the raw ("FOR PROCESSING") DM images; i.e., grey-level histogram, co-occurrence, run length, fractal dimension, Gabor Wavelet, local binary pattern, Laws, and co-occurrence Laws features. For each feature, the range of variation across technique settings in phantom images was calculated. This range was scaled against the range of variation in the clinical distribution (specifically, the range corresponding to the middle 90% of the distribution). In order for a radiomic feature to be considered robust, this metric of imaging acquisition variation (IAV) should be as small as possible (approaching zero). An IAV threshold of 0.25 was proposed for the purpose of this study. Out of 341 features, 284 features (83%) met the threshold IAV ≤ 0.25. In conclusion, we have developed a method to identify robust radiomic features in DM.

The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Ca ncer imaging P henomics T ool k it (CaPTk www.cbica.upenn.edu/captk), a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. The target audience of CaPTk consists of both computational scientists and clinical experts. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. CaPTk's long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development.

We retrospectively analyzed negative screening digital mammograms from 115 women who developed unilateral breast cancer at least one year later and 460 matched controls. Texture features were estimated in multiple breast regions defined by an anatomically-oriented polar grid, and were weighted by their position and underlying dense versus fatty tissue composition. Elastic net regression with cross-validation was performed and area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate ability to predict breast cancer. We also compared our anatomy-augmented features to current state-of-the-art in which parenchymal texture was assessed without considering breast anatomy and evaluated the added value of the extracted features to breast density, body-mass-index (BMI) and age as baseline predictors. Our anatomy-augmented texture features resulted in higher discriminatory capacity (AUC = 0.63 vs. AUC = 0.59) when breast anatomy was not considered (p = 0.021), with dense tissue regions and the central breast quadrant being more heavily weighted. Texture also improved baseline models (from AUC = 0.62 to AUC = 0.67, p = 0.029). Our findings suggest that incorporating breast anatomy information could augment imaging markers of breast cancer risk with the potential to improve personalized breast cancer risk assessment.

Rationale and Objectives: We evaluate utilizing convolutional neural networks (CNNs) to optimally fuse parenchymal complexity measurements generated by texture analysis into discriminative meta-features relevant for breast cancer risk prediction., Materials and Methods: With Institutional Review Board approval and Health Insurance Portability and Accountability Act compliance, we retrospectively analyzed "For Processing" contralateral digital mammograms (GE Healthcare 2000D/DS) from 106 women with unilateral invasive breast cancer and 318 age-matched controls. We coupled established texture features (histogram, co-occurrence, run-length, structural), extracted using a previously validated lattice-based strategy, with a multichannel CNN into a hybrid framework in which a multitude of texture feature maps are reduced to meta-features predicting the case or control status. We evaluated the framework in a randomized split-sample setting, using the area under the curve (AUC) of the receiver operating characteristic (ROC) to assess case-control discriminatory capacity. We also compared the framework to CNNs directly fed with mammographic images, as well as to conventional texture analysis, where texture feature maps are summarized via simple statistical measures that are then used as inputs to a logistic regression model., Results: Strong case-control discriminatory capacity was demonstrated on the basis of the meta-features generated by the hybrid framework (AUC = 0.90), outperforming both CNNs applied directly to raw image data (AUC = 0.63, P <.05) and conventional texture analysis (AUC = 0.79, P <.05)., Conclusions: Our results suggest that informative interactions between patterns exist in texture feature maps derived from mammographic images, which can be extracted and summarized via a multichannel CNN architecture toward leveraging the associations of textural measurements to breast cancer risk., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risks held by CA for both patient safety and state economies. This paper investigated the unexplored potential of kinematic features in assisting the diagnostic decision for CA in the framework of a computer-aided diagnosis (CAD) tool. To this end, 15 CAD schemes were designed and were fed with a wide variety of kinematic features of the atherosclerotic plaque and the arterial wall adjacent to the plaque for 56 patients from two different hospitals. The CAD schemes were benchmarked in terms of their ability to discriminate between symptomatic and asymptomatic patients and the combination of the Fisher discriminant ratio, as a feature-selection strategy, and support vector machines, in the classification module, was revealed as the optimal motion-based CAD tool. The particular CAD tool was evaluated with several cross-validation strategies and yielded higher than 88% classification accuracy; the texture-based CAD performance in the same dataset was 80%. The incorporation of kinematic features of the arterial wall in CAD seems to have a particularly favorable impact on the performance of image-data-driven diagnosis for CA, which remains to be further elucidated in future prospective studies on large datasets.

Objectives: Galectin-3, a member of galectines, a family of b-galactoside-specific lectins, has been reported to propagate vascular inflammation. The role of galectin-3 in carotid atherosclerosis is controversial. The aim of this study was to investigate the relationship of galectin-3 with plaque vulnerability in patients with high grade carotid stenosis., Methods: This was a cross sectional study of patients undergoing carotid endarterectomy (CEA). Carotid plaques obtained from 78 consecutive patients (40 symptomatic [SG], 38 asymptomatic [AG]) undergoing CEA were histologically analyzed for galectin-3, macrophages (CD68) and laminin. Pre-operatively the biochemical profile and plaque echogenicity (gray-scale median, GSM) score were determined., Results: There were no significant differences in clinical and demographic parameters between SG and AG(p > .05). The SG had a lower GSM score (44.21 ± 18.24 vs. 68.79 ± 28.79, p < .001) and a smaller positive stained area for galectin-3 (4.89 ± 1.60% vs. 12.01 ± 5.91%, p < .001) and laminin (0.88 ± 0.71% vs. 3.46 ± 2.12%, p < .001) than the AG. On the other hand, intra-plaque macrophage content was increased in SG (p < .001). For the whole cohort, symptomatic status was independently associated with intra-plaque contents of both galectin-3 (OR=0.634, p < .001), and GSM score (OR=0.750, p < .001). Notably, patients on long term statin treatment had elevated galectin-3 and lowered macrophage intra-plaque concentrations compared with those on short term treatment (p < .05)., Conclusions: A low galectin-3 intra-plaque concentration seems to correlate with clinically and ultrasonically defined unstable human carotid plaques. Long term statin treatment may induce increase of intra-plaque galectin-3 concentration mediating plaque stabilization.

Valid characterization of carotid atherosclerosis (CA) is a crucial public health issue, which would limit the major risk held by CA for both patient safety and state economies. CA is typically diagnosed and assessed using duplex ultrasonography (US). Elastrography Imaging (EI) is a promising US technique for quantifying tissue elasticity (ES). In this work, we investigated the association between ES of carotid atherosclerotic lesions, derived from EI, and texture indices, calculated from US image analysis. US and EI images of 23 atherosclerotic plaques (16 patients) were analyzed. Texture features derived from US image analysis (Gray-Scale Median (GSM), plaque area (A) and co-occurrence-matrixderived features) were calculated. Statistical analysis revealed associations between US texture features and EI measured indices. This result indicates accordance in US and EI techniques and states the promising role of EI in diagnosis of CA.

Objective: We investigated the relationship of circulating novel adipokines, retinol-binding protein 4 (RBP4) and omentin-1, with advanced carotid atherosclerosis and ultrasound indexes of severity (total plaque area-TPA) and plaque echogenicity and vulnerability (Gray-Scale median - GSM score)., Methods: We enrolled 225 patients with high-grade carotid stenosis (HGCS) who underwent carotid revascularization (73 Symptomatic patients, 152 asymptomatic patients) and 75 age- and sex-matched, asymptomatic individuals with low-grade (<50%) carotid stenosis (LGCS). Seventy-three individuals without current manifestations of atherosclerotic disease served as control group (COG). All participants underwent carotid ultrasound with TPA and GSM score assessment. Moreover, clinical parameters, metabolic profile, and circulating levels of hsCRP and adipokines were assessed., Results: RBP4 was significantly elevated in HGCS (51.44 ± 16.23 mg/L) compared to LGCS (38.39 ± 8.85 mg/L), independent of symptoms existence, whereas RBP4 levels in COG were even lower (25.74 ± 10.72 mg/L, p < 0.001 compared to either HGCS or LGCS). Inversely, serum omentin-1 levels were significantly lower across HGCS (490.41 ± 172 ng/ml) and LGCS (603.20 ± 202.43 ng/ml) than COG (815.3 ± 185.32, p < 0.001). Moreover, the considerable difference between HGCS and LGCS (p < 0.001) was exclusively attributed to the excessive suppression of omentin-1 concentrations in symptomatic versus asymptomatic (p = 0.004) patients. HGCS and LGCS did not differ in the rest of clinical and biochemical parameters. In multiple regression analysis, RBP4 (beta = 0.232, p = 0.025) and hsCRP (beta = 0.300, p = 0.004) emerged as independent determinants of TPA in patients with carotid atherosclerosis. Low serum levels of omentin-1 correlated with GSM score and symptoms but that association was lost in multivariate analysis.., Conclusion: RBP4 serum levels were significantly elevated in patients with established carotid atherosclerosis and were positively associated with atherosclerosis severity. The association of low serum omentin-1 with carotid plaque echolucency requires further investigation.. ClinicalTrials.gov Identifier: NCT00636766., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Carotid atherosclerosis is the main cause of fatal cerebral ischemic events, thereby posing a major burden for public health and state economies. We propose a web-based platform named CAROTID to address the need for optimal management of patients with carotid atherosclerosis in a twofold sense: (a) objective selection of patients who need carotid-revascularization (i.e., high-risk patients), using a multifaceted description of the disease consisting of ultrasound imaging, biochemical and clinical markers, and (b) effective storage and retrieval of patient data to facilitate frequent follow-ups and direct comparisons with related cases. These two services are achieved by two interconnected modules, namely the computer-aided diagnosis (CAD) tool and the intelligent archival system, in a unified, remotely accessible system. We present the design of the platform and we describe three main usage scenarios to demonstrate the CAROTID utilization in clinical practice. Additionally, the platform was evaluated in a real clinical environment in terms of CAD performance, end-user satisfaction and time spent on different functionalities. CAROTID classification of high- and low-risk cases was 87%; the corresponding stenosis-degree-based classification would have been 61%. Questionnaire-based user satisfaction showed encouraging results in terms of ease-of-use, clinical usefulness and patient data protection. Times for different CAROTID functionalities were generally short; as an example, the time spent for generating the diagnostic decision was 5min in case of 4-s ultrasound video. Large datasets and future evaluation sessions in multiple medical institutions are still necessary to reveal with confidence the full potential of the platform., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

This paper investigated the performance of affine optical flow (AFOF) in motion tracking of the arterial wall from B-mode ultrasound images and the effect of its combination with multiscale image analysis on the accuracy of the process. Multiscale AFOF (MAFOF) exploits the information obtained with AFOF from the approximation sub-images at different spatial resolution levels of the images, obtained using a 2D discrete wavelet transform. Both AFOF and MAFOF were evaluated through their application to synthetic image sequences of the common carotid artery. Multiscale image analysis increased the accuracy in motion tracking, with MAFOF yielding average displacement error reductions of 9% with respect to AFOF. The methods were also effectively applied to real ultrasound image sequences of the carotid artery. The results showed that MAFOF could be considered as a reliable estimator for arterial wall motion from B-mode ultrasound images.