Occurrence and Positive Predictive Value of Additional Nonmass Findings for Risk Stratification of Breast Microcalcifications in Mammography

AbstractPurposeTo assess the occurrence and positive predictive value of additional nonmass findings to stratify the risk of breast microcalcifications.MethodsThis retrospective evaluation included 278 lesions with vacuum- or image-guided hook-wire biopsy for suspicious microcalcifications. The lesions were categorized into exclusive microcalcifications and microcalcifications with focal asymmetry, tubular density or architectural distortion (ie, nonmass findings). To evaluate the utility of additional nonmass findings for risk stratification, outcome variables were positive predictive values and odds ratios for malignancy and invasive carcinoma.ResultsForty-five of 278 microcalcification lesions (16%) were associated with nonmass findings: 28 focal asymmetries, 2 tubular densities, and 15 focal asymmetries in conjunction with tubular densities. Architectural distortion was observed in 28 of these cases. The odds ratio for additional nonmass findings relative to exclusive microcalcifications was 5.9 and was statistically significant (P < .00001). Architectural distortion was the most specific indicator for malignancy and invasiveness, with odds ratios of 6.5 (P = .0072) and 5.6 (P = .0214), respectively.ConclusionsMicrocalcifications with nonmass findings were less frequent than exclusive microcalcifications but were more predictive for malignancy. Architectural distortion demonstrated the highest risk of malignancy and invasiveness. Assessment of additional nonmass findings might be useful for further risk stratification of microcalcifications, indications for additional imaging, and pretreatment considerations

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Engineered antibodies: new possibilities for brain PET?

International audienceAlmost 50 million people worldwide are affected by Alzheimer's disease (AD), the most common neurodegenerative disorder. Development of disease-modifying therapies would benefit from reliable, non-invasive positron emission tomography (PET) biomarkers for early diagnosis, monitoring of disease progression, and assessment of therapeutic effects. Traditionally, PET ligands have been based on small molecules that, with the right properties, can penetrate the blood-brain barrier (BBB) and visualize targets in the brain. Recently a new class of PET ligands based on antibodies have emerged, mainly in applications related to cancer. While antibodies have advantages such as high specificity and affinity, their passage across the BBB is limited. Thus, to be used as brain PET ligands, antibodies need to be modified for active transport into the brain. Here, we review the development of radioligands based on antibodies for visualization of intrabrain targets. We focus on antibodies modified into a bispecific format, with the capacity to undergo transferrin receptor 1 (TfR1)-mediated transcytosis to enter the brain and access pathological proteins, e.g. amyloid-beta. A number of such antibody ligands have been developed, displaying differences in brain uptake, pharmacokinetics, and ability to bind and visualize the target in the brain of transgenic mice. Potential pathological changes related to neurodegeneration, e.g. misfolded proteins and neuroinflammation, are suggested as future targets for this novel type of radioligand. Challenges are also discussed, such as the temporal match of radionuclide half-life with the ligand's pharmacokinetic profile and translation to human use. In conclusion, brain PET imaging using bispecific antibodies, modified for receptor-mediated transcytosis across the BBB, is a promising method for specifically visualizing molecules in the brain that are difficult to target with traditional small molecule ligands

Inferring Ecological Processes from Taxonomic, Phylogenetic and Functional Trait β-Diversity

Understanding the influences of dispersal limitation and environmental filtering on the structure of ecological communities is a major challenge in ecology. Insight may be gained by combining phylogenetic, functional and taxonomic data to characterize spatial turnover in community structure (β-diversity). We develop a framework that allows rigorous inference of the strengths of dispersal limitation and environmental filtering by combining these three types of β-diversity. Our framework provides model-generated expectations for patterns of taxonomic, phylogenetic and functional β-diversity across biologically relevant combinations of dispersal limitation and environmental filtering. After developing the framework we compared the model-generated expectations to the commonly used “intuitive” expectation that the variance explained by the environment or by space will, respectively, increase monotonically with the strength of environmental filtering or dispersal limitation. The model-generated expectations strongly departed from these intuitive expectations: the variance explained by the environment or by space was often a unimodal function of the strength of environmental filtering or dispersal limitation, respectively. Therefore, although it is commonly done in the literature, one cannot assume that the strength of an underlying process is a monotonic function of explained variance. To infer the strength of underlying processes, one must instead compare explained variances to model-generated expectations. Our framework provides these expectations. We show that by combining the three types of β-diversity with model-generated expectations our framework is able to provide rigorous inferences of the relative and absolute strengths of dispersal limitation and environmental filtering. Phylogenetic, functional and taxonomic β-diversity can therefore be used simultaneously to infer processes by comparing their empirical patterns to the expectations generated by frameworks similar to the one developed here

Global Island Monitoring Scheme (GIMS) : a proposal for the long-term coordinated survey and monitoring of native island forest biota

Islands harbour evolutionary and ecologically unique biota, which are currently disproportionately threatened by a multitude of anthropogenic factors, including habitat loss, invasive species and climate change. Native forests on oceanic islands are important refugia for endemic species, many of which are rare and highly threatened. Long-term monitoring schemes for those biota and ecosystems are urgently needed: (i) to provide quantitative baselines for detecting changes within island ecosystems, (ii) to evaluate the effectiveness of conservation and management actions, and (iii) to identify general ecological patterns and processes using multiple island systems as repeated 'natural experiments'. In this contribution, we call for a Global Island Monitoring Scheme (GIMS) for monitoring the remaining native island forests, using bryophytes, vascular plants, selected groups of arthropods and vertebrates as model taxa. As a basis for the GIMS, we also present new, optimized monitoring protocols for bryophytes and arthropods that were developed based on former standardized inventory protocols. Effective inventorying and monitoring of native island forests will require: (i) permanent plots covering diverse ecological gradients (e.g. elevation, age of terrain, anthropogenic disturbance); (ii) a multiple-taxa approach that is based on standardized and replicable protocols; (iii) a common set of indicator taxa and community properties that are indicative of native island forests' welfare, building on, and harmonized with existing sampling and monitoring efforts; (iv) capacity building and training of local researchers, collaboration and continuous dialogue with local stakeholders; and (v) long-term commitment by funding agencies to maintain a global network of native island forest monitoring plots.Peer reviewe

User Experiences of Development of Dependence on the Synthetic Cannabinoids, 5f-AKB48 and 5F-PB-22, and Subsequent Withdrawal Syndromes

Emergence of synthetic cannabinoids (SCBs) in herbal smoking mixtures is a public health concern. New SCB’s such as 5f-AKB48 and 5F-PB-22 have been detected in French seizures and in sudden death post mortems in the US. The aim was to describe development of dependence on herbal smoking mixtures containing the SCB’s, 5f-AKB48 and 5F-PB-22 and subsequent withdrawal syndromes. Dependent users of herbal smoking mixtures known to contain the SCB’s 5f-AKB48 and 5F-PB-22 with an average Severity of Dependence Score (SDS) of 13 were interviewed using a structured guide (three males/three females). Narratives were analysed using the Empirical Phenomenological Psychological (EPP) five step method. Six themes with 68 categories emerged from the analysis. Themes are illustrated as 1) Networks and Product Availability; 2) Drivers and Motives for Use; 3) Effect and Pathways toward Dependence; 4) Poly Substance Use and Comparisons to Natural Cannabis; 5) Dependence and Withdrawal and 6) Self-detoxification Attempts. Two higher levels of abstraction above these theme-levels emerged from the data, with sole use of herbal smoking mixtures containing 5f-AKB48 and 5F-PB-22 centering on the interplay between intense cravings, compulsive all-consuming seeking, use and re-dose behaviours, and fear of the psychiatric and self-harms caused when in withdrawal. This is the first study describing dependence and withdrawal experiences in users dependent on 5f-AKB48 and 5F-PB-22. Given the potential for adverse psychiatric and physical consequences of dependent use, further development of specific clinical responses and clinical research around toxicity and withdrawal severity are warranted

Carotid Plaque Imaging with SPECT/CT and PET/CT

A major contributor to the occurrence of ischemic stroke is the existence of carotid atherosclerosis. A vulnerable carotid atherosclerotic plaque may rupture or erode, thus causing a thrombotic event. Currently, clinical decision-making with regard to carotid endarterectomy or stenting is still primarily based on the extent of luminal stenosis, estimated with CT angiography and/or (duplex) ultrasonography. However, there is growing evidence that the anatomic impact of stenosis alone has limited value in predicting the exact consequences of plaque vulnerability. Various molecular processes have, independently of degree of stenosis, shown to be importantly associated with the plaque's capability to cause thrombotic events. These molecular processes can be visualized with nuclear medicine techniques allowing the identification of vulnerable patients by non-invasive in vivo SPECT(/CT) and PET(/CT) imaging. This chapter provides an overview of SPECT(/CT) and PET(/CT) imaging with specific radiotracers that have been evaluated for the detection of plaques together with a future perspective in this field of imaging.</p

Ulnar-sided wrist pain. II. Clinical imaging and treatment

Pain at the ulnar aspect of the wrist is a diagnostic challenge for hand surgeons and radiologists due to the small and complex anatomical structures involved. In this article, imaging modalities including radiography, arthrography, ultrasound (US), computed tomography (CT), CT arthrography, magnetic resonance (MR) imaging, and MR arthrography are compared with regard to differential diagnosis. Clinical imaging findings are reviewed for a more comprehensive understanding of this disorder. Treatments for the common diseases that cause the ulnar-sided wrist pain including extensor carpi ulnaris (ECU) tendonitis, flexor carpi ulnaris (FCU) tendonitis, pisotriquetral arthritis, triangular fibrocartilage complex (TFCC) lesions, ulnar impaction, lunotriquetral (LT) instability, and distal radioulnar joint (DRUJ) instability are reviewed

Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission

Purpose Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer’s disease, Parkinson’s disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). Methods FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. Results Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). Conclusion Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset