Safely reducing unnecessary benign breast biopsies by applying non-mass and DWI directional variance filters to ADC thresholding.

BACKGROUND: Thresholding apparent diffusion coefficient (ADC) maps obtained from Diffusion-Weighted-Imaging (DWI) has been proposed for identifying benign lesions that can safely avoid biopsy. The presence of malignancies with high ADC values leads to high thresholds, limiting numbers of avoidable biopsies. PURPOSE: We evaluate two previously reported methods for identifying avoidable biopsies: using case-set dependent ADC thresholds that assure 100% sensitivity and using negative likelihood ratio (LR-) with a fixed ADC threshold of 1.50 × 10-3 mm2/s. We evaluated improvements in efficacy obtained by excluding non-mass lesions and lesions with anisotropic intra-lesion morphologic characteristics. STUDY TYPE: Prospective. POPULATION: 55 adult females with dense breasts with 69 BI-RADS 4 or 5 lesions (38 malignant, 31 benign) identified on ultrasound and mammography and imaged with MRI prior to biopsy. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T. DWI. ASSESSMENT: Analysis of DWI, including directional images was done on an ROI basis. ROIs were drawn on DWI images acquired prior to biopsy, referencing all available images including DCE, and mean ADC was measured. Anisotropy was quantified via variation in ADC values in the lesion core across directional DWI images. STATISTICAL TESTS: Improvement in specificity at 100% sensitivity was evaluated with exact McNemar test with 1-sided p-value < 0.05 indicating statistical significance. RESULTS: Using ADC thresholding that assures 100% sensitivity, non-mass and directional variance filtering improved the percent of avoidable biopsies to 42% from baseline of 10% achieved with ADC thresholding alone. Using LR-, filtering improved outcome to 0.06 from baseline 0.25 with ADC thresholding alone. ADC thresholding showed a lower percentage of avoidable biopsies in our cohort than reported in prior studies. When ADC thresholding was supplemented with filtering, the percentage of avoidable biopsies exceeded those of prior studies. DATA CONCLUSION: Supplementing ADC thresholding with filters excluding non-mass lesions and lesions with anisotropic characteristics on DWI can result in an increased number of avoidable biopsies.

Discrimination of benign from malignant breast lesions in dense breasts with model-based analysis of regions-of-interest using directional diffusion-weighted images.

BACKGROUND: There is an increasing interest in non-contrast-enhanced magnetic resonance imaging (MRI) for detecting and evaluating breast lesions. We present a methodology utilizing lesion core and periphery region of interest (ROI) features derived from directional diffusion-weighted imaging (DWI) data to evaluate performance in discriminating benign from malignant lesions in dense breasts. METHODS: We accrued 55 dense-breast cases with 69 lesions (31 benign; 38 cancer) at a single institution in a prospective study; cases with ROIs exceeding 7.50 cm2 were excluded, resulting in analysis of 50 cases with 63 lesions (29 benign, 34 cancers). Spin-echo echo-planar imaging DWI was acquired at 1.5 T and 3 T. Data from three diffusion encoding gradient directions were exported and processed independently. Lesion ROIs were hand-drawn on DWI images by two radiologists. A region growing algorithm generated 3D lesion models on augmented apparent-diffusion coefficient (ADC) maps and defined lesion core and lesion periphery sub-ROIs. A lesion-core and a lesion-periphery feature were defined and combined into an overall classifier whose performance was compared to that of mean ADC using receiver operating characteristic (ROC) analysis. Inter-observer variability in ROI definition was measured using Dice Similarity Coefficient (DSC). RESULTS: The region-growing algorithm for 3D lesion model generation improved inter-observer variability over hand drawn ROIs (DSC: 0.66 vs 0.56 (p < 0.001) with substantial agreement (DSC > 0.8) in 46% vs 13% of cases, respectively (p < 0.001)). The overall classifier improved discrimination over mean ADC, (ROC- area under the curve (AUC): 0.85 vs 0.75 and 0.83 vs 0.74 respectively for the two readers). CONCLUSIONS: A classifier generated from directional DWI information using lesion core and lesion periphery information separately can improve lesion discrimination in dense breasts over mean ADC and should be considered for inclusion in computer-aided diagnosis algorithms. Our model-based ROIs could facilitate standardization of breast MRI computer-aided diagnostics (CADx).

Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour.

Built structures, such as animal nests or buildings that humans occupy, serve two overarching purposes: shelter and a space where individuals interact. The former has dominated much of the discussion in the literature. But, as the study of collective behaviour expands, it is time to elucidate the role of the built environment in shaping collective outcomes. Collective behaviour in social animals emerges from interactions, and collective cognition in humans emerges from communication and coordination. These collective actions have vast economic implications in human societies and critical fitness consequences in animal systems. Despite the obvious influence of space on interactions, because spatial proximity is necessary for an interaction to occur, spatial constraints are rarely considered in studies of collective behaviour or collective cognition. An interdisciplinary exchange between behavioural ecologists, evolutionary biologists, cognitive scientists, social scientists, architects and engineers can facilitate a productive exchange of ideas, methods and theory that could lead us to uncover unifying principles and novel research approaches and questions in studies of animal and human collective behaviour. This article, along with those in this theme issue aims to formalize and catalyse this interdisciplinary exchange.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.

Can we identify general architectural principles that impact the collective behaviour of both human and animal systems?

The search for general common principles that unify disciplines is a longstanding challenge for interdisciplinary research. Architecture has always been an interdisciplinary pursuit, combining engineering, art and culture. The rise of biomimetic architecture adds to the interdisciplinary span. We discuss the similarities and differences among human and animal societies in how architecture influences their collective behaviour. We argue that the emergence of a fully biomimetic architecture involves breaking down what we call 'pernicious dualities' that have permeated our discourse for decades, artificial divisions between species, between organism and environment, between genotype and phenotype, and in the case of architecture, the supposed duality between the built environment and its builders. We suggest that niche construction theory may serve as a starting point for unifying our thinking across disciplines, taxa and spatial scales.This article is part of the theme issue 'Interdisciplinary approaches for uncovering the impacts of architecture on collective behaviour'.

Socioeconomic consequences of mercury use and pollution.

In the past, human activities often resulted in mercury releases to the biosphere with little consideration of undesirable consequences for the health of humans and wildlife. This paper outlines the pathways through which humans and wildlife are exposed to mercury. Fish consumption is the major route of exposure to methylmercury. Humans can also receive toxic doses of mercury through inhalation of elevated concentrations of gaseous elemental mercury. We propose that any effective strategy for reducing mercury exposures requires an examination of the complete life cycle of mercury. This paper examines the life cycle of mercury from a global perspective and then identifies several approaches to measuring the benefits of reducing mercury exposure, policy options for reducing Hg emissions, possible exposure reduction mechanisms, and issues associated with mercury risk assessment and communication for different populations.

Morphologic blooming in breast MRI as a characterization of margin for discriminating benign from malignant lesions.

RATIONALE AND OBJECTIVES: Develop a fully automated, objective method for evaluating morphology on breast magnetic resonance (MR) images and evaluate effectiveness of the new morphologic method for detecting breast cancers. MATERIALS AND METHODS: We present a new automated method (morphologic blooming) for identifying and classifying breast lesions on MR that measures margin sharpness, a characteristic related to blooming, defined as rapid enhancement, with a border that is initially sharp but becomes unsharp after 7 minutes. Independent training sets (98 biopsy-proven lesions) and testing sets (179 breasts, 127 patients, acquired at five institutions) were used. Morphologic blooming was evaluated as a stand-alone feature and as an adjunct to kinetics using free-response receiver operating characteristic and sensitivity analysis. Dependence of false-positive (FP) rates on acquisition times and pathologies of contralateral breasts were evaluated. RESULTS: Sensitivity of morphologic blooming was 80% with 2.46 FP per noncancerous breast: FPs did not vary significantly by acquisition times. FPs varied significantly by pathologies of contralateral breasts (cancerous contralateral: 4.29 FP/breast; noncancerous contralateral: 0.48 FP/breast; P < .0001). Evaluation of 45 cancers showed suspicious morphologies on 10/15 (67%) cancers with benign-like kinetics and suspicious kinetics on 5/10 (50%) cancers with benign-like morphologies. CONCLUSION: We present a new, fully automated method of identifying and classifying margin sharpness of breast lesions on MR that can be used to direct radiologists' attention to lesions with suspicious morphologies. Morphologic blooming may have important utility for assisting radiologists in identifying cancers with benign-like kinetics and discriminating normal tissues that exhibit cancer-like enhancement curves and for improving the performance of computer-aided detection systems.