Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software

Purpose: Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation, however the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. Methods: Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased to simulate measurement error. Increments of 1mm were used up to ±15% error of recorded CBT was achieved. New images were created for each 1mm step in thickness resulting in a total of 974 images which then had Volpara Density Grade (VDG) and volumetric density percentage assigned. Results: A change in VDG was recorded in 38.5% (n= 20) of mammograms when applying ±15% error to the recorded CBT and 11.5 % (n= 6) were within the QC standard prescribed error of ±5mm. Conclusion: The current QC standard of ±5mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered

Joint AAPM Task Group 282/EFOMP Working Group Report: Breast dosimetry for standard and contrast‐enhanced mammography and breast tomosynthesis

: Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard mammography, digital breast tomosynthesis, and their contrast-enhanced variants. In addition, it includes models of the breast in both the cranio-caudal and the medio-lateral oblique views. Special emphasis was placed on the breast and system models used being based on evidence, either by analysis of large sets of patient data or by performing measurements on imaging devices from a range of manufacturers. Due to the vast number of dose conversion coefficients resulting from the developed model, and the relative complexity of the calculations needed to apply it, a software program has been made available for download or online use, free of charge, to apply the developed breast dosimetry method. The program is available for download or it can be used directly online. A separate User's Guide is provided with the software

A review on automatic mammographic density and parenchymal segmentation

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models

Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134976/1/mp8512.pd

Effective radiation attenuation calibration for breast density: compression thickness influences and correction

<p>Abstract</p> <p>Background</p> <p>Calibrating mammograms to produce a standardized breast density measurement for breast cancer risk analysis requires an accurate spatial measure of the compressed breast thickness. Thickness inaccuracies due to the nominal system readout value and compression paddle orientation induce unacceptable errors in the calibration.</p> <p>Method</p> <p>A thickness correction was developed and evaluated using a fully specified two-component surrogate breast model. A previously developed calibration approach based on effective radiation attenuation coefficient measurements was used in the analysis. Water and oil were used to construct phantoms to replicate the deformable properties of the breast. Phantoms consisting of measured proportions of water and oil were used to estimate calibration errors without correction, evaluate the thickness correction, and investigate the reproducibility of the various calibration representations under compression thickness variations.</p> <p>Results</p> <p>The average thickness uncertainty due to compression paddle warp was characterized to within 0.5 mm. The relative calibration error was reduced to 7% from 48-68% with the correction. The normalized effective radiation attenuation coefficient (planar) representation was reproducible under intra-sample compression thickness variations compared with calibrated volume measures.</p> <p>Conclusion</p> <p>Incorporating this thickness correction into the rigid breast tissue equivalent calibration method should improve the calibration accuracy of mammograms for risk assessments using the reproducible planar calibration measure.</p

Mammographic breast density: comparison of methods for quantitative evaluation.

PURPOSE: To evaluate the results from two software tools for measurement of mammographic breast density and compare them with observer-based scores in a large cohort of women. MATERIALS AND METHODS: Following written informed consent, a data set of 36 281 mammograms from 8867 women were collected from six United Kingdom centers in an ethically approved trial. Breast density was assessed by one of 26 readers on a visual analog scale and with two automated density tools. Mean differences were calculated as the mean of all the individual percentage differences between each measurement for each case (woman). Agreement in total breast volume, fibroglandular volume, and percentage density was assessed with the Bland-Altman method. Association with observer's scores was calculated by using the Pearson correlation coefficient (r). RESULTS: Correlation between the Quantra and Volpara outputs for total breast volume was r = 0.97 (P < .001), with a mean difference of 43.5 cm(3) for all cases representing 5.0% of the mean total breast volume. Correlation of the two measures was lower for fibroglandular volume (r = 0.86, P < .001). The mean difference was 30.3 cm(3) for all cases representing 21.2% of the mean fibroglandular tissue volume result. Quantra gave the larger value and the difference tended to increase with volume. For the two measures of percentage volume density, the mean difference was 1.61 percentage points (r = 0.78, P < .001). Comparison of observer's scores with the area-based density given by Quantra yielded a low correlation (r = 0.55, P < .001). Correlations of observer's scores with the volumetric density results gave r values of 0.60 (P < .001) and 0.63 (P < .001) for Quantra and Volpara, respectively. CONCLUSION: Automated techniques for measuring breast density show good correlation, but these are poorly correlated with observer's scores. However automated techniques do give different results that should be considered when informing patient personalized imaging. (©) RSNA, 2015 Clinical trial registration no. ISRCTN 73467396.Supported by the National Institute for Health Research’s Health Technology Assessment Programme.This is the final version of the article. It first appeared at http://pubs.rsna.org/doi/full/10.1148/radiol.1414150

Diagnostic Reference Levels for digital mammography in Australia

Aims: In 3 phases, this thesis explores: radiation doses delivered to women during mammography, methods to estimate mean glandular dose (MGD), and the use of mammographic breast density (MBD) in MGD calculations. Firstly, it examines Diagnostic reference levels (DRLs) for digital mammography in Australia, with novel focus on the use of compressed breast thickness (CBT) and detector technologies as a guide when determining patient derived DRLs. Secondly, it analyses the agreement between Organ Dose estimated by different digital mammography units and calculated MGD for clinical data. Thirdly, it explores the novel use of MBD in MGD calculations, suggesting a new dose estimation called the actual glandular dose (AGD), and compares MGD to AGD. Methods: DICOM headers were extracted from 52405 anonymised mammograms using 3rd party software. Exposure and QA information were utilised to calculate MGD using 3 methods. LIBRA software was used to estimate MBD for 31097 mammograms. Median, 75th and 95th percentiles were calculated across MGDs obtained for all included data and according to 9 CBT ranges, average population CBT, and for 3 detector technologies. The significance of the differences, correlations, and agreement between MGDs for different CBT ranges, calculation methods, and different density estimation methods were analysed. Conclusions: This thesis have recommended DRLs for mammography in Australia, it shows that MGD is dependent upon CBT and detector technology, hence DRLs were presented as a table for different CBTs and detectors. The work also shows that Organ Doses reported by vendors vary from that calculated using established methodologies. Data produced also show that the use of MGD calculated using standardised glandularities underestimates dose at lower CBTs compared to AGD by up to 10%, hence, underestimating radiation risk. Finally, AGD was proposed; it considers differences in breast composition for individualised radiation-induced risk assessment

Development of a phantom to test fully automated breast density software – a work in progress

Objectives: Mammographic density (MD) is an independent risk factor for breast cancer and may have a future role for stratified screening. Automated software can estimate MD but the relationship between breast thickness reduction and MD is not fully understood. Our aim is to develop a deformable breast phantom to assess automated density software and the impact of breast thickness reduction on MD. Methods: Several different configurations of poly vinyl alcohol (PVAL) phantoms were created. Three methods were used to estimate their density. Raw image data of mammographic images were processed using Volpara to estimate volumetric breast density (VBD%); Hounsfield units (HU) were measured on CT images; and physical density (g/cm3) was calculated using a formula involving mass and volume. Phantom volume versus contact area and phantom volume versus phantom thickness was compared to values of real breasts. Results: Volpara recognized all deformable phantoms as female breasts. However, reducing the phantom thickness caused a change in phantom density and the phantoms were not able to tolerate same level of compression and thickness reduction experienced by female breasts during mammography. Conclusion: Our results are promising as all phantoms resulted in valid data for automated breast density measurement. Further work should be conducted on PVAL and other materials to produce deformable phantoms that mimic female breast structure and density with the ability of being compressed to the same level as female breasts. Advances in knowledge: We are the first group to have produced deformable phantoms that are recognized as breasts by Volpara software

Pathways to clinical CLARITY: volumetric analysis of irregular, soft, and heterogeneous tissues in development and disease

AbstractThree-dimensional tissue-structural relationships are not well captured by typical thin-section histology, posing challenges for the study of tissue physiology and pathology. Moreover, while recent progress has been made with intact methods for clearing, labeling, and imaging whole organs such as the mature brain, these approaches are generally unsuitable for soft, irregular, and heterogeneous tissues that account for the vast majority of clinical samples and biopsies. Here we develop a biphasic hydrogel methodology, which along with automated analysis, provides for high-throughput quantitative volumetric interrogation of spatially-irregular and friable tissue structures. We validate and apply this approach in the examination of a variety of developing and diseased tissues, with specific focus on the dynamics of normal and pathological pancreatic innervation and development, including in clinical samples. Quantitative advantages of the intact-tissue approach were demonstrated compared to conventional thin-section histology, pointing to broad applications in both research and clinical settings.</jats:p