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

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

High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in high-density breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in high-resolution mammograms. The training images were split by breast density BI-RADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in high-density breasts by 2% and 6% in two different test sets and was useful as a domain adaptation technique. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.Comment: 9 figures, 3 table

The impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings

Introduction: Mammography is the most used breast screening tool and was proven to reduce breast-cancer-associated mortality. The estimated sensitivity of mammography varies between 77% and 95%; however, sensitivity could be 26% lower in dense breasts than in entirely fatty breasts. The ability to represent the complex 3D breast architecture and early changes in anatomical structures in a 2D view is the biggest challenge for mammography. In Digital Breast Tomosynthesis (DBT), tomographic images are reconstructed from multiple projections acquired from different angles. This technique allows the generation of 3D data, reduction of tissue overlap and allows better evaluation of masses, architectural distortion, and asymmetries compared with conventional two-dimensional mammographic images.Objective: To evaluate the impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings.Methods: Prospective cohort for 180 women with mammographic non-mass findings who presented to Alexandria University Radio diagnosis Department either for screening or diagnostic purposes between July 2019 and August 2020 with mean age 51.44 ± 10.67 . Digital breast tomosynthesis and ultrasound was done for all patients. Lesions were evaluated on DM; DBT alone then combined DBT & DM. Comparison of results according to changes in BIRADS, diagnostic performance using histopathology as gold standard.Results: 208 non-mass findings were detected by conventional mammography (104 asymmetry, 35 architectural distortion, 69 micro calcifications), Tomosynthesis reduced the BIRADS 3 count by 32%, upgraded the count of BIRADS 4 lesions by 11.4% while upgraded the BIRADS 2 by 18.9% with consequent improvement of sensitivity and specificity, PPV, NPV and accuracy to 96%, 95%, 94%,97%, and 95.6%.Conclusion: Combined FFDM and DBT improved the diagnostic performance in evaluation of non-mass findings and proper BIRADS categorization

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

PB.23: Effect of detector type on cancer detection in digital mammography

This work measured the effect that image quality associated with different detectors has on cancer detection in mammography using a novel method for changing the appearance of images.\ud \ud A set of 270 mammography cases (one view, both breasts) was acquired using five Hologic Selenias and two Hologic Dimensions X-ray units: 80 normal, 80 with simulated inserted subtle calcification clusters, 80 with subtle real noncalcification malignant lesions and 30 with benign lesions (biopsy proven). These 270 cases (Arm 1) were converted to appear as if they had been acquired on two other imaging systems: needle image plate computed radiography (CR) (Arm 2) and powder phosphor CR (Arm 3). Three experienced mammography readers marked the location of suspected cancers in the images and classified whether each lesion would require further investigation and the confidence in that decision. Performance was calculated as the area under curve (AUC) of the alternative free-response receiver operating characteristic curv