This study investigates the impact of breast density on visual searching pattern. A set of 74 one-view malignancy containing mammographic images were examined by 7 radiologists. Eye position was recorded and visual search parameters such as total time examining a case, time to hit the lesion, dwell time and number of hits per area were collected. Fixations were calculated in 3 areas of interests: background breast parenchyma, dense areas of parenchyma and lesion. Significant increases in dwell time and number of hits in dense areas of parenchyma were noted for highcompared to low- mammographic density images when the lesion overlay the fibroglandular tissue (p<0.01). When the lesion was outside the fibroglandular tissue, significant increase in dwell time and number of hits in dense areas of parenchyma in high- compared to low- mammographic density images were observed (p<0.01). No significant differences have been found in total time examining a case, time to first fixate the lesion, dwell time and number of hits in background breast parenchyma and lesion areas. In conclusion, our data suggests that dense areas of breast parenchyma attract radiologists’ visual attention. Lesions overlaying the fibroglandular tissue were detected faster, therefore lesion location, whether overlaying or outside the fibroglandular tissue, appeared to have an impact on radiologists' visual searching pattern.
Mammographic breast density (MBD) is a risk factor for breast cancer. Both qualitative and quantitative methods have been used to evaluate MBD. However as it is impossible to measure the actual weight or volume of fibroglandular tissue evident on a mammogram, therefore it is hard to know the true correlation between measured mammographic density and the fibroglandular tissue volume. A phantom system has been developed that represents glandular tissue within an adipose tissue structure. Although a previous study has found strong correlation between the synthesised glandular mass and several image descriptors, it is not known if the correlation is still present when a high level of background noise is introduced. The background noise is required to more realistically simulate clinical image appearance. The aim of this study is to investigate if the correlation between percentage density, integrated density, and standard deviation of mean grey value of the whole phantom and simulated glandular tissue mass is affected by background noise being added to the phantom images. For a set of one hundred phantom mammographic images, clustered lumpy backgrounds were synthesised and superimposed onto phantom images. The correlation between the synthesised glandular mass and the image descriptors were calculated. The results showed the correlation is strong and statistically significant for the above three descriptors with r is 0.7597, 0.8208, and 0.7167 respectively. This indicates these descriptors may be used to assess breast fibroglandular tissue content of the breast using mammographic images.
The aim of this study is to examine the impact of breast density and lesion location on detection. A set of 55 mammographic images (23 abnormal images with 26 lesions and 32 normal images) were examined by 22 expert radiologists. The images were classified by an expert radiologist according to the Synoptic Breast Imaging Report of the National Breast Cancer Centre (NBCC) as having low mammographic density (D1<25% glandular and D2> 25-50% glandular) or high density (D3 51-75% glandular and D4> 75-glandular). The observers freely examined the images and located any malignancy using a 5-point confidence. Performance was defined using the following metrics: sensitivity, location sensitivity, specificity, receiver operating characteristic (ROC Az) curves and jackknife free-response receiver operator characteristics (JAFROC) figures of merit. Significant increases in sensitivity (p= 0.0174) and ROC (p=0.0001) values were noted for the higher density compared with lower density images according to NBCC classification. No differences were seen in radiologists’ performance between lesions within or outside the fibroglandular region. In conclusion, analysis of our data suggests that radiologists scored higher using traditional metrics in higher mammographic density images without any improvement in lesion localisation. Lesion location whether within or outside the fibroglandular region appeared to have no impact on detection abilities suggesting that if a masking effect is present the impact is minimal. Eye-tracking analyses are ongoing.
High mammographic density is a risk factor for breast cancer. As it is impossible to measure actual weight or volume of fibroglandular tissue evident within a mammogram, it is hard to know the correlation between measured mammographic density and the actual fibroglandular tissue volume. The aim of this study is to develop a phantom that represents glandular tissue within an adipose tissue structure so that correlations between image feature descriptors and the synthesised glandular structure can be accurately quantified. In this phantom study, ten different weights of fine steel wool were put into gelatine to simulate breast structure. Image feature descriptors are investigated for both the whole phantom image and the simulated density. Descriptors included actual area and percentage area of density, mean pixel intensity for the whole image and dense area, standard deviation of mean intensity, and integrated pixel density which is the production of area and mean intensity. The results show high level correlation between steel-wool weight and percentage density measured on images (r = 0.8421), and the integrated pixel density of dense area (r = 0.8760). The correlation is significant for mean intensity standard deviation for the whole phantom (r = 0.8043). This phantom study may help identify more accurate descriptors of mammographic density, thus facilitating better assessments of fibroglandular tissue appearances.
The main aim of this study is to investigate the outlining and categorising of mammographic breast density by expert
radiologists in order to help to understand what kind of region radiologists perceive as breast density and how they assess
the density of a mammogram. It investigates inter-radiologist variability in breast density outlining and assessment.
Forty-five normal cranio-caudal view mammograms with a range of appearances of breast density were presented to
twenty radiologists. Each participant was asked to manually outline any mammographic breast density using an interactive pen tablet and to visually classify mammographic breast density in two ways by using the BI-RADS density categorization system by the American College of Radiology, and by estimating the percentage of area of mammographic breast density. Large differences were found in breast density outlining for all BI-RADS density categories. Scattered and patchy breast density appeared to be associated with large variation in outlining. There was
moderate inter-radiologist agreement in BI-RADS density categorising (Kappa = 0.489). Breast density is a complex
radiological feature that impacts upon assessment consistency.