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A method and computer-tool to estimate percentage MRI breast density using 3D T1-weighted Magnetic Resonance Imaging (MRI) is introduced, and compared with mammographic percentage density (XRM).
Ethical approval and informed consent were obtained. A method to assess MRI breast density as percentage volume occupied by water-containing tissue on 3D T1-weighted MR images is described and applied in a pilot study to 138 subjects who were imaged by both MRI and XRM during the MARIBS screening study. For comparison, percentage mammographic density was measured from matching XRMs as a ratio of dense to total projection areas scored visually using a 21 point score and measured by applying a 2D interactive program (CUMULUS). The MRI and XRM percent methods were compared, including assessment of left-right and inter-reader consistency.
Percent MRI density correlated strongly (r=0.78, p<0.0001) with percent mammographic density estimated using Cumulus. Comparison with visual assessment also showed a strong correlation. The mammographic methods overestimate density compared with MRI volumetric assessment by a factor approaching 2.
MRI provides direct 3D measurement of the proportion of water based tissue in the breast. It correlates well with visual and computerised percent mammographic density measurements. This method may have direct application in women having breast cancer screening by breast MRI and may aid in determination of risk.
Percentage mammographic density is a major risk-factor for breast cancer. Individuals with dense breasts have 4-6 fold greater chance of developing breast cancer than those with fatty breasts (1). There is however no standard, accurate method for measuring mammographic density. A two-dimensional (2D) interactive computer-programme (CUMULUS) has been widely used, giving percentage mammographic density and dense breast area (2). This method and other similar interactive computer and visual estimates analyse a three-dimensional (3D) organ by 2D techniques, so are inevitably limited. MRI has been explored as a method of estimating the water content of a breast via slices or slabs of breast tissue (3, 4). More recently MRI techniques for measuring MRI density based on segmentation of 3D images have been reported (5-7).
The MARIBS study (8) was a UK national MRI screening study of women at genetic risk of breast cancer. MRI provides radiation-free MRI density measurements suitable for young women and TP53 carriers, who may be susceptible to cancer initiation by radiation (9). Such screening may provide density measures that can be used in the risk-models applied to high-risk women (10). A method of assessing MRI density using 3D data has been developed, and in a pilot study is compared with existing methods of percentage mammographic density measurement.
Subjects were chosen from participants in the MARIBS study, in which 649 women aged 35-49 years with a very strong family history of breast cancer (estimated annual risk of breast cancer was at least 0.9%) attended for MRI and X-ray mammography (XRM) annually. A sample population of 138 subjects without pathologically confirmed findings of benign or malignant lesions, but with date-matched MRI and mammography, was chosen. The age distribution was similar to the main study population.
The MRI imaging technique has been described in detail elsewhere (11) and is detailed in Web Appendix 1 In brief: a 3D T1-weighted high resolution scan was performed prior to the dynamic contrast enhanced measurements and used for MRI density measurements, together with a proton density image which was used for uniformity correction. All acquisitions used for MRI density measurements employed a dedicated double breast-coil with scanning in the prone position without compression.
The detail of the method can be found in Web Appendix 2. In brief: an MRI density measurement tool (Fig 1) was developed that calculated the percentage MRI volumetric content of water based (dense) tissue in the breast. Water containing tissues were identified by interactive segmentation of tissues anterior to the pectoral muscle on the basis of signal intensity in the pre-contrast T1 weighted images. A coil-uniformity correction based on the proton density image was applied prior to segmentation of water-based and fat-based tissues. Percentage MRI Density was calculated as the ratio of the volume occupied by MRI water containing tissue to the total volume of breast tissue. (EB and SR) had trained to obtain consistent readings using this experimental method. EB read the images for all 138 cases reported here. SR read a subset for repeatability analysis (52 images from 26 women). Due to team changes, intra-reader repeatability estimates cannot be obtained for EB's MRI readings.
Original mammograms retrieved from the centres were digitised using an Array 2905 DICOM ScanPro Plus Laser Film Digitiser Version 1.3E (Array Corp. USA, USA) at optical density of 4.7. Medial-Lateral Oblique (MLO) XRM images were used for mammographic density assessment by the 2D interactive method developed by the University of Toronto (CUMULUS V3.1) (2, 12). This method estimates dense breast area and whole breast area from scanned mammograms and yields a percentage value for density. The pectoral muscle is excluded from the image before measuring. CUMULUS analyses were undertaken by 2 experienced observers. RW read all the images and a subset were read also by IW (59 images from 30 women) and RW independently second read 30 images from 30 women for repeatability studies. All readings were read as individual images blinded to all patient information and all other readings.
Mammographic density was assessed by experienced radiologists (CB & RW). The digitised images were viewed individually on a standard PC and a percentage estimate of density to 5% was made by the radiologist using a 21-point scale. This method is designed to detect differences smaller than one Boyd category (13). CB read all the images. RW undertook repeat readings of 10% of the images, and CB read the same 10% a second time for repeatability studies.
Firstly we assessed the relationship between the paired MRI percent density (volumetric) and projection based (Cumulus) mammographic percent density measurements, using correlation and regression analysis. The analysis included the right and left breasts of each woman where available (264 breasts from 137 women), using the sandwich variance estimator to allow for the non-independence of the two sides. The relationship between MRI-density and visually scored percent density on a 21-point scale was similarly assessed, based on the 210 breasts from 108 women for whom this information was available.
Secondly we tested left to right side density correlation for each of the modalities, to inform on the consistency of each approach. MRI-density estimates for both breasts were available for 137 women (132 women for the CUMULUS-based estimates). Systematic and absolute differences between the left and right breasts were tested for using t-tests and the difference in density estimates between sides was plotted against the average density estimate i.e. a Bland-Altman plot.
The images from a random sample of 30 women were analysed by a second reader to assess the inter-reader agreement of each method (26 women for the MRI method) using t-tests and Bland-Altman plots, as above.
The percent MRI and CUMULUS mammographic density findings showed a positive linear correlation, based on the images of 264 breasts from 133 women (correlation factor r=0.78) (Fig. 2). Linear regression analysis, allowing for the non-independence of the two images from each woman, gave the following regression equation:
The 95% CI for the slope was 0.48-0.63, p<0.001 i.e. the MRI-measure of the percentage density was over 40% lower than that estimated by the CUMULUS method. The relationship was weaker for the less-dense breasts; the slope was 0.25 for the 54 breasts with CUMULUS-density less than 20% (95% CI 0.07-0.43, p=0.007) and 0.62 (95% CI 0.50-0.73, p<0.001) for the 210 breasts with a CUMULUS-density above this level (p=0.001 for the difference). There was also evidence of a strong positive linear relationship between the MRI-density and the percentage area mammographic density estimated visually using the 21-point scale, with a very similar overall slope (slope=0.56, 95% CI=0.47-0.64, p<0.001).
The intra-modality left/right side correlation coefficients were 0.95 for MRI density and 0.84 for XRM percent density (Fig. 3a and 3b), which were not significantly different (p=0.16). There were no systematic differences between left and right breast percent densities as measured by either MRI (p=0.51) or XRM (p=0.85). The mean absolute difference in estimated percentage density between sides was larger for the XRM-based method (7.9 percentage point difference, 95% CI = 6.5-9.2) than for the MRI method (3.6 percentage point difference, 95% CI = 3.1-4.1, p<0.001 comparing MRI with XRM), with the XRM method showing a greater range of values, including some substantial differences between sides. However, when the estimates from the mammographic method were scaled according to the linear regression coefficients (estimated above) the mean absolute difference between sides was closer to that observed using MRI, with overlapping confidence intervals (4.4 percentage points, 95% CI=3.6-5.2).
Figures 3c and 3d show the MRI density measurements to have a higher inter-reader consistency than the XRM CUMULUS measurements for the 30 samples whose images were analysed by a second observer. The mean absolute difference between MRI readers was 4.3 percentage points (s.d=3.1, 95% CI = 3.4-5.2) but one MRI reader did not give estimates that were consistently higher than the other (p=0.13). The linear relationship between the density estimates from the two XRM readers was not quite as strong, although the difference was not significant (intraclass correlation coefficient = 0.0.79 for XRM; r=0.90 for MRI, p=0.16 for the interaction term in a linear regression model). The mean absolute difference in estimated density between observers was larger than for MRI (7.2 percentage points, s.d.=5.5, 95% CI=5.8-8.6, p=0.001 comparing MRI with XRM), but was almost identical to the difference between MRI readers once the XRM-based estimates had been scaled according to the MRI/XRM regression-coefficients (4.3 percentage points difference, 95% CI 3.4-5.2). The differences between XRM observers were not consistent in direction (p=0.30).
Thirty images were independently re-analysed by the original XRM reader, with a mean absolute difference in estimated density between reads of 5.2 percentage points (95% CI=3.9-6.4).
Mammographic breast density has been identified as an independent risk factor for breast cancer (1). Women with density in more than 75% of the mammogram have a risk of breast cancer 4.7 times those with density in 10% or less of the mammogram (14). Genetic differences may affect density, which may further modulate the risk of breast cancer (15). High mammographic density increases the chances of missing cancer on X-ray mammography. MRI screening is included in recent recommendations for women at high risk of familial breast cancer (16) or who have received supra-diaphragmatic irradiation for lymphoma (17), and breast density may guide this use in high risk groups (18).
Mammographic density evaluates the x-ray attenuation of different tissues in a 2D-projection of a 3D-volume. In most studies these are not calibrated directly for compression, x-ray exposure and film response. Percent mammographic density does not reliably measure the true proportion of parenchymal tissue in the breast, but correlates well with risk in many studies (1).
The MRI estimates of breast density described here provide a direct 3D method of assessing the proportion of water-based tissues (presumed parenchyma) in a volume of breast tissue, which may more accurately assess the proportion of parenchyma in the breast. However it is intrinsically a different (although related) estimate of breast density to that provided by mammography. It has not yet been validated in a case-control setting to determine whether it is predictive of cancer risk. It is an accessible technique for women where there is concern about radiation sensitivity due to genetic defect (eg TP53) or young age. Neither technique truly measures the entire breast, due to anatomical issues in sampling of, for example, the axilla.
Figure 2 shows a good correlation between volume breast percent density measured by MRI and mammographic area percent density estimated using CUMULUS, although the slope of the regression line implies that projection-based mammographic assessment overestimates the actual breast percentage density by a factor approaching two. This can be explained by the 2D representation of the breast on which the threshold readings are made. The fat above and below the breast plate is not included in the fat measurement. A well accepted 21-point visual assessment scheme (13) similar to the Boyd score (19) also showed a good correlation with MRI breast density. Lee et al 1997 (5) found a similar correlation (r=.63) in their study and similarly found differences in relative accuracy when compared with percent mammographic density between the measures in dense and fatty breasts.
The techniques used in this study sample the breast tissue in different ways, and none of them can encompass all of the breast tissue, due to limitations in the field of view, superposition of other tissues, and areas of motion. 2D XRM techniques are limited by the need to assess projections of X-ray attenuation, non-linearities and variability in breast thickness and compression, X-ray exposure and film processing of the data. Despite these limitations mammographic density has been shown to predict breast cancer risk well. The MRI assessment of density is affected by coil uniformity, addressed by a correction method. Partial volume effects may contribute some error to the analysis, although this is expected to be small. Currently assessment of breast density is restricted to volumes (for the prone patient) lying below the pectoral muscle.
Our results can be compared with previous MRI measures of breast density, none of which have been established into analytical studies beyond their first experimental description. The method of Lee required manual mapping of the individual slices of the 3D MRI study (5).. Wei et al (7)used a semi automated method of segmentation with good repeatability (0.99), which exceeded the mammographic density measure (0.91 and 0.89 for CC and MLO views). Klifa et al (6) applied fuzzy c-means clustering and manual segmentation to obtain a breast tissue index that correlated with mammographic density (r=0.75 and r=0.78 respectively). Graham et al (4) used relative water content (r=0.79, p<0.0001) and mean T2 relaxation time (r=−0.61, P<0.0001). This team also showed that their measure of density was associated with the same sociodemographic and anthropometric risk factors for breast cancer as are found with percent mammographic density. Since many of the mammographic methods are operator dependent and time consuming, the MRI measure has the potential to give an equally reliable method with full automation. Our present method uses interactive thresholding and takes significant operator time to apply. However our team are attempting to introduce automation to the computer procedure. Our present pilot project of 138 women is larger than those presented in the literature (the projects involve Klifa 10 cases (6), Lee 40 cases (5), Graham 42 cases (4), Poon 23 cases (3), Wei 67 cases (7)), and form the first stage in the plan to analyse MRI density in the high risk group of the MARIBS multicentre high risk screening study.
A limitation of the study is that no validation with biological phantoms was undertaken for MRI. Although mammographic density is widely used, the amount of such biological validation is extremely limited, since the experiments are difficult to devise.
Our pilot study shows that breast MRI assessment of percent density correlates well with mammographic measures of percent density. Our study is based on the supposition that they measure the same tissue, for which we do not at present have pathological evidence. Where MRI screening is undertaken in high risk young women it may have potential to provide a useful measure of MRI density. This may then support the application of surveillance, risk assessment and preventative strategies. This will only be true if better comparisons can be made to large epidemiological studies on cases and controls, and the histological validation can be established. This is the pilot to a larger study in high risk women.
Histograms display. Optimal water-fat threshold is superimposed on the histogram of the signal intensities from the voxels within the breast, after segmenting the background. The threshold is interactively adjusted using fusion display as a visual aid for segmentation accuracy.
This study was funded by Cancer Research UK (C11518/A5644). It uses data collected as part of the MARIBS study - a UK-wide collaboration of 22 genetics centres and their associated MRI and mammography departments. The key clinical contributors are listed in Web Appendix 3. We acknowledge the work of many others—radiographers, nurses, clerical staff, physicists, engineers—whose contribution is important but who have not been named. A special thank you is extended to the women and their surgeons and oncologists who referred them without whom the study would not have been possible. The national study was supported by a grant from the UK Medical Research Council (G9600413). The cost of the MRI studies was funded from subvention funding for research from the UK National Health Service. The protocol was based in part on developments supported by the Cancer Research UK and the Yorkshire Cancer Research Campaign. Contributions towards training and education were made by Schering Healthcare (Burgess Hill, UK) and Oracle Education (Bracknell, UK). DFE and DT are funded by Cancer Research UK.
The MR equipment supplied by four different manufactures (GE Medical Systems, Slough, UK; Marconi Medical Systems and Philips Medical Systems, Reigate, UK; Siemens Medical Solutions, Bracknell, UK) had field strengths in the range 1.0 – 1.5 Tesla and purpose built dual breast coils. All of the systems used the agreed MARIBS protocol (20). The annual MRI examination comprised three types of measurement: dynamic contrast-enhanced T1 weighted, high spatial resolution T1 weighted, and a proton density measurement prior to contrast. The dynamic contrast-enhanced measurement consisted of two reference pre-contrast datasets and four to six post-contrast datasets acquired after the administering of a contrast agent with temporal resolution of 90s. The datasets were acquired in the coronal plane using a 3D T1-weighted sequence (fast spoiled gradient echo, fast low-angle shot, or fast field echo) with a spatial resolution of 1.33 × 1.33 × 2.5 mm, and the image array dimensions (frequency × phase × number of slices) 256 × 128 × 60-64. Dedicated bi-lateral breast coils were used. The contrast agent, gadopentetate dimeglumine (Gd-DTPA; Schering Healthcare, Burgess Hill, UK), was administered intravenously as a bolus at 0.2 mmol/kg body weight. Two 3D high spatial resolution image sets were acquired, one before and one after the dynamic measurement. These were acquired in the coronal plane with a spatial resolution of 0.7 × 0.7 × 2.5 mm (the latter with optional fat saturation). Proton density images were acquired in the coronal plane with the same spatial resolution and array dimensions as the dynamic datasets.
For the purpose of this study, MRI density was defined as the relative volumetric content of water-containing (i.e. dense) tissue in the breast. Thus MRI density was computed as the ratio of volume occupied by water containing tissue to the total volume of breast tissue considered. T1-weighted images were selected for the density assessment, as this type of imaging sequence provides good contrast between water and fat. (Signal from fat on T1-weighted image is significantly stronger.) The voxels containing dense tissue, fat, and background air were discriminated by thresholding on signal intensities. The analysis was limited to a volume of breast defined by a straight line corresponding with a slice of image data anterior to the pectoral muscles.
The density measurement tool that was used in this work (Fig. 1) allows the trained user (EB) to interactively segment the voxels of a T1-weighted image of the breast into three classes: dense tissue, fat, and the background. The segmentation process is carried out by the interactive adjustment of two thresholds, the first discriminating background from the breast volume, and the second discriminating water-containing tissue from fat. While adjusting the thresholds, the user observes the simultaneously updated fusion display, which serves as a visual aid to check the segmentation accuracy (Web Fig. 1). The fusion display shows a color-encoded segmentation map where each of the above three classes is presented by a different customizable color, which is superimposed on the original image.
MR data usually suffer from several sources of image non-uniformity. Non-uniformity of the breast MR image can cause a more than 50% difference in signal from similar tissues at different locations within the coil (Web Fig. 2). This presents a difficulty for threshold-based segmentation approaches. The tool uses a uniformity correction algorithm normalizing the image based on the proton density data (21). Uniformity correction is performed after the background is segmented, as the background signal is unrelated to the non-uniformity of the magnetic field. Thus the first threshold, segmenting the background, is applied to uniformity uncorrected voxel data. Uniformity correction allows a single threshold discriminating dense tissue from fat to be chosen, which is applicable to the whole volume of both breasts. Histograms of the voxel data are available as additional quality guides for segmentation and uniformity correction (Web Fig. 3). Finally, the relative volumetric content of dense tissue is computed as a ratio of the number of voxels classified as containing dense tissue to the total number of voxels within the breast volume. Separate values of density are obtained for the left and right breasts. The tool was developed “in-house” using IDL (Research Systems Inc, Boulder, Colorado) and integrated into the MRIBview specialized Breast DCE-MRI workstation (22,23,24) (Institute of Cancer Research, Sutton, UK). A second reader (SR) assessed a 10% sample by the same methods to check inter-observer repeatability.
The tool also allows uniformity correction of T1-weighted images lacking complimentary proton density measurements. The algorithm employed in these cases is based on a single interactively adjustable parameter, which generates a quadratic profile multiplying voxels of each j-th slice by the correction factor Fj:
where a is the index of first slice anterior to pectoral muscles, n is the index of the most anterior slice (index increases in anterior direction), and C is the parameter interactively adjustable by the user to achieve best classification possible, as confirmed using the information shown in Web Figs 1 and 3.
MO Leach (Chairman and Principal Investigator, Professor of Physics as Applied to Medicine)
J Brown (Health Economist)
A Coulthard (Consultant Radiologist)
AK Dixon (Professor and Honorary Consultant Radiologist)
JM Dixon (Consultant Surgeon and Senior Lecturer)
D Easton (Professor of Genetic Epidemiology)
RA Eeles (Reader in Clinical Cancer Genetics and Honorary Consultant in Cancer Genetics and Clinical Oncology)
DG Evans (Consultant Geneticist)
FJ Gilbert (Professor of Radiology)
J Hawnaur (Consultant Radiologist)
P Kessar (Consultant Radiologist)
SR Lakhani (Professor of Breast Cancer Pathology)
S Moss (Epidemiologist)
A Nerurkar (Consultant Pathologist)
AR Padhani (Consultant Radiologist)
AJ Potterton (Consultant Radiologist)
BAJ Ponder (Professor and Head of Department of Oncology)
J Sloane (Professor of Pathology; deceased)
LW Turnbull (Professor of Radiology and Honorary Consultant)
LG Walker (Professor of Cancer Rehabilitation)
RML Warren (Consultant Radiologist)
LJ Pointon (Study Coordinator)
GE Kwan-Lim (Study Coordinator)
RJC Hoff (Assistant Study Coordinator)
K Chan (Data Manager)
M Khazen (Image Analysis Physicist)
RML Warren (Study Radiologist)
J Anderson (Health Psychologist)
C Levesley (Psychology Research Assistant)
I Griebsch (Health Economist)
D Thompson (Statistician)
C Hayes (Study Physicist)
R Gregory (Study Physicist)
M Sydenham (Acting Study Coordinator)
K Bletcher (Data Manager)
GP Liney (Study Physicist)
B Browne (Data Manager)
K McPherson (Chairman, Visiting Professor of Public Health Epidemiology)
R Blamey (Professor Emeritus and Consultant Breast Surgeon)
SW Duffy (Professor of Cancer Screening).
A Howell (Chairman, Professor of Medical Oncology)
D Easton (Study Statistician, Genetic Epidemiologist)
DG Evans (Study Representative, Consultant Geneticist)
JE Husband (Host Institution Representative, Professor of Radiology)
E Maher(Independent Member, Professor of Medical Genetics)
MJ Michell (Independent Member, Consultant Radiologist)
RML Warren (Study Radiologist, Consultant Radiologist)
W Watson (Consumer Representative, Founder of the Hereditary Breast Cancer Group).
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