Impact of objective volumetric breast density estimates on mean glandular dose calculations in digital mammography

Transcription

1 Impact of objective volumetric breast density estimates on mean glandular dose calculations in digital mammography Poster No.: C-1576 Congress: ECR 2015 Type: Scientific Exhibit Authors: G. Gennaro, I. Sechopoulos, L. Gallo, V. Rossetti, R Highnam ; Padova/IT, Atlanta, GA/US, Castelfranco Veneto/IT, 4 5 Torino/IT, Wellington/NZ Keywords: Breast, Radiation physics, Computer applications, Mammography, Dosimetry, Equipment, Quality assurance DOI: /ecr2015/C-1576 Any information contained in this pdf file is automatically generated from digital material submitted to EPOS by third parties in the form of scientific presentations. References to any names, marks, products, or services of third parties or hypertext links to thirdparty sites or information are provided solely as a convenience to you and do not in any way constitute or imply ECR's endorsement, sponsorship or recommendation of the third party, information, product or service. ECR is not responsible for the content of these pages and does not make any representations regarding the content or accuracy of material in this file. As per copyright regulations, any unauthorised use of the material or parts thereof as well as commercial reproduction or multiple distribution by any traditional or electronically based reproduction/publication method ist strictly prohibited. You agree to defend, indemnify, and hold ECR harmless from and against any and all claims, damages, costs, and expenses, including attorneys' fees, arising from or related to your use of these pages. Please note: Links to movies, ppt slideshows and any other multimedia files are not available in the pdf version of presentations. Page 1 of 20

2 Aims and objectives The mean glandular dose (MGD) is the parameter used to estimate the amount of radiation dose absorbed by the breast during mammography. MGD is calculated by multiplying the air-kerma measured at the breast surface entrance by appropriate conversion factors obtained from Monte Carlo calculations [1-3] or analytical models [4]. Depending on the considered model, conversion factors count for the energy characteristics of the X-ray spectrum selected for breast exposure, usually represented by the half value layer (HVL), and for the breast absorption "properties", i.e. compressed breast thickness and "composition", where the composition is given as percentage of glandular tissue, assuming that the breast is composed of a certain fraction of glandular tissue and a complementary fraction of adipose tissue [1-2]. While the HVL and the compressed breast thickness can be experimentally determined, the glandular tissue fraction, often reported as "breast density" can only be hypothesized. In the past, breast density (BD) was visually assessed by radiologists through the estimation of the amount of "white" in the overall distribution of "black and white" of the mammography images. Breast density was considered to range between 0% and 100%, where 0% characterized breasts composed by adipose tissue which appeared as "transparent" when imaged by X-rays, while 100% was assigned to breasts consisting essentially of glandular tissue and appearing as "fully opaque" at X-rays. In this context, the intermediate composition, 50% adipose - 50% glandular, was considered a "typical value", characterizing the majority of breasts [1]. For this reason, when breast density assessment by radiologists was not available, the estimation of MGD was done assuming that all breasts had the same fifty-fifty composition. This general assumption produced a large uncertainty on the MGD estimation, not counting for the wide, individual differences in breast composition. The introduction of digital technologies in mammography has favored the development of software tools able to quantify the percentage breast density from the digital mammograms [5-6]. The use of such tools have also indicated that the fifty-fifty assumption usually made for MGD estimation is very far from the results obtained computing breast density by volumetric methods obtained from the digital images, applying physical models to reconstruct the effective breast absorption [7-8]. In this work, a large number of mammography images collected from different digital mammography equipment was processed by a commercial software and volumetric breast density (VBD) measured. Each VBD value was used to determine the dose conversion factor associated to breast composition, as required by the Dance's dose model [1], and mean glandular dose was estimated. MGD "adjusted" by the compositional Page 2 of 20

3 factor was compared with MGD values provided by manufacturers and stored in the image file headers. Methods and materials A total of digital mammograms of 5076 patients (standard mammography consists of two projection images per breast, i.e. 4 images per patient) were acquired by six mammography units, using the standard automatic exposure mode (AEC). In Table 1 on page 6, the total number of patients and images are given per mammography equipment used. Table 1: Brand and model of the digital mammography systems used, number of patients and images collected per each. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT All the images (raw data) were processed by Volpara (Volpara, Matakina Ltd, NZ) and volumetric breast density (VBD), i.e. the relative fraction of glandular tissue over the full breast volume, was calculated. MGD = Ki x g x c x s where Ki is the incident air kerma at the upper surface of the breast, measured without backscatter [9]; Page 3 of 20

4 g is the incident-air-kerma-to-mgd conversion factor for a 50% breast "glandularity" and an x-ray spectrum produced by a mammography system with molybdenum/molybdenum (Mo/Mo) anode/filter combination; c is a factor correcting from breast "glandularity" different from 50%; s is a factor correcting for spectra others than Mo/Mo. Conversion factors (g, c, and s) are published for different anode/filter combinations, HVL values and breast thickness in the range mm. Any other value can be determined by interpolation from published tables. Fraction of entrance dose absorbed by the breast increases while the photon energy (x-ray spectrum HVL) increases, and, for a given spectrum, decreases while breast thickness increases. Regarding differences among breast density, compared to the "fiftyfifty" case considered the standard for dosimetry, there is an increase of the fraction of absorbed dose up to 30% for breast glandularity below 50%, and a decrease down to 20% for breast glandularity below 50%. In Fig. 1 on page 6, g-factors for different HVLs and c-factors for different breast glandularity values are shown as a function of compressed breast thickness. Fig. 1: Left: g-factors versus compressed breast density for different HVL values. The g-value increases as x-ray energy increases, and decreases as breast thickness increases; Right: c-factors versus compressed breast density for different breast glandularities. The compositional factor is equal to 1 for 50% glandularity, while is greater than 1 for glandularity below 50% (increase in the dose absorption rate), and lower than 1 for glandularity above 50% (decrease in the dose absorption rate). References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT Page 4 of 20

5 A quick remark to remember that breast "glandularity" is slightly different than volumetric breast density, and has to deal with the breast model used in the Monte Carlo which produced the conversion factors [1]. In fact, breast was modelled by a semicircular volume made by a uniform mixture composed by a fraction of glandular tissue and the complementary fraction of fat, and a few millimeter shell of adipose tissue representing skin and subcutaneous fat (Fig. 2 on page 7). Compositional factors (c-factors) in the Dance model were obtained considering variations in the internal breast volume, while keeping the external shell unchanged. Fig. 2: Figure 2: Breast model used in the Dance model to derive dose conversion factors. Breast is represented by an internal volume, whose composition can change between 0% and 100% glandularity, circumscribed by an external shell (skin and subcutaneous fat) which is kept unchanged in the Monte Carlo. For this reason, breast glandularity does not coincide perfectly with breast density. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT Page 5 of 20

6 In this study, the Volpara software was used to calculate volumetric breast density from each image and subsequently determine breast glandularity, according to Dance model; glandularity was used to calculate a more accurate c-factor and "adjust" the estimated mean glandular dose compared to the 50/50 assumption. This was done overall and separately for each mammography unit. Distributions of MGD adjusted for breast density by different systems were compared with those obtained from MGD values provided by each manufacturer and stored in the DICOM image header. A Wilcoxon test for paired samples was applied. A p-value < 0.05 was considered statistically significant. Images for this section: Table 1: Brand and model of the digital mammography systems used, number of patients and images collected per each. Page 6 of 20

7 Fig. 1: Left: g-factors versus compressed breast density for different HVL values. The gvalue increases as x-ray energy increases, and decreases as breast thickness increases; Right: c-factors versus compressed breast density for different breast glandularities. The compositional factor is equal to 1 for 50% glandularity, while is greater than 1 for glandularity below 50% (increase in the dose absorption rate), and lower than 1 for glandularity above 50% (decrease in the dose absorption rate). Page 7 of 20

8 Fig. 2: Figure 2: Breast model used in the Dance model to derive dose conversion factors. Breast is represented by an internal volume, whose composition can change between 0% and 100% glandularity, circumscribed by an external shell (skin and subcutaneous fat) which is kept unchanged in the Monte Carlo. For this reason, breast glandularity does not coincide perfectly with breast density. Page 8 of 20

9 Results The overall mean compressed breast thickness was (53.4 ± 14) mm, with some differences among systems, as illustrated in Fig. 3 on page 13. A significant difference (p < ) versus the other systems was found for the GE Senographe DS that has 2 a smaller field-of-view (18 x 24 cm ) and therefore is used to image breasts limited in size, and for the IMG Giotto Image 3DL which showed a miscalibration in the estimation of compressed breast thickness. Fig. 3: Distributions of compressed breast thickness for each mammography unit. The mean value is around 55 mm for four out of six systems. Lower mean values were measured by the GE Senographe DS, with a smaller field-of-view compared to the other systems, suitable to image relatively small breasts, and by the IMS Giotto Page 9 of 20

10 systems which systematically underestimated breast thickness, demonstrating a calibration problem. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT The distribution of breast glandularity obtained by Volpara confirmed results already published, indicating that the "typical breast density" is much lower than 50%. Median glandularity was 16.4% (range 0% %), but very few breasts showed glandularity above 50% (Fig. 4 on page 14). Fig. 4: Distribution of breast glandularity obtained from Volpara. Most of breasts show glandularity much below 50%. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT Using breast glandularity to calculate MGD as required by the Dance model, and comparing results with MGD data provided by manufacturers (applying different models and assumptions on breast composition) and extracted from the image headers, comparison plots depicted in Fig. 5 on page 15 were obtained. Page 10 of 20

11 It can be noticed that the two MGD distributions (system and adjusted for breast density) are comparable only for the Hologic Dimensions 1, while for the other systems MGD adjusted for breast density) is mostly higher than system MGD, with the exception of the Philips Microdose system. Page 11 of 20

12 Fig. 5: Comparison of MGD provided by mammography manufacturers and MGD adjusted for breast density for each of the six systems included in the study. System MGD was mostly lower than MGD adjusted for breast density. The opposite was observed for the Philips system, for which system MGD distribution was higher, and for one of the two Hologic systems showing comparable results. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT The Wilcoxon test demonstrated that median system MGD were significantly lower than median MGD adjusted for breast density in all cases. (Table 2 on page 16). Table 2: Results from the Wilcoxon paired test, comparing system MGD provided by mammography manufacturers and MGD adjusted for breast density as calculated after breast density quantification. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT Differences between system MGD, where breast composition is often assumed to be 50/50, and MGD adjusted for breast density are better enhanced by Bland-Altman plots [10], as shown in Fig. 6 on page 17. The difference (with sign) between system MGD (MGDsystem) and MGD adjusted for breast density (MGDBDadj) is represented case-bycase as a function of system MGD. The solid line at the center of the distribution, parallel to the x-axis, is the mean value, while the two dashed lines indicate the width (two standard deviations) of the distribution of the differences. Page 12 of 20

13 Fig. 6: Bland-Altman plots of system MGD and MGD adjusted for BD, for each mammography unit. The absolute difference for each case is represented by an individual point on the y-axis as a function of the system MGD. The solid line, parallel to the x-axis is the mean difference; (center value of the distribution of MGD differences); the dashed lines are the (mean - 2*SD) and (mean + 2*SD) values, with SD standard deviation of the distribution of MGD differences. References: Radiology, Veneto Institute of Oncology (IOV), IRCCS - Padova/IT Images for this section: Page 13 of 20

14 Fig. 3: Distributions of compressed breast thickness for each mammography unit. The mean value is around 55 mm for four out of six systems. Lower mean values were measured by the GE Senographe DS, with a smaller field-of-view compared to the other systems, suitable to image relatively small breasts, and by the IMS Giotto systems which systematically underestimated breast thickness, demonstrating a calibration problem. Page 14 of 20

15 Fig. 4: Distribution of breast glandularity obtained from Volpara. Most of breasts show glandularity much below 50%. Page 15 of 20

16 Fig. 5: Comparison of MGD provided by mammography manufacturers and MGD adjusted for breast density for each of the six systems included in the study. System MGD was mostly lower than MGD adjusted for breast density. The opposite was observed for the Philips system, for which system MGD distribution was higher, and for one of the two Hologic systems showing comparable results. Page 16 of 20

17 Table 2: Results from the Wilcoxon paired test, comparing system MGD provided by mammography manufacturers and MGD adjusted for breast density as calculated after breast density quantification. Fig. 6: Bland-Altman plots of system MGD and MGD adjusted for BD, for each mammography unit. The absolute difference for each case is represented by an individual Page 17 of 20

18 point on the y-axis as a function of the system MGD. The solid line, parallel to the x-axis is the mean difference; (center value of the distribution of MGD differences); the dashed lines are the (mean - 2*SD) and (mean + 2*SD) values, with SD standard deviation of the distribution of MGD differences. Page 18 of 20

19 Conclusion This study proved that dose values (MGD) provided by manufacturers, using different models and assuming breast density a priori, are significantly different compared to those obtained after accounting for real differences among breast densities, now available processing images by suitable software tools. Software packages for quantitative breast density assessment can also be used to estimate mean glandular dose more accurately, allowing comparison of dosimetric data across manufacturers, applying the same computational method and consistent assumptions. Personal information Gisella Gennaro, PhD Radiology Unit, Veneto Institute of Oncology IOV - IRCCS, Padua, Italy gisella.gennaro@ioveneto.it Ioannis Sechopoulos, PhD Elmory University School of Medicine, Atlanta, GA/USA Lara Gallo, PhD Medical Physics Division, Castelfranco Veneto Hospital, Castelfranco, Italy Veronica Rossetti, PhD Medical Physics Division, "Città della Salute" Hospital, Turin, Italy Ralph Highnam, PhD Volpara Solutions Ltd, Wellington, New Zealand References Page 19 of 20

20 1. Dance D.R., Skinner C.L., Young K.C., Beckett J.R., Kotre,C.J. (2000), "Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol", Phys Med Biol 45: Boone J.M. (2002), Normalized glandular dose (DgN) coefficients for arbitrary X-ray spectra in mammography: computer-fit values of Monte Carlo derived data, Med Phys 29: Dance D.R., Young K.C., van Engen R.E. (2009), Further factors for the estimation of mean glandular dose using the United Kingdom, European and IAEA breast dosimetry protocols, Phys Med Biol 54: Sobol W.T., Wu X. (1997), Parametrization of mammography normalized average glandular dose tables, Med Phys 24: Highnam R., Jeffreys M., McCormack V., Warren R., Davey Smith G., Brady M. (2007), Comparing measurements of breast density, Phys Med Biol 52: Yaffe M.J. (2008), Mammographic density. Measurement of mammographic density, Breast Cancer Res 10: Yaffe M.J., Boone J.M., Packard N., Alonzo-Proulx O., Huang S.Y., Peressotti C.L., Al-Mayah A., Brock K. (2009), The myth of the breast, Med Phys 36: Alonzo-Proulx O., Jong R.A., Yaffe M.J. (2012), Volumetric breast density characteristics as determined from digital mammograms, Phys Med Biol 57: International Atomic Energy Agency (2007), "Dosimetry in diagnostic radiology: an internal code of practice", Technical Report Series no. 457, IAEA Vienna. 10. Bland J.M., Altman D.G. (1986), Statistical methods for assessing agreement between two methods of clinical measurements, Lancet 8: Page 20 of 20