KEYWORDS: Digital breast tomosynthesis, Breast, Visibility, Breast density, Cancer detection, Cancer, Breast cancer, Magnetic resonance imaging, X-ray imaging, Ultrasonography
Breast cancer (BC) detectability depends on many factors: the type of cancer, breast tissue related factors, the choice and use of technology and human factors. New imaging techniques should provide higher accuracy and less false negatives. To tailor any future virtual imaging trial (VIT), a detailed description of invasive BC lesions was undertaken. In this single-institution retrospective study, imaging characteristics of 100 consecutive invasive BCs diagnosed in our hospital were assessed in terms of a visibility score, BI-RADS descriptors, breast density, lesion size and location on all breast x-ray imaging techniques and ultrasound (US). Seventy-seven out of these 100 invasive BCs were diagnosed using DBT in addition to FFDM and US and in 29 cases MRI was performed. Not all imaging modalities are equally well performing regarding visualization of invasive BC; 29 out of 77 lesions were poorly visible on FFDM, 9 on DBT, 34 on SM, and 11 on US. Four lesions were poorly visible on all these modalities, but fortunately clearly visible on MRI. The studied invasive lesions that are well visible on all modalities are mostly irregular spiculated lesions with a high density, and have in this study a median size of 18mm. The poorly visible lesions are also mostly irregularly shaped, show more variations in their margins and have a smaller median size of 12.5mm. They are equally or highly dense compared to the background tissue and are in general present in slightly denser breasts. Two lesions are not visible on mammography due to the peripheral location of the invasive breast cancer, one was located sternal and one very peripheral in the axillary tail. Both lesions were visible on ultrasound. This database provides detailed information on the imaging characteristics of invasive BCs which could be a valuable input for VITs.
KEYWORDS: Kidney, Mammography, Arteries, Inflammation, Breast, Detection and tracking algorithms, Chemical vapor deposition, Breast cancer, Digital mammography, Mode conditioning cables
Breast artery calcification (BAC) is increasingly recognized as a specific marker of medial calcification and may help to identify risk factors of medial artery calcification. Amongst these are high age, diabetes mellitus, hypertension and chronic kidney disease (CKD). Present retrospective observational cohort study focused on the latter patient group with CKD and aimed to define the prevalence and progression rate of BAC in chronic kidney disease (CKD) patients across stages of disease, to define clinical and biochemical correlates of BAC and to explore the association of BAC with incident cardiovascular morbidity and mortality. The main findings of the present observational study are as follows: (a) BAC is common in CKD and its prevalence, severity and rate of progression increase parallel to the degree of kidney dysfunction; (b) inflammation and hyperphosphatemia are (nontraditional) risk factors for BAC in CKD patients; and (c) BAC associates with a dismal cardiovascular outcome in renal transplant recipients. In conclusion, BAC is common among CKD patients, progresses at a slower pace in Tx patients as compared to CKD5D patients, and associates with dismal cardiovascular outcomes. BAC score, kidney function and serum phosphate at baseline seem to be important determinants of progression. BAC is not routinely mentioned in mammogram reports, while the measurement of BAC may offer a personalized, non-invasive approach to risk-stratify CKD patients for cardiovascular disease at no additional cost or radiation since a majority of women over the age of 40 undergo regular breast cancer screening.
Objectives: To assess the accuracy of size measurements of spiculated masses for both 2D mammography (MX) and Digital Breast Tomosynthesis (DBT) in comparison to histopathologic outcomes in breast cancer patients. Methods: A retrospective study was conducted from January 2015 till December 2016 with inclusion of biopsy proven breast cancer patients who presented themselves with masses with spicules. Two readers performed size measurements of both the masses only as well as of the masses with spicules. Results were compared to histopathological results. Results: A total of 180 spiculated masses were included in this study. Analysis showed that for the mass only measurements both readers showed a significant underestimation on both 2D Mx as well as DBT versus histopathology. While the mass with spicules measurements showed more variable results, partly depending on tumor size and clinical working experience. We found no influence of viewing direction or breast density. Conclusions: Even with DBT it is very difficult to determine the correct size of spiculated mases. Measurements of only the central mass are not correlating well with histopathology. Especially for larger masses (> 25mm), radiologists tend to underestimate the size of spiculated masses. For smaller masses measurement of the mass center only as well as inclusion of the spicules correlated well with pathology sizes.
A number of different physical and digital anthropomorphic breast phantoms have been proposed to assess and optimize the performance of breast x-ray imaging systems. All mimic, to some extent, different characteristics of the breast but a systematic realism of phantom realism applied to a number of phantoms using human readers has not been performed, for either full field digital mammography (FFDM), or digital breast tomosynthesis (DBT). We present a reader study in which radiologists performed a subjective evaluation of the visual realism between a selected group of available software phantoms (Stochastic Solid Breast Texture (SSBT) and power law noise texture), physical phantoms (CIRS BR3D breast imaging phantom and the L1 phantom) and clinical mammography images. Regions of interest (ROIs) of 2×2 cm2 and 2×2×3 cm3 , for FFDM and DBT stacks respectively, were scored. The readers were asked to judge how well the ROIs represented real breast texture using a 5-point rating scale. Observer ratings were analysed using the receiver operating characteristic (ROC) methodology and the area under the ROC curve (AUC) was used as the figure-of-merit (FOM). The Mann-Whitney test was used to assess the differences between separate groups. For the question of breast texture realism, the SSBT and power-law noise texture images obtained a high score. For DBT, SSBT was also found to have a high visual realism while the power-law noise texture images were found to have mediocre visual realism.
Automation of systematic scoring of breast glandularity on CT thorax examinations performed for another clinical reason could aid in detecting postmenopausal women with increased breast cancer risk. We propose a novel method that combines automated deep learning based breast segmentation from CT thorax examinations with computation of breast glandularity based on radiodensity and volumetric breast density. Reasonable segmentation Dice scores were found as well as very strong correlation between the risk measures computed on the ground truth and with the proposed approach. Hence, the proposed method can offer reliable breast cancer risk measures with limited additional workload for the radiologist.
The purpose of the study is to test the performance of the combination of digital breast tomosynthesis (DBT) and synthetic views on the detection for cancers presenting as calcifications compared to the performance of planar mammography combined with DBT. A pilot study is presented. A set of 22 cases without cancer were collected from a Siemens Inspiration mammography system. Twenty-two simulated calcification clusters were inserted into the planar and DBT projections of 16 cases. For each case one breast and one view were used. The images were processed using Siemens proprietary software. Seven experienced mammography readers viewed the cases in three study arms: planar alone (ArmP), planar with DBT (ArmP&D) and synthetic 2D with DBT (ArmS&D). The observers marked the suspected location of the clusters and classified the likelihood of there being a suspicious calcification clusters for each case. A JAFROC figure of merit (FoM) was calculated for each study arm. The detection fractions of all cases were 46±16% (P and P&D), 34±19% (S&D). For lesion marked for recall then the maximum detection rate was 19%. The FoMs were 0.48±0.15 (P) and 0.42±0.17 (P&D), but significantly lower (p≤0.003) for S&D (0.32±0.16). This pilot study demonstrated the feasibility of undertaking a larger study. The overall detection were lower (<50%) than optimal for a virtual clinical trial. We plan to increase the detection rate by using less subtle clusters in the final study. When using synthetic 2D images instead of planar images alongside DBT, the FoM was lower for subtle calcification clusters.
This work investigated the effect of the grid-less acquisition mode with scatter correction software developed by Siemens Healthcare (PRIME mode) on image quality and mean glandular dose (MGD) in a comparative study against a standard mammography system with grid. Image quality was technically quantified with contrast-detail (c-d) analysis and by calculating detectability indices (d’) using a non-prewhitening with eye filter model observer (NPWE). MGD was estimated technically using slabs of PMMA and clinically on a set of 11439 patient images. The c-d analysis gave similar results for all mammographic systems examined, although the d’ values were slightly lower for the system with PRIME mode when compared to the same system in standard mode (-2.8% to -5.7%, depending on the PMMA thickness). The MGD values corresponding to the PMMA measurements with automatic exposure control indicated a dose reduction from 11.0% to 20.8% for the system with PRIME mode compared to the same system without PRIME mode. The largest dose reductions corresponded to the thinnest PMMA thicknesses. The results from the clinical dosimetry study showed an overall population-averaged dose reduction of 11.6% (up to 27.7% for thinner breasts) for PRIME mode compared to standard mode for breast thicknesses from 20 to 69 mm. These technical image quality measures were then supported using a clinically oriented study whereby simulated clusters of microcalcifications and masses were inserted into patient images and read by radiologists in an AFROC study to quantify their detectability. In line with the technical investigation, no significant difference was found between the two imaging modes (p-value 0.95).
The purpose of the study is to evaluate the performance of different image processing algorithms in terms of
representation of microcalcification clusters in digital mammograms.
Clusters were simulated in clinical raw ("for processing") images. The entire dataset of images consisted of 200 normal
mammograms, selected out of our clinical routine cases and acquired with a Siemens Novation DR system. In 100 of the
normal images a total of 142 clusters were simulated; the remaining 100 normal mammograms served as true negative
input cases. Both abnormal and normal images were processed with 5 commercially available processing algorithms:
Siemens OpView1 and Siemens OpView2, Agfa Musica1, Sectra Mamea AB Sigmoid and IMS Raffaello Mammo 1.2.
Five observers were asked to locate and score the cluster(s) in each image, by means of dedicated software tool.
Observer performance was assessed using the JAFROC Figure of Merit. FROC curves, fitted using the IDCA method,
have also been calculated.
JAFROC analysis revealed significant differences among the image processing algorithms in the detection of
microcalcifications clusters (p=0.0000369). Calculated average Figures of Merit are: 0.758 for Siemens OpView2, 0.747
for IMS Processing 1.2, 0.736 for Agfa Musica1 processing, 0.706 for Sectra Mamea AB Sigmoid processing and 0.703
for Siemens OpView1.
This study is a first step towards a quantitative assessment of image processing in terms of cluster detection in clinical
mammograms. Although we showed a significant difference among the image processing algorithms, this method does
not on its own allow for a global performance ranking of the investigated algorithms.
Purpose:
1/ To validate a method for simulating microcalcifications in mammography
2/ To evaluate the effect of anatomical background on visibility of (simulated) microcalcifications
Materials and methods:
Microcalcifications were extracted from the raw data of specimen from a stereotactic vacuum needle biopsy. The sizes
of the templates varied from 200 μm to 1350μm and the peak contrast from 1.3% to 24%. Experienced breast imaging
radiologists were asked to blindly evaluate images containing real and simulated lesions. Analysis was done using ROC
methodology.
The simulated lesions have been used for the creation of composite image datasets: 408 microcalcifications were
simulated into 161 ROI's of 59 digital mammograms, having different anatomical backgrounds. Nine radiologists were
asked to detect and rate them under conditions of free-search. A modified receiver operating characteristic study
(FROC) was applied to find correlations between detectability and anatomical background.
Results:
1/ The calculated area under the ROC curve, Az, was 0.52± 0.04. Simulated microcalcifications could not be
distinguished from real ones.
2/ In the anatomical background classified as Category 1 (fatty), the detection fraction is the lowest (0.48), while for
type 2,3,4 there is a gradually decrease (from 0.61 to 0.54) as the glandularity increases. The number of false positives is
the highest for the background Category 1 (24%), compared to the other three types (16%). A 80% detectability is
found for microcalcifications with a diameter > 400μm and a peak contrast >10%. Anatomic noise seems to limit
detectability of large low contrast lesions, having a diameter >700μm.
A database of raw composite mammograms containing simulated microcalcifications was generated. Databases can be used for technology assessment, quality assurance and comparison of different processing algorithms or different visualization modalities in digital mammography. Clinical mammograms were selected and fully documented for this scope. Microcalcifications were simulated in mammography images following a methodology developed and validated in an earlier work of our group. To create microcalcification templates, specimen containing lesions with different morphology types were acquired. From a basic set of (ideal) microcalcification templates, a set of specific templates for the systems under study was generated. The necessary input to do so is the system MTF and attenuation values of aluminum sheets with different thickness. In order to make the whole process less time consuming and applicable on a large scale, dedicated software tools for the creation of composite images have been developed. Automatic analysis of scores from observer performance study, in terms of microcalcification detectability on the composite images, is also implemented. We report on the functionalities foreseen in these new software tools. Simulated microcalcifications were successfully created and inserted in raw images of the Siemens Novation DR, the AGFA DM1000 and the AGFA CR MM2.0.
The purpose of this study is to describe a method that allows the calculation of a contrast-detail curve for a particular
system configuration using simulated micro calcifications into clinical mammograms.
We made use of simulated templates of micro calcifications and adjusted their x-ray transmission coefficients and
resolution to the properties of the mammographic system under consideration (4). We expressed the thickness of the
simulated micro calcifications in terms of Al equivalence.
In a first step we validated that the thickness of very small Al particles with well known size and thickness can be
calculated from their x-ray transmission characteristics at a particular X-ray beam energy.
Then, micro calcifications with equivalent diameters in the plane of the detector ranging from 300 to 800 μm and
thicknesses, expressed in Al equivalent, covering 77 to 800 μm were simulated into the raw data of real clinical images.
The procedure was tested on 2 system configurations: the GE Senographe 2000 D and the Se based Agfa Embrace
DM1000 system. We adapted the X-ray transmissions and spatial characteristics of the simulated micro calcifications
such that the same physical micro calcification could be simulated into images with the specific exposure parameters
(Senographe 2000D: 28 kVp-Rh/Rh, Embrace DM1000: 28 kVp-Mo/Rh), compressed breast thickness (42+/-5mm) and
detector under consideration. After processing and printing, 3 observers scored the visibility of the micro calcifications.
We derived contrast-detail curves. This psychophysical method allows to summarize the performance of a digital
mammography detector including processing and visualization.
We evaluated the visibility of simulated subtle microcalcifications in real digital mammograms acquired with a flat-panel system (GE) and a CR system (Fuji). Ideal templates of microcalcifications were created, based on the attenuation characteristics of subtle microcalcifications from biopsied specimen in magnified images. X-ray transmission coefficients were expressed in Al-equivalent thickness. In this way, the X-ray transmission of a particular lesion could be re-calculated for other X-ray beams, different mammography systems and for different breast thickness. Extra corrections for differences in spatial resolution were based on the pre-sampled MTF. Zero to 10 simulated microcalcifications were randomly distributed in square frames. These software phantoms were then inserted in sets of raw mammograms of the modalities under study. The composed images were compressed, processed and printed as in clinical routine. Two experienced radiologists indicated the locations of the microcalcifications and rated their detection confidence. It is possible to assess the visibility of 'well controlled’ microcalcifications in digital clinical mammograms. Microcalcifications were better visible in the CR images than in the flat panel images. This psychophysical method comes close to the radiologists’ practice. It allows fpr including processing and visualization in the analysis and was well appreciated by our radiologists.
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