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OBJECTIVES: This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT. METHODS: A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images. RESULTS: For calcification clusters, there were no significant differences (p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images. CONCLUSIONS: This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses. KEY POINTS: • The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used. ispartof: EUROPEAN RADIOLOGY vol:32 issue:2 pages:806-814 ispartof: location:Germany status: published

Funder: Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601, Funder: American Cancer Society NHPDCSGBR-GBRLONG Policy Research Unit in Cancer Awareness, Screening and early Diagnosis, PR-PRU-1217-21601, BACKGROUND: This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers. METHODS: This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls. RESULTS: FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001). CONCLUSION: FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.

Objectives: To record the radiation doses involved in UK breast screening and to identify any changes since previous publications related to technical factors and the population screened. Methods: Mammographic exposure factors for 68,998 women imaged using 411 X-ray sets spread across the UK were compiled. Local output and half value layer measurements for each X-ray set were used to estimate mean glandular dose (MGD) using the standard UK method. Results: Mean MGDs in digital mammography have increased by 11% since 2010–12 for both medio-lateral oblique (MLO) and cranio-caudal (CC) views. The mean compressed breast thickness (CBT) has increased (4.8% CC, 5.2% MLO) over the same period. The mean MLO CBT value of 62.4 ± 0.1 mm is outside the 50 to 60 mm range used for diagnostic reference levels. The increase in MGD is consistent with the CBT changes. The mean MGD in the 50 to 60 mm CBT range is 1.44 ± 0.03 mGy for MLO views. CBT varies with age and peaks at 51. Conclusions: Mean CBT has increased with time, and this has increased mean MGDs for digital mammography. CBT also varies with age. Advances in knowledge: Updated average MGDs in the UK are provided. There is evidence that breast size is increasing in the UK and that mean CBT is affected by age-related changes in the breast.

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening. An artificial intelligence (AI) system performs as well as or better than radiologists at detecting breast cancer from mammograms, and using a combination of AI and human inputs could help to improve screening efficiency.

PURPOSE The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. METHODS Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53 mm were created. Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29 kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. RESULTS The threshold diameters of the lesions were 6.2, 4.9, and 6.7 mm (masses) and 225, 370, and 399 μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill-defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p = 0.059) and 18% (p = 0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p

This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT.A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images.For calcification clusters, there were no significant differences (p0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images.This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DMDBT and SMDBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses.• The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used.

Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. In mammography, measurement of breast density, dose estimation, and differentiation between cysts and solid tumours are example applications requiring accurate data on tissue attenuation. Published attenuation data are, however, sparse and cover a relatively wide range. To supplement available data we have previously measured the attenuation of cyst fluid and solid lesions using photon-counting spectral mammography. The present study aims to measure the attenuation of normal adipose and glandular tissue, and to measure the effect of formalin fixation, a major uncertainty in published data. A total of 27 tumour specimens, 7 fibro-glandular tissue specimens, and 15 adipose tissue specimens were included. Spectral (energy-resolved) images of the samples were acquired and the image signal was mapped to equivalent thicknesses of two known reference materials, from which x-ray attenuation as a function of energy can be derived. The spread in attenuation between samples was relatively large, partly because of natural variation. The variation of malignant and glandular tissue was similar, whereas that of adipose tissue was lower. Formalin fixation slightly altered the attenuation of malignant and glandular tissue, whereas the attenuation of adipose tissue was not significantly affected. The difference in attenuation between fresh tumour tissue and cyst fluid was smaller than has previously been measured for fixed tissue, but the difference was still significant and discrimination of these two tissue types is still possible. The difference between glandular and malignant tissue was close-to significant; it is reasonable to expect a significant difference with a larger set of samples. We believe that our studies have contributed to lower the overall uncertainty of breast tissue attenuation in the literature due to the relatively large sample sets, the novel measurement method, and by clarifying the difference between fresh and fixed tissue. QC 20210108

Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. For instance, techniques to better characterize cysts at mammography screening would be highly desirable to reduce recalls, but the development is hampered by the lack of attenuation data for cysts. We have developed a method to measure x-ray attenuation of tissue samples using a prototype photon-counting spectral mammography unit. The method was applied to measure the attenuation of 50 samples of breast cyst fluid and 50 samples of water. Spectral (energy-resolved) images of the samples were acquired and the image signal was mapped to equivalent thicknesses of two known reference materials, which can be used to derive the x-ray attenuation as a function of energy. The attenuation of cyst fluid was found to be significantly different from water. There was a relatively large natural spread between different samples of cyst fluid, whereas the homogeneity of each individual sample was found to be good; the variation within samples did not reach above the quantum noise floor. The spectral method proved stable between several measurements on the same sample. Further, chemical analysis and elemental attenuation calculation were used to validate the spectral measurement on a subset of the samples. The two methods agreed within the precision of the elemental attenuation calculation over the mammographic energy range., Comment: arXiv admin note: substantial text overlap with arXiv:2101.02449

The development of new x-ray imaging techniques often requires prior knowledge of tissue attenuation, but the sources of such information are sparse. We have measured the attenuation of adipose breast tissue using spectral imaging, in vitro and in vivo. For the in-vitro measurement, fixed samples of adipose breast tissue were imaged on a spectral mammography system, and the energy-dependent x-ray attenuation was measured in terms of equivalent thicknesses of aluminum and poly-methyl methacrylate (PMMA). For the in-vivo measurement, a similar procedure was applied on a number of spectral screening mammograms. The results of the two measurements agreed well and were consistent with published attenuation data and with measurements on tissue-equivalent material., Comment: arXiv admin note: text overlap with arXiv:2101.02449

Purpose to develop a channelized model observer (CHO) that matches human reader (HR) scoring of a physical phantom containing breast simulating structure and mass lesion-like targets for use in quality control of digital breast tomosynthesis (DBT) imaging systems. Methods A total of 108 DBT scans of the phantom was acquired using a Siemens Inspiration DBT system. The detectability of mass-like targets was evaluated by human readers using a 4-alternative forced choice (4-AFC) method. The percentage correct (PC) values were then used as the benchmark for CHO tuning, again using a 4-AFC method. Three different channel functions were considered: Gabor, Laguerre-Gauss and Difference of Gaussian. With regard to the observer template, various methods for generating the expected signal were studied along with the influence of the number of training images used to form the covariance matrix for the observer template. Impact of bias in the training process on the observer template was evaluated next, as well as HR and CHO reproducibility. Results HR performance was most closely matched by 8 Gabor channels with tuned phase, orientation and frequency, using an observer template generated from training image data. Just 24 DBT image stacks were required to give robust CHO performance with 0% bias, although a bias of up to 33% in the training images also gave acceptable performance. CHO and HR reproducibility were similar (on average 3.2 PC versus 3.4 PC). Conclusions The CHO algorithm developed matches human reader performance and is therefore a promising candidate for automated readout of phantom studies.