Multiplex immunofluorescence (mIF) staining plays an important role in profiling biomarkers and allows investigation of co-relationships between multiple biomarkers in the same tissue section. The Cell DIVE mIF platform (Leica Microsystems) employs an alkaline solution of hydrogen peroxide as a fluorophore inactivation reagent in the sequential staining, imaging, and bleaching protocol for use on FFPE sections. Suboptimal bleaching efficiency, degradation of tissue structure, and loss of antigen immunogenicity occasionally are encountered with the standard bleaching process. To overcome these impediments, we adopted a modified photochemical bleaching method, which utilizes an intense LED light exposure concurrent with the application of hydrogen peroxide. Repeated stain/bleach rounds with different antibodies were performed on breast tissue and other tissue sections. Residual signal after conventional bleaching and the modified technique were compared and tissue integrity and antigen immunogenicity were assessed. The modified technique effectively eliminates fluorescence signal from previous staining rounds and produces consistent results for multiple rounds of staining and imaging. With the modified method, photochemical treatments did not destroy tissue sub-cellular contents, and the tissue antigenicity was well preserved during the entire mIF process. Overall processing time was reduced from 36 to 30 hours in an mIF procedure with 8 rounds. With the conventional method, tissue quality was highly degraded after 8 rounds. The new technique allows reduced turn-around time, provides reliable fluorophore removal in mIF with excellent maintenance of tissue integrity, facilitating studies of the co-localization of multiple biomarkers in tissues of interest.

PURPOSE:A comprehensive, centrally-monitored physics quality control (QC) program was developed for the Tomosynthesis Imaging Screening Trial (TMIST), a randomized controlled trial of digital breast tomosynthesis (TM) versus digital mammography (DM) for cancer screening. As part of the program, in addition to a set of phantom-based tests, de-identified data on image acquisition and processing parameters were captured from the DICOM headers of all individual patient images in the trial. These data were analyzed to assess the potential usefulness of header data from digital mammograms and tomosynthesis images of patients for quality assurance in breast imaging. METHODS:Data were automatically extracted from the headers of all de-identified patient mammograms and tomosynthesis images in the TMIST study. Image acquisition parameters and estimated radiation doses were tracked for individual sites, systems and across system types. These parameters included (among others) kV, target/filter use, number of acquired views per examination, AEC mode, compression thickness and force and detector temperature. Consistency of manually entered study data parameters (subject ID, screening time-point) from TMIST was evaluated. Preliminary observations from the program are presented. RESULTS:We report on data from 812 651 images from 135 525 examinations acquired between October, 2017 and December, 2022. Data came from 6 system models from 3 manufacturers. There was greater variability both in the number of views used and in the estimated (proxy) doses received in DM exams compared to TM. Mean proxy doses per examination varied among manufacturers from 2.76-4.54 mGy for DM and 3-4.84 mGy for the tomosynthesis component in the TM arm with maximum examination proxy doses of 20 and 26 mGy for DM and TM respectively. Mean proxy doses per examination for the combination examination in TM (tomosynthesis plus digital mammography) varied from 6.6 to 7.6 mGy among manufacturers with a maximum of 44.5 mGy. CONCLUSIONS:Overall, modern digital mammography and tomosynthesis systems used in TMIST have operated very reliably. Doses vary considerably due to variation in the number of views per examination, thickness and fibro-glandularity of the breast, and choices in the use of synthesized versus actual 2D mammography in the TM examination. These data may also be useful in predicting equipment problems. Header information is valuable not only for automated QC, but also for cross-checking accuracy and consistency of data in a clinical study.

BACKGROUND:The Tomosynthesis Mammography Imaging Screening Trial (TMIST), EA1151 conducted by the Eastern Cooperative Oncology Group (ECOG)/American College of Radiology Imaging Network (ACRIN) is a randomized clinical trial designed to assess the effectiveness for breast cancer screening of digital breast tomosynthesis (TM) compared to digital mammography (DM). Equipment from multiple vendors is being used in the study. PURPOSE:For the findings of the study to be valid and capture the true capacities of the two technology types, it is important that all equipment is operated within appropriate parameters with regard to image quality and dose. A harmonized QC program was established by a core physics team. Since there are over 120 trial sites, a centralized, automated QC program was chosen as the most practical design. This report presents results of the weekly QC testing program. A companion paper will review quality monitoring based on data from the headers of the patient images. METHODS:Study images are collected centrally after de-identification using the "TRIAD" application developed by ACR. The core physics team devised and implemented a minimal set of quality control (QC) tests to evaluate the tomosynthesis and 2D mammography systems. Weekly, monthly and annual testing is performed by the site mammography technologists with images submitted directly to the physics core. The weekly physics QC tests are described: SDNR of a low-contrast mass object, artifact spread, spatial resolution, tracking of technical factors, and in-slice noise power spectra. RESULTS:As of December 31, 2022 (5 years), 145 sites with 411 machines had submitted QC data. A total of 136 742 TMIST participant screening imaging studies had been performed. The 5th and 95th percentile mean glandular doses for a single tomosynthesis exposure to a 4.0 cm thick PMMA phantom ("standard breast phantom") were 1.24 and 1.68 mGy respectively. The largest sources of QC non-conformance were: operator error, not following the QC protocol exactly, unreported software updates and preventive maintenance activities that affected QC setpoints. Noise power spectra were measured, however, standardization of performance targets across machine types and software revisions was difficult. Nevertheless, for each machine type, test measurement results were very consistent when the protocol was followed. Deviations in test results were mostly related to software and hardware changes. CONCLUSION:Most systems performed very consistently. Although this is a harmonized program using identical phantoms and testing protocols, it is not appropriate to apply universal threshold or target metrics across the machine types because the systems have different non-linear reconstruction algorithms and image display filters. It was found to be more useful to assess pass/fail criteria in terms of relative deviations from baseline values established when a system is first characterized and after equipment is changed. Generally, systems which needed repair failed suddenly, but in retrospect, for a few cases, drops in SDNR and increases in mAs were observed prior to tube failure. TMIST is registered as NCT03233191 by Clinicaltrials.gov.

PURPOSEIn the reconstruction of volume breast images from x-ray projections in breast tomosynthesis, some tomographic systems truncate the image data presented to the radiologist such that a non-negligible amount of tissue may be missing from the breast image. QC tests were conducted to determine if this problem existed in imaging in the TMIST Study.METHODSTest tools developed for TMIST containing small objects at known heights were used in routine weekly and annual QC testing of tomosynthesis units to assess the degree to which phantom material that was irradiated in imaging was excluded from the reconstructed image. Results from 318 tests on five system types from three manufacturers are reported.RESULTSThe presence and extent of this problem varied among system types. The cause was most frequently related to machine errors in the determination of breast thickness or to deflection of components during breast compression. In particular, the problem occurred when a compression paddle other than the one calibrated for tomosynthesis was used for the tests. This was also verified to have occurred in some clinical imaging.CONCLUSIONSMissing volume can be avoided by intentionally reconstructing additional image slices above and below the presumed locations of the breast support and compression plate. A compression paddle which has been calibrated for tomosynthesis should be used both for clinical imaging and testing. The prevalence of this phenomenon suggests that more frequent testing for volume coverage may be advisable.

•Demographic and treatment characteristics were found to increase patients’ susceptibility to developing radiation dermatitis.•The administration of a boost was predictive of erythema, edema, and desquamation.•Predictors of erythema included a high dose fractionation or tissue volume irradiated.•Higher pain was reported among patients with a high body mass index or dose fractionation schedule.•The findings of this prospective study may inform future practice on the care and prevention of skin toxicities in high-risk patients.

PURPOSE:Radiation dermatitis (RD) is a side effect experienced by many patients undergoing radiotherapy (RT) for breast cancer. In the present study, the Edmonton Symptom Assessment System (ESAS), a validated patient-reported symptom screening tool, was used to determine the impacts of RT-induced skin outcomes on ESAS items. Patient- and treatment-related factors and skin treatments to manage RD symptoms, were assessed for association with ESAS scores. METHODS:Patient and treatment characteristics were collected retrospectively for breast cancer patients treated with adjuvant RT between December 2013 and November 2015. Prospective data was collected through clinician-reported surveys. Linear regression analyses were performed to detect the relationship between patient-reported ESAS scores and clinician-reported RD symptoms. RESULTS:A total of 857 patients were included in the analysis. Moderate to severe scores were commonly reported for fatigue (n = 412, 48%), wellbeing (n = 386, 45%) and anxiety (n = 266, 31%). Oral analgesic use was associated with ESAS fatigue, drowsiness, pain, nausea, lack of appetite, shortness of breath, and wellbeing (P < .05), while dressings were only associated with anxiety (P = .02). No RD symptoms were found to be significantly associated with any ESAS items. CONCLUSIONS:The ESAS accurately reflects symptoms of fatigue, anxiety, and wellbeing for breast cancer patients undergoing RT. Our study, however, found no association between ESAS scores and RD severity, which may reflect the shortcomings of the ESAS in assessing symptom burden. Further research is necessary to warrant the development of a new site-specific symptom screening tool for use in RT for breast cancer.

Purpose Patients are commonly enrolled into clinical trials. It has been reported that these patients may have better outcomes than those not enrolled into the study. The reasoning from these improvements could be attributable to closer follow-up, better patient adherence, more health aware patients have, or reaction to observation such as the Hawthorne effect. Methods Three hundred forty-six patients were approached for a prospective skin toxicity study in adjuvant breast cancer radiotherapy (RT) but declined participation between January 2018 and July 2019 (non-trial group). They were retrospectively reviewed and patient, treatment and RT-related characteristics, as well as the occurrence of skin reactions, and the usage of topical treatments were collected. This was compared with a comparison cohort of 349 patients who were enrolled into a previously conducted prospective study (trial group). Results More patients in the trial group had conventional RT (CFRT) versus hypofractionated RT (HFRT). Data was further stratified and there was no significant difference in moist desquamation, topical antibiotic usage, dressing application, home care use, topical corticoid steroid use, and oral analgesic use. There was a significantly lower pain score in the group compared with the non-trial group in both HFRT and CFRT (OR = 0.091 and OR = 0.348, p < 0.0001). Conclusion Our results demonstrated no differences other than pain between the trial group and non-trial group. Therefore, in this cohort, patients enrolled into an observational trial while undergoing adjuvant breast RT demonstrated similar experiences of skin reactions. There are challenges to assessing differences between these groups, as confounding is likely. Therefore, it is recommended for future studies to additionally assess the impact of study participation on outcomes such as quality of life, quality of care received, and/or anxiety levels.

Anthropomorphic breast phantoms are used to create images that mimic aspects of clinical breast images and are useful in optimization and characterization of breast imaging systems. Here, a full-sized compressed physical breast phantom is designed and manufactured with 100 m resolution, high reproducibility and x-ray properties similar to that of breast tissues. The phantom design is based on a digital model derived from the morphology and distribution of large, medium and small scale fibroglandular and inter-glandular adipose tissue observed in clinical breast computerized tomography (bCT) images. The physical phantom consists of four slabs of a polyamide-12 component that mimics adipose tissue fabricated using selective laser sintering (SLS). The fibroglandular component is a low viscosity resin doped with a small amount of zinc oxide nanoparticles (<110 nm) to increase attenuation. The phantom was imaged on a Senographe Pristina and compared to image simulations of the virtual phantom. The power spectral parameter, β was 3.8±0.2 and 3.9±0.5 for the physical and virtual phantoms in a digital mammogram. The corresponding Laplacian fractional entropy (LFE) averaged 0.22 and 0.14 across the range 0.125–1.29 mm-1. Very good texture cancellation was obtained in contrast-enhanced spectral mammography.

Anthropomorphic breast phantoms are useful for development and characterization of breast x-ray imaging systems. Rapid prototyping (RP) opens a new way for generating complex shapes similar to real breast tissue patterns at reasonably high resolution and a high degree of reproducibility. Such a phantom should have x-ray attenuation properties similar to adipose and fibroglandular tissue across a broad x-ray energy range. However material selection is limited to those that are compatible with the printing system, which often requires adding non-organic dopants. Fortunately, there are some off-the-shelf materials that may be suitable for breast phantoms. Here a polyamide-12/water texture phantom is being investigated, which can be used for mammography, tomosynthesis and breast CT. Polyamide-12 (PA-12) is shown to have linear attenuation coefficients across an energy range of 15 40 keV matching adipose tissue to within 10% effective breast density. A selective laser sintering (SLS) printer is used for manufacturing the phantom. The phantom was imaged on the Senographe Pristina (GE Healthcare, Chicago, IL), while initial assessment of 3D fidelity with the original design was performed by acquiring volume images of the phantom on a micro-CT system. A root mean distance error of 0.22 mm was seen between the micro-CT volume and the original. The PA-12 structures appeared to be slightly smaller than in the original, possibly due to infiltration of the water into the PA-12 surfaces. Power spectra measurements for mammograms of the simulated and physical phantoms both demonstrated an inverse power-law spectrum shape with exponent beta = 3.72 and 3.76, respectively.

The Tomosynthesis Mammography Imaging Screening Trial (TMIST) Lead-In Study is a randomized screening trial that aims to compare the performance of standard two-dimensional full-field digital mammography (FFDM) and tomosynthesis at five sites in Canada, with multiple vendors’ platforms and a target enrollment of 6300 women. To characterize and monitor the image quality of the tomosynthesis systems in the trial, a quality control (QC) program has been developed, including semi-annual physics tests, and daily tests performed on a phantom imaged by the radiographer. Here we describe the test regimen and phantoms and present initial results. The physics tests include measurement of image quality parameters in the reconstructed tomographic slices and evaluation of the AEC performance by measuring signal difference to noise ratio (SDNR) of a low contrast simulated lesion. The physics tests have been performed on a GE Senoclaire, two Hologic Selenia Dimensions and two Siemens Mammomat Inspiration units. In addition to the physics tests, we have remotely collected 15 months of daily QC data on the GE unit and 2 and 3 months on the Hologic units.

PURPOSE:To estimate the reliability of a reference standard two-dimensional area-based method and three automated volumetric breast density measurements by using repeated measures.MATERIALS AND METHODS:Thirty women undergoing screening mammography consented to undergo a repeated left craniocaudal examination performed by a second technologist in this prospective institutional review board-approved HIPAA-compliant study. Breast density was measured by using an area-based method (Cumulus ABD) and three automated volumetric methods (CumulusV [University of Toronto], Volpara [version 1.4.5; Volpara Solutions, Wellington, New Zealand), and Quantra [version 2.0; Hologic, Danbury, Conn]). Discrepancy between the first and second breast density measurements (Δ1-2) was obtained for each algorithm by subtracting the second measurement from the first. The Δ1-2 values of each algorithm were then analyzed with a random-effects model to derive Bland-Altman-type limits of measurement agreement.RESULTS:Variability was higher for Cumulus ABD and CumulusV than for Volpara or Quantra. The within-breast density measurement standard deviations were 3.32% (95% confidence interval [CI]: 2.65, 4.44), 3.59% (95% CI: 2.86, 4.48), 0.99% (95% CI: 0.79, 1.33), and 1.64% (95% CI: 1.31, 1.39) for Cumulus ABD, CumulusV, Volpara, and Quantra, respectively. Although the mean discrepancy between repeat breast density measurements was not significantly different from zero for any of the algorithms, larger absolute breast density discrepancy (Δ1-2) values were associated with larger breast density values for Cumulus ABD and CumulusV but not for Volpara and Quantra.CONCLUSION:Variability in a repeated measurement of breast density is lowest for Volpara and Quantra; these algorithms may be more suited to incorporation into a risk model.

PURPOSE:Current quality control protocols for digital mammography rely on subjective assessments of image quality or simple measures that are not comparable between vendor platforms. The noise-equivalent quanta (NEQ) can be expressed in units of image quanta (fluence) for the spatial frequency range of interest, enabling comparisons between systems and x-ray spectra. The purpose of this work is to explore use of a simple phantom to measure the components of the noise-equivalent quanta of digital mammography systems for use in routine quality control.METHODS:A simple phantom is imaged on six mammography systems from different vendors. The phantom contains uniform regions for measurement of noise power spectrum (NPS), slanted edges for measurement of modulation transfer function (MTF), and objects of various thicknesses for measurement of contrast. Images were acquired at a range of dose levels on each system to examine how measurements scale with dose, and multiple images were taken at a single dose point to examine measurement reproducibility.RESULTS:The phantom and measurement methods show good reproducibility, with average coefficient of variation values of less than or equal to 15% on all systems evaluated. Measured MTF and NPS values are comparable to other published results when the increase in scattered radiation generated by placing the phantom on the breast support is accounted for.CONCLUSIONS:Measurement of the parameters required to calculate NEQ from a single image of a simple phantom is practical, and shows promise as a method of evaluating image quality for routine quality control of digital mammography systems.

A reader study was conducted to tune the parameters of an observer model used to predict the detectability index (dʹ ) of test objects as a task-based quality control (QC) metric for digital mammography. A simple test phantom was imaged to measure the model parameters, namely, noise power spectrum,modulation transfer function and test-object contrast. These are then used ina non-prewhitening observer model, incorporating an eye-filter and internal noise, to predict dʹ. The model was tuned by measuring dʹ of discs in a four-alternative forced choice reader study. For each disc diameter, dʹ was used to estimate the threshold thicknesses for detectability. Data were obtained for six types of digital mammography systems using varying detector technologies and x-ray spectra. A strong correlation was found between measured and modeled values of dʹ, with Pearson correlation coefficient of 0.96. Repeated measurements from separate images of the test phantom show an average coefficient of variation in dʹ for different systems between 0.07 and 0.10. Standard deviations in the threshold thickness ranged between 0.001 and 0.017 mm. The model is robust and the results are relatively system independent, suggesting that observer model dʹ shows promise as a cross platform QC metric for digital mammography.

Background: Evidence from animal models shows that tissue stiffness increases the invasion and progression of cancers, including mammary cancer. We here use measurements of the volume and the projected area of the compressed breast during mammography to derive estimates of breast tissue stiffness and examine the relationship of stiffness to risk of breast cancer.Methods: Mammograms were used to measure the volume and projected areas of total and radiologically dense breast tissue in the unaffected breasts of 362 women with newly diagnosed breast cancer (cases) and 656 women of the same age who did not have breast cancer (controls). Measures of breast tissue volume and the projected area of the compressed breast during mammography were used to calculate the deformation of the breast during compression and, with the recorded compression force, to estimate the stiffness of breast tissue. Stiffness was compared in cases and controls, and associations with breast cancer risk examined after adjustment for other risk factors.Results: After adjustment for percent mammographic density by area measurements, and other risk factors, our estimate of breast tissue stiffness was significantly associated with breast cancer (odds ratio = 1.21, 95% confidence interval = 1.03, 1.43, p = 0.02) and improved breast cancer risk prediction in models with percent mammographic density, by both area and volume measurements.Conclusion: An estimate of breast tissue stiffness was associated with breast cancer risk and improved risk prediction based on mammographic measures and other risk factors. Stiffness may provide an additional mechanism by which breast tissue composition is associated with risk of breast cancer and merits examination using more direct methods of measurement.

Accurate and automatic segmentation of the pectoralis muscle is essential in many breast image processing procedures, for example, in the computation of volumetric breast density from digital mammograms. Its segmentation is a difficult task due to the heterogeneity of the region, neighborhood complexities, and shape variability. The segmentation is achieved by pixel classification through a Markov random field (MRF) image model. Using the image intensity feature as observable data and local spatial information as a priori, the posterior distribution is estimated in a stochastic process. With a variable potential component in the energy function, by the maximum a posteriori (MAP) estimate of the labeling image, given the image intensity feature which is assumed to follow a Gaussian distribution, we achieved convergence properties in an appropriate sense by Metropolis sampling the posterior distribution of the selected energy function. By proposing an adjustable spatial constraint, the MRF-MAP model is able to embody the shape requirement and provide the required flexibility for the model parameter fitting process. We demonstrate that accurate and robust segmentation can be achieved for the curving-triangle-shaped pectoralis muscle in the medio-lateral-oblique (MLO) view, and the semiellipticshaped muscle in cranio-caudal (CC) view digital mammograms. The applicable mammograms can be either "For Processing" or "For Presentation" image formats. The algorithm was developed using 56 MLO-view and 79 CC-view FFDM "For Processing" images, and quantitatively evaluated against a random selection of 122 MLO-view and 173 CC-view FFDM images of both presentation intent types.

Tiled images are sometimes obtained for women with large breasts, which is a limitation of receptor size. In this retrospective HIPAA compliant study, automated breast density measurements for tiled images are compared with full MLO and CC views. Women with tiled views between July and December 2007 followed by full views within 15 months were included. Volumetric breast density (VBD) for tiled MLO views had very good correlation with full views (r = 0.88), while correlation between tiled and full CC views was poor (r = 0.31). VBD for all women requiring tiled CC views was low (<10%). In conclusion, VBD measured from a tiled MLO view is a reasonable substitute for a full MLO measure. Attributable risk of breast density for women requiring tiled CC views may be sufficiently low compared other factors such as high body mass index.

PURPOSE:To compare physical measures pertaining to image quality among digital mammography systems utilized in a large breast screening program. To examine qualitatively differences in these measures and differences in clinical cancer detection rates between CR and DR among sites within that program. METHODS:As part of the routine quality assurance program for screening, field measurements are made of several variables considered to correlate with the diagnostic quality of medical images including: modulation transfer function, noise equivalent quanta, d' (an index of lesion detectability) and air kerma to allow estimation of mean glandular dose. In addition, images of the mammography accreditation phantom are evaluated. RESULTS:It was found that overall there were marked differences between the performance measures of DR and CR mammography systems. In particular, the modulation transfer functions obtained with the DR systems were found to be higher, even for larger detector element sizes. Similarly, the noise equivalent quanta, d', and the phantom scores were higher, while the failure rates associated with low signal-to-noise ratio and high dose were lower with DR. These results were consistent with previous findings in the authors' program that the breast cancer detection rates at sites employing CR technology were, on average, 30.6% lower than those that used DR mammography. CONCLUSIONS:While the clinical study was not large enough to allow a statistically powered system-by-system assessment of cancer detection accuracy, the physical measures expressing spatial resolution, and signal-to-noise ratio are consistent with the published finding that sites employing CR systems had lower cancer detection rates than those using DR systems for screening mammography.

Quality control for digital mammography should be objective, reproducible and applicable across different manufacturers' systems and technologies. Ideally it should be possible to set clearly defined thresholds of acceptable behaviour that can be universally applied. Other works have proposed combining measurements of detector performance with an observer model and task function to calculate the detectability index d′. This work builds on those concepts by proposing a simple phantom design for measuring system performance from a single image, allowing the calculation of NEQ and d′ and including effects due to scatter and all noise sources. A second contrast-detail test-object is proposed for validation of the model using a 4AFC observer study design.