Breast density prediction from low and standard dose mammograms using deep learning: effect of image resolution and model training approach on prediction quality.

Purpose. To improve breast cancer risk prediction for young women, we have developed deep learning methods to estimate mammographic density from low dose mammograms taken at approximately 1/10th of the usual dose. We investigate the quality and reliability of the density scores produced on low dose mammograms focussing on how image resolution and levels of training affect the low dose predictions.Methods. Deep learning models are developed and tested, with two feature extraction methods and an end-to-end trained method, on five different resolutions of 15,290 standard dose and simulated low dose mammograms with known labels. The models are further tested on a dataset with 296 matching standard and real low dose images allowing performance on the low dose images to be ascertained.Results. Prediction quality on standard and simulated low dose images compared to labels is similar for all equivalent model training and image resolution versions. Increasing resolution results in improved performance of both feature extraction methods for standard and simulated low dose images, while the trained models show high performance across the resolutions. For the trained models the Spearman rank correlation coefficient between predictions of standard and low dose images at low resolution is 0.951 (0.937 to 0.960) and at the highest resolution 0.956 (0.942 to 0.965). If pairs of model predictions are averaged, similarity increases.Conclusions. Deep learning mammographic density predictions on low dose mammograms are highly correlated with standard dose equivalents for feature extraction and end-to-end approaches across multiple image resolutions. Deep learning models can reliably make high quality mammographic density predictions on low dose mammograms.

Radiation doses received in the UK breast screening programmes 2019-2023.

OBJECTIVE: To report the latest UK mammography dose survey results and to compare radiation doses from digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) in UK breast screening. METHODS: Anonymized exposure factors were collected for 111 152 screening cases and 5113 assessment cases from 405 x-ray sets across the United Kingdom using an online submission system linked to a national database of mammography quality control data. Output and beam quality measurements from each set were combined with exposure data to estimate mean glandular doses (MGD). RESULTS: FFDM doses increased by ∼10% compared to the 2016-2019 national survey but compressed breast thicknesses (CBT) remained similar. DBT doses were 34%-40% higher than FFDM overall and 34% higher than FFDM for breasts 50-60 mm thick. We found a possible overestimation of PMMA breast equivalent thicknesses at low CBTs, but the evidence was not conclusive. CONCLUSION: Recent changes to the mix of x-ray models in use in UK breast screening have resulted in higher FFDM breast doses. DBT doses in the NHSBSP are on average higher than FFDM by ∼34%-40%. ADVANCES IN KNOWLEDGE: This is the first national study to report DBT and FFDM MGDs in UK breast screening.

Special Section Guest Editorial: Introduction to the JMI Special Issue on Advances in Breast Imaging.

The editorial introduces the JMI Special Issue on Advances in Breast Imaging.

Successes and challenges in extracting information from DICOM image databases for audit and research.

In radiography, much valuable associated data (metadata) is generated during image acquisition. The current setup of picture archive and communication systems (PACS) can make extraction of this metadata difficult, especially as it is typically stored with the image. The aim of this work is to examine the current challenges in extracting image metadata and to discuss the potential benefits of using this rich information. This work focuses on breast screening, though the conclusions are applicable to other modalities.The data stored in PACS contain information, currently underutilised, and is of great benefit for auditing and improving imaging and radiographic practice. From the literature, we present examples of the potential clinical benefit such as audits of dose, and radiographic practice, as well as more advanced research highlighting the effects of radiographic practice, e.g. cancer detection rates affected by imaging technology.This review considers the challenges in extracting data, namely,• The search tools for data on most PACS are inadequate being both time-consuming and limited in elements that can be searched.• Security and information governance considerations• Anonymisation of data if required• Data curationThe review describes some solutions that have been successfully implemented.• Retrospective extraction: direct query on PACS• Extracting data prospectively• Use of structured reports• Use of trusted research environmentsUltimately, the data access process will be made easier by inclusion during PACS procurement. Auditing data from PACS can be used to improve quality of imaging and workflow, all of which will be a clinical benefit to patients.

Lessons learned from independent external validation of an AI tool to detect breast cancer using a representative UK data set.

OBJECTIVE: To pilot a process for the independent external validation of an artificial intelligence (AI) tool to detect breast cancer using data from the NHS breast screening programme (NHSBSP). METHODS: A representative data set of mammography images from 26,000 women attending 2 NHS screening centres, and an enriched data set of 2054 positive cases were used from the OPTIMAM image database. The use case of the AI tool was the replacement of the first or second human reader. The performance of the AI tool was compared to that of human readers in the NHSBSP. RESULTS: Recommendations for future external validations of AI tools to detect breast cancer are provided. The tool recalled different breast cancers to the human readers. This study showed the importance of testing AI tools on all types of cases (including non-standard) and the clarity of any warning messages. The acceptable difference in sensitivity and specificity between the AI tool and human readers should be determined. Any information vital for the clinical application should be a required output for the AI tool. It is recommended that the interaction of radiologists with the AI tool, and the effect of the AI tool on arbitration be investigated prior to clinical use. CONCLUSION: This pilot demonstrated several lessons for future independent external validation of AI tools for breast cancer detection. ADVANCES IN KNOWLEDGE: Knowledge has been gained towards best practice procedures for performing independent external validations of AI tools for the detection of breast cancer using data from the NHS Breast Screening Programme.

Development of an algorithm to convert mammographic images to appear as if acquired with different technique factors.

Purpose: We set out a fully developed algorithm for adapting mammography images to appear as if acquired using different technique factors by changing the signal and noise within the images. The algorithm accounts for difference between the absorption by the object being imaged and the imaging system. Approach: Images were acquired using a Hologic Selenia Dimensions x-ray unit for the validation, of three thicknesses of polymethyl methacrylate (PMMA) blocks with or without different thicknesses of PMMA contrast objects acquired for a range of technique factors. One set of images was then adapted to appear the same as a target image acquired with a higher or lower tube voltage and/or a different anode/filter combination. The average linearized pixel value, normalized noise power spectra (NNPS), and standard deviation of the flat field images and the contrast-to-noise ratio (CNR) of the contrast object images were calculated for the simulated and target images. A simulation study tested the algorithm on images created using a voxel breast phantom at different technique factors and the images compared using local signal level, variance, and power spectra. Results: The average pixel value, NNPS, and standard deviation for the simulated and target images were found to be within 9%. The CNRs of the simulated and target images were found to be within 5% of each other. The differences between the target and simulated images of the voxel phantom were similar to those of the natural variability. Conclusions: We demonstrated that images can be successfully adapted to appear as if acquired using different technique factors. Using this conversion algorithm, it may be possible to examine the effect of tube voltage and anode/filter combination on cancer detection using clinical images.

Radiation doses in the United Kingdom breast screening programmes 2016-2019.

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.

The relationship between age of digital mammography systems and number of reported faults and downtime.

PURPOSE: To investigate the relationship between age of mammographic x-ray equipment, and number of reported faults and related consequences. METHODS: A centralised online fault reporting database is used by all UK breast screening programmes to collate faults with mammography equipment. Data on faults occurring in 2018 and 2019 for digital x-ray imaging systems were analysed. The effect of the age of mammography systems on the number of equipment faults, and the consequences of these faults was examined. The number of days downtime, number of cancelled appointments, number of repeated images, and number of recalled participants were used to quantify the severity of faults. RESULTS: This analysis covers a two year period and includes 4271 faults and 522 individual x-ray sets. On average, an x-ray set was 6.1 years old at the time when a fault occurred. 77% of x-ray sets experienced five of fewer annual faults. X-ray sets of nine years old had the highest average number of annual faults. Systems of ten years old had the highest average number of days downtime per year, and the highest average number of cancellations per year. The indicated primary use of 48% of the x-ray sets included in this analysis was screening, but a disproportionate 87% of cancelled appointments occurred due to faults on these units compared to those used primarily for assessment, or for a mixture of assessment and screening. CONCLUSIONS: Information from this unique dataset can be used to support guidance on equipment replacement programmes for mammographic x-ray sets.

Correction: Abdi et al. Visual Evaluation of Image Quality of a Low Dose 2D/3D Slot Scanner Imaging System Compared to Two Conventional Digital Radiography X-ray Imaging Systems. Diagnostics 2021, 11, 1932.

In the original publication [...].

Evaluation of a Programme of Online Arts Activities for Patients with Kidney Disease during the COVID-19 Pandemic.

Patients living with end-stage kidney disease (ESKD) have been seriously impacted by the COVID-19 pandemic. As these patients are considered extremely clinically vulnerable, they were advised to 'shield' at home, with limited face-to-face contact and support for the duration of the pandemic. Living with ESKD impacts heavily on patients' mental health and wellbeing, and this extended period of isolation and loneliness is likely to have a further negative effect on patients' mental wellbeing. The Renal Arts Group (RAG), Queen's University Belfast, aims to improve the quality of life of those living with ESKD and the extended renal community through engagement with the arts. We developed an initiative, funded by the Economic and Social Research Council, and carried out an evaluation. The initiative included a programme of online arts-based activities that built upon the work of RAG and provided mental wellbeing support for patients who faced an extended, lonely period of self-isolation. We worked with experienced arts practitioners to identify appropriate activities and developed five workshops and tutorials that were delivered online. We received positive feedback from participants who found the activities to be enjoyable, beneficial to their mental wellbeing and were interested in undertaking further activities online. We conducted interviews with the arts facilitators and identified three themes for consideration when developing online arts activities for the renal community. Participants reported that the activities benefited their mental wellbeing, were enjoyable and provided an opportunity to meet others with shared interests. The arts facilitators reported experiences around accessibility, audience engagement, impact on health and wellbeing and facilitator experience, that should be considered when developing online arts activities for the renal community. This evaluation will inform future work in this area, and the arts tutorial videos developed as part of this project will remain available online for members of the renal community to access.

Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging.

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.

Visual Evaluation of Image Quality of a Low Dose 2D/3D Slot Scanner Imaging System Compared to Two Conventional Digital Radiography X-ray Imaging Systems.

The purpose of this study was to assess the image quality of the low dose 2D/3D slot scanner (LDSS) imaging system compared to conventional digital radiography (DR) imaging systems. Visual image quality was assessed using the visual grading analysis (VGA) method. This method is a subjective approach that uses a human observer to evaluate and optimise radiographic images for different imaging technologies. METHODS AND MATERIALS: ten posterior-anterior (PA) and ten lateral (LAT) images of a chest anthropomorphic phantoms and a knee phantom were acquired by an LDSS imaging system and two conventional DR imaging systems. The images were shown in random order to three (chest) radiologists and three experienced (knee) radiographers, who scored the images against a number of criteria. Inter- and intraobserver agreement was assessed using Fleiss' kappa and weighted kappa. RESULTS: the statistical comparison of the agreement between the observers showed good interobserver agreement, with Fleiss' kappa coefficients of 0.27-0.63 and 0.23-0.45 for the chest and knee protocols, respectively. Comparison of intraobserver agreement also showed good agreement with weighted kappa coefficients of 0.27-0.63 and 0.23-0.45 for the chest and knee protocols, respectively. The LDSS imaging system achieved significantly higher VGA image quality compared to the DR imaging systems in the AP and LAT chest protocols (p < 0.001). However, the LDSS imaging system achieved lower image quality than one DR system (p ≤ 0.016) and equivalent image quality to the other DR systems (p ≤ 0.27) in the knee protocol. The LDSS imaging system achieved effective dose savings of 33-52% for the chest protocol and 30-35% for the knee protocol compared with DR systems. CONCLUSIONS: this work has shown that the LDSS imaging system has the potential to acquire chest and knee images at diagnostic quality and at a lower effective dose than DR systems.

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial.

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 < 0.001) for all glandularities. CONCLUSIONS: We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p = 0.059) and 18% (p = 0.19) glandularities and significantly larger diameters for calcifications (p < 0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters.

Quantitative Image Quality Metrics of the Low-Dose 2D/3D Slot Scanner Compared to Two Conventional Digital Radiography X-ray Imaging Systems.

The aim of this study was to determine the quantitative image quality metrics of the low-dose 2D/3D EOS slot scanner X-ray imaging system (LDSS) compared with conventional digital radiography (DR) X-ray imaging systems. The effective detective quantum efficiency (eDQE) and effective noise quantum equivalent (eNEQ) were measured using chest and knee protocols. METHODS: A Nationwide Evaluation of X-ray Trends (NEXT) of a chest adult phantom and a PolyMethylmethacrylate (PMMA) phantom were used for the chest and knee protocols, respectively. Quantitative image quality metrics, including effective normalised noise power spectrum (eNNPS), effective modulation transfer function (eMTF), eDQE and eNEQ of the LDSS and DR imaging systems were assessed and compared. RESULTS: In the chest acquisition, the LDSS imaging system achieved significantly higher eNEQ and eDQE than the DR imaging systems at lower and higher spatial frequencies (0.001 ≤ p ≤ 0.044). For the knee acquisition, the LDSS imaging system also achieved significantly higher eNEQ and eDQE than the DR imaging systems at lower and higher spatial frequencies (0.001 ≤ p ≤ 0.002). However, there was no significant difference in eNEQ and eDQE between DR systems 1 and 2 at lower and higher spatial frequencies (0.10 < p < 1.00) for either chest or knee protocols. CONCLUSION: The LDSS imaging system performed well compared to the DR systems. Thus, we have demonstrated that the LDSS imaging system has the potential to be used for clinical diagnostic purposes.

Development of a complex arts-based intervention for patients with end-stage kidney disease whilst receiving haemodialysis.

BACKGROUND: Patients with end-stage kidney disease who receive haemodialysis experience a protracted treatment regimen that can result in an increased risk of depression and anxiety. Arts-based interventions could address this unique issue; however, no arts-based interventions have been developed for delivery within a haemodialysis unit and evaluation within a randomised controlled trials (RCTs). AIM: To develop a complex arts-based intervention for patients with end-stage kidney disease whilst receiving haemodialysis. METHODS: The development process utilised the Arts in Health framework (Fancourt, 2017). The framework was addressed through the establishment of an interdisciplinary advisory group, collaboration and consultation with stakeholders, a scoping and realist review, shadowing of artists-in-residence, personal arts practice and logic modelling. RESULTS: The intervention involved six 1-h long, one-to-one facilitated sessions focused on creative writing and visual art. Patients could choose between art form and self-select a subject matter. The sessions had a primary focus on skill development and were delivered using principles derived from the psychological theory of flow. CONCLUSION: The Arts in Health framework provided an appropriate and pragmatic approach to intervention development. Complex arts-based interventions can be developed for the purpose of evaluation within a trial framework. This intervention was designed to strike a balance between standardised components, and a person-centred approach necessary to address existential boredom.

OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data.

Supplemental material is available for this article.

Validation of a mammographic image quality modification algorithm using 3D-printed breast phantoms.

Purpose: To validate a previously proposed algorithm that modifies a mammogram to appear as if it was acquired with different technique factors using realistic phantom-based mammograms. Approach: Two digital mammography systems (an indirect- and a direct-detector-based system) were used to acquire realistic mammographic images of five 3D-printed breast phantoms with the technique factors selected by the automatic exposure control and at various other conditions (denoted by the original images). Additional images under other simulated conditions were also acquired: higher or lower tube voltages, different anode/filter combinations, or lower tube current-time products (target images). The signal and noise in the original images were modified to simulate the target images (simulated images). The accuracy of the image modification algorithm was validated by comparing the target and simulated images using the local mean, local standard deviation (SD), local variance, and power spectra (PS) of the image signals. The absolute relative percent error between the target and simulated images for each parameter was calculated at each sub-region of interest (local parameters) and frequency (PS), and then averaged. Results: The local mean signal, local SD, local variance, and PS of the target and simulated images were very similar, with a relative percent error of 5.5%, 3.8%, 7.8%, and 4.4% (indirect system), respectively, and of 3.7%, 3.8%, 7.7%, and 7.5% (direct system), respectively. Conclusions: The algorithm is appropriate for simulating different technique factors. Therefore, it can be used in various studies, for instance to evaluate the impact of technique factors in cancer detection using clinical images.

Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis.

PURPOSE: To validate a candidate instrument, to be used by different professionals to assess image quality in digital mammography (DM), against detection performance results. METHODS: A receiver operating characteristics (ROC) study was conducted to assess the detection performance in DM images with four different image quality levels due to different quality issues. Fourteen expert breast radiologists from five countries assessed a set of 80 DM cases, containing 60 lesions (40 cancers, 20 benign findings) and 20 normal cases. A visual grading analysis (VGA) study using a previously-described candidate instrument was conducted to evaluate a subset of 25 of the images used in the ROC study. Eight radiologists that had participated in the ROC study, and seven expert breast-imaging physicists, evaluated this subset. The VGA score (VGAS) and the ROC and visual grading characteristics (VGC) areas under the curve (AUCROC and AUCVGC) were compared. RESULTS: No large differences in image quality among the four levels were detected by either ROC or VGA studies. However, the ranking of the four levels was consistent: level 1 (partial AUCROC: 0.070, VGAS: 6.77) performed better than levels 2 (0.066, 6.15), 3 (0.061, 5.82), and 4 (0.062, 5.37). Similarity between radiologists' and physicists' assessments was found (average VGAS difference of 10 %). CONCLUSIONS: The results from the candidate instrument were found to correlate with those from ROC analysis, when used by either observer group. Therefore, it may be used by different professionals, such as radiologists, radiographers, and physicists, to assess clinically-relevant image quality variations in DM.

How does image quality affect radiologists' perceived ability for image interpretation and lesion detection in digital mammography?

OBJECTIVES: To study how radiologists' perceived ability to interpret digital mammography (DM) images is affected by decreases in image quality. METHODS: One view from 45 DM cases (including 30 cancers) was degraded to six levels each of two acquisition-related issues (lower spatial resolution and increased quantum noise) and three post-processing-related issues (lower and higher contrast and increased correlated noise) seen during clinical evaluation of DM systems. The images were shown to fifteen breast screening radiologists from five countries. Aware of lesion location, the radiologists selected the most-degraded mammogram (indexed from 1 (reference) to 7 (most degraded)) they still felt was acceptable for interpretation. The median selected index, per degradation type, was calculated separately for calcification and soft tissue (including normal) cases. Using the two-sided, non-parametric Mann-Whitney test, the median indices for each case and degradation type were compared. RESULTS: Radiologists were not tolerant to increases (medians: 1.5 (calcifications) and 2 (soft tissue)) or decreases (median: 2, for both types) in contrast, but were more tolerant to correlated noise (median: 3, for both types). Increases in quantum noise were tolerated more for calcifications than for soft tissue cases (medians: 3 vs. 4, p = 0.02). Spatial resolution losses were considered less acceptable for calcification detection than for soft tissue cases (medians: 3.5 vs. 5, p = 0.001). CONCLUSIONS: Perceived ability of radiologists for image interpretation in DM was affected not only by image acquisition-related issues but also by image post-processing issues, and some of those issues affected calcification cases more than soft tissue cases. KEY POINTS: • Lower spatial resolution and increased quantum noise affected the radiologists' perceived ability to interpret calcification cases more than soft tissue lesion or normal cases. • Post-acquisition image processing-related effects, not only image acquisition-related effects, also impact the perceived ability of radiologists to interpret images and detect lesions. • In addition to current practices, post-acquisition image processing-related effects need to also be considered during the testing and evaluation of digital mammography systems.

Development and content validity evaluation of a candidate instrument to assess image quality in digital mammography: A mixed-method study.

PURPOSE: To develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. METHODS: Interviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). RESULTS: Twelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. CONCLUSIONS: By following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.