To prospectively determine the prevalence and predictive value of three-dimensional (3D) and dynamic breast magnetic resonance (MR) imaging and contrast material kinetic features alone and as part of ...predictive diagnostic models.
The study protocol was approved by the institutional review board or ethics committees of all participating institutions, and informed consent was obtained from all participants. Although study data collection was performed before HIPAA went into effect, standards that would be compliant with HIPAA were adhered to. Data from the International Breast MR Consortium trial 6883 were used in the analysis. Women underwent 3D (minimum spatial resolution, 0.7 x 1.4 x 3 mm; minimal temporal resolution, 4 minutes) and dynamic two-dimensional (temporal resolution, 15 seconds) MR imaging examinations. Readers rated enhancement shape, enhancement distribution, border architecture, enhancement intensity, presence of rim enhancement or internal septations, and the shape of the contrast material kinetic curve. Regression was performed for each feature individually and after adjustment for associated mammographic findings. Multivariate models were also constructed from multiple architectural and dynamic features. Areas under the receiver operating characteristic curve (Az values) were estimated for all models.
There were 995 lesions in 854 women (mean age, 53 years +/- 12 standard deviation; range, 18-80 years) for whom pathology data were available. The absence of enhancement was associated with an 88% negative predictive value for cancer. Qualitative characterization of the dynamic enhancement pattern was associated with an Az value of 0.66 across all lesion architectures. Focal mass margins (Az = 0.76) and signal intensity (Az = 0.70) were highly predictive imaging features. Multivariate models were constructed with an Az value of 0.880.
Architectural and dynamic features are important in breast MR imaging interpretation. Multivariate models involving feature assessment have a diagnostic accuracy superior to that of qualitative characterization of the dynamic enhancement pattern.
The DMIST (Digital Mammography Imaging Screening Trial) reported improved breast cancer detection with digital mammography compared with film mammography in selected population subgroups, but it did ...not assess the economic value of digital relative to film mammography screening.
To evaluate the cost-effectiveness of digital mammography screening for breast cancer.
Validated, discrete-event simulation model.
Data from DMIST and publicly available U.S. data.
U.S. women age 40 years or older.
Lifetime.
Societal and Medicare.
All-film mammography screening; all-digital mammography screening; and targeted digital mammography screening, which is age-targeted digital mammography (for women <50 years of age) and age- and density-targeted digital mammography (for women <50 years of age or women > or =50 years of age with dense breasts).
Cost per quality-adjusted life-year (QALY) gained.
All-digital mammography screening cost $331,000 (95% CI, $268,000 to $403,000) per QALY gained relative to all-film mammography screening but was more costly and less effective than targeted digital mammography screening. Targeted digital mammography screening resulted in more screen-detected cases of cancer and fewer deaths from cancer than either all-film or all-digital mammography screening, with cost-effectiveness estimates ranging from $26,500 (CI, $21,000 to $33,000) per QALY gained for age-targeted digital mammography to $84,500 (CI, $75,000 to $93,000) per QALY gained for age- and density-targeted digital mammography. In the Medicare population, the cost-effectiveness of density-targeted digital mammography screening varied from a base-case estimate of $97,000 (CI, $77,000 to $131,000) to $257,000 per QALY gained (CI, $91,000 to $536,000) in the alternative-case analyses, in which the sensitivity of film mammography was increased and the sensitivity of digital mammography in women with nondense breasts was decreased.
Results were sensitive to the cost of digital mammography and to the prevalence of dense breasts.
Results were dependent on model assumptions and DMIST findings.
Relative to film mammography, screening for breast cancer by using all-digital mammography is not cost-effective. Age-targeted screening with digital mammography seems cost-effective, whereas density-targeted screening strategies are more costly and of uncertain value, particularly among women age 65 years or older.
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
Purpose
In 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.
Methods
Test 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.
Results
The 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.
Conclusions
Missing 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.
The purpose of this study was to assess the impact of computer-aided detection (CAD) systems on the performance of radiologists with digital mammograms acquired during the Digital Mammographic ...Imaging Screening Trial (DMIST).
Only those DMIST cases with proven cancer status by biopsy or 1-year follow-up that had available digital images were included in this multireader, multicase ROC study. Two commercially available CAD systems for digital mammography were used: iCAD SecondLook, version 1.4; and R2 ImageChecker Cenova, version 1.0. Fourteen radiologists interpreted, without and with CAD, a set of 300 cases (150 cancer, 150 benign or normal) on the iCAD SecondLook system, and 15 radiologists interpreted a different set of 300 cases (150 cancer, 150 benign or normal) on the R2 ImageChecker Cenova system.
The average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI, 0.67-0.77) with the iCAD system (p = 0.07). Similarly, the average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI 0.67-0.77) with the R2 system (p = 0.08). Sensitivity and specificity differences without and with CAD for both systems also were not significant.
Radiologists in our studies rarely changed their diagnostic decisions after the addition of CAD. The application of CAD had no statistically significant effect on radiologist AUC, sensitivity, or specificity performance with digital mammograms from DMIST.
Digital mammography Pisano, Etta D; Yaffe, Martin J
Radiology,
02/2005, Letnik:
234, Številka:
2
Journal Article
Recenzirano
In digital mammography, the processes of image acquisition, display, and storage are separated, which allows optimization of each. Radiation transmitted through the breast is absorbed by an ...electronic detector, the response of which is faithful over a wide range of intensities. Once this information is recorded, it can be displayed by using computer image-processing techniques to allow arbitrary settings of image brightness and contrast, without the need for further exposure to the patient. In this article, the current state of the art in technology for digital mammography and data from clinical trials that support the use of the technology will be reviewed. In addition, several potentially useful applications that are being developed with digital mammography will be described.
We developed deep learning algorithms to automatically assess BI-RADS breast density.
Using a large multi-institution patient cohort of 108,230 digital screening mammograms from the Digital ...Mammographic Imaging Screening Trial, we investigated the effect of data, model, and training parameters on overall model performance and provided crowdsourcing evaluation from the attendees of the ACR 2019 Annual Meeting.
Our best-performing algorithm achieved good agreement with radiologists who were qualified interpreters of mammograms, with a four-class κ of 0.667. When training was performed with randomly sampled images from the data set versus sampling equal number of images from each density category, the model predictions were biased away from the low-prevalence categories such as extremely dense breasts. The net result was an increase in sensitivity and a decrease in specificity for predicting dense breasts for equal class compared with random sampling. We also found that the performance of the model degrades when we evaluate on digital mammography data formats that differ from the one that we trained on, emphasizing the importance of multi-institutional training sets. Lastly, we showed that crowdsourced annotations, including those from attendees who routinely read mammograms, had higher agreement with our algorithm than with the original interpreting radiologists.
We demonstrated the possible parameters that can influence the performance of the model and how crowdsourcing can be used for evaluation. This study was performed in tandem with the development of the ACR AI-LAB, a platform for democratizing artificial intelligence.
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