Performance of diagnostic mammography differs in the United States and Denmark

Modeling viscous damping in actuated breast tissue to provide diagnostic insight for breast cancer: A proof-of-concept analysis

PURPOSE

This study develops a viscous damping model (VDM) based on Rayleigh Damping (RD) with potential use in low cost, non-invasive breast cancer diagnostics using Digital Image Elasto Tomography (DIET).

METHODS

A clinical trial involving 13 subjects, each with a tumor in one breast, resulted in 13 cancerous and 13 healthy breasts. Displacement data following actuator induced steady state vibration in the breast tissue were captured using the DIET system. Over 14 000 reference points on the breast surface were split into four segments and viscous damping constant calculated for each reference point. The VDM was fit to median-filtered data for each breast segment and VDM coefficients compared within each breast. One model coefficient, relating to stiffness, was hypothesized to differ in breast segments containing a tumor. Comparison of " b " coefficients in different breast segments using percentage tolerances provided an unbiased, generalizable diagnostic method. Bootstrapping with replacement was used to upsample the data and create smooth receiver operator characteristic (ROC) curves. A total of 12 breast segmentation configurations were used to demonstrate the robustness of the method.

RESULTS

Fitting the VDM to median-filtered data gave consistent results for one VDM coefficient (" a ") across all breasts. The second VDM coefficient (" b ") showed diagnostic potential with breast segments having consistent coefficients in healthy breasts. In cancerous breasts " b " coefficients were found to be statistically different in segments containing and adjacent to the tumor compared with the segment furthest from the tumor with p < 0.02 using the Student t-Test. Large discrepancies in " b " coefficients were found to be indicative of a tumor with a 14.5% tolerance resulting in sensitivity and specificity of 76.9%. The optimal breast configuration resulted in an area under the ROC curve (AUC) of 0.81 with sensitivity and specificity at 77% and 72%, respectively.

CONCLUSION

This VDM method enables a computationally simple diagnostic technique using DIET for comfortable breast screening for women of all ages. Regular screening potential allows for tolerance alteration based on age, prior subject-specific results, and other risk factors to manage false positives, reducing psychological harm while optimizing early detection for successful treatment and decreased mortality.

Mammographic compression practices of force- and pressure-standardisation protocol: A scoping review

INTRODUCTION

As an efficient, effective and moderately inexpensive modality, mammography has been implemented as a cancer screening tool and in diagnostic management. However, appropriate breast compression is necessary for optimal outcomes. Current key measures of compression force are subjective and variable, giving rise to the concept of a 'personalised' pressure-standardisation protocol.

METHODS

A scoping review of the literature was performed using the Arksey and O'Malley framework to explore the existing force- and pressure-standardisation protocols in clinical application. A comprehensive search strategy and standardised study selection and evaluation were completed. This synthesis of existing knowledge can lead to the implementation of mechanically standardised mammographic compression pressure as a feasible tailored approach to clinical practice. Four databases (PubMed, MEDLINE, Embase and Scopus) were searched from the databases' inception to 13 December 2019 for relevant information, and eighteen articles were selected for analysis.

RESULTS

In addition to current protocol comparison, emerging key concepts include the reasoning behind standardisation, the benefits of improved diagnostic outcomes/decreased pain with negligible change in image quality and average glandular dose (AGD), and the recommendation of a 10kPa (approximate) pressure-standardisation protocol. Research to date is largely based abroad (Netherlands), with a strong focus on screening practices. Consequently, several gaps in the current literature were identified as potential directions for future investigation.

CONCLUSIONS

As a suggested mammographic guideline, compression pressures of approximately 10kPa aid in image acquisition reproducibility both within and between women; pain levels decrease, with minimal variations to breast thickness, AGD and image quality.

Physician and Nonphysician Estimates of Positive Predictive Value in Diagnostic v. Mass Screening Mammography: An Examination of Bayesian Reasoning

BACKGROUND

The same test with the same result has different positive predictive values (PPVs) for people with different pretest probability of disease. Representative thinking theory suggests people are unlikely to realize this because they ignore or underweight prior beliefs when given new information (e.g., test results) or due to confusing test sensitivity (probability of positive test given disease) with PPV (probability of disease given positive test). This research examines whether physicians and MBAs intuitively know that PPV following positive mammography for an asymptomatic woman is less than PPV for a symptomatic woman and, if so, whether they correctly perceive the difference.

DESIGN

Sixty general practitioners (GPs) and 84 MBA students were given 2 vignettes of women with abnormal (positive) mammography tests: 1 with prior symptoms (diagnostic test), the other an asymptomatic woman participating in mass screening (screening test). Respondents estimated pretest and posttest probabilities. Sensitivity and specificity were neither provided nor elicited.

RESULTS

Eighty-eight percent of GPs and 46% of MBAs considered base rates and estimated PPV in diagnosis greater than PPV in screening. On average, GPs estimated a 27-point difference and MBAs an 18-point difference, compared to actual of 55 or more points. Ten percent of GPs and 46% of MBAs ignored base rates, incorrectly assessing the 2 PPVs as equal.

CONCLUSIONS

Physicians and patients are better at intuitive Bayesian reasoning than is suggested by studies that make test accuracy values readily available to be confused with PPV. However, MBAs and physicians interpret a positive in screening as more similar to a positive in diagnosis than it is, with nearly half of MBAs and some physicians wrongly equating the two. This has implications for overdiagnosis and overtreatment.

National Performance Benchmarks for Modern Diagnostic Digital Mammography: Update from the Breast Cancer Surveillance Consortium

Purpose To establish contemporary performance benchmarks for diagnostic digital mammography with use of recent data from the Breast Cancer Surveillance Consortium (BCSC). Materials and Methods Institutional review board approval was obtained for active or passive consenting processes or to obtain a waiver of consent to enroll participants, link data, and perform analyses. Data were obtained from six BCSC registries (418 radiologists, 92 radiology facilities). Mammogram indication and assessments were prospectively collected for women undergoing diagnostic digital mammography and linked with cancer diagnoses from state cancer registries. The study included 401 548 examinations conducted from 2007 to 2013 in 265 360 women. Results Overall diagnostic performance measures were as follows: cancer detection rate, 34.7 per 1000 (95% confidence interval [CI]: 34.1, 35.2); abnormal interpretation rate, 12.6% (95% CI: 12.5%, 12.7%); positive predictive value (PPV) of a biopsy recommendation (PPV₂), 27.5% (95% CI: 27.1%, 27.9%); PPV of biopsies performed (PPV₃), 30.4% (95% CI: 29.9%, 30.9%); false-negative rate, 4.8 per 1000 (95% CI: 4.6, 5.0); sensitivity, 87.8% (95% CI: 87.3%, 88.4%); and specificity, 90.5% (95% CI: 90.4%, 90.6%). Among cancers detected, 63.4% were stage 0 or 1 cancers, 45.6% were minimal cancers, the mean size of invasive cancers was 21.2 mm, and 69.6% of invasive cancers were node negative. Performance metrics varied widely across diagnostic indications, with cancer detection rate (64.5 per 1000) and abnormal interpretation rate (18.7%) highest for diagnostic mammograms obtained to evaluate a breast problem with a lump. Compared with performance during the screen-film mammography era, diagnostic digital performance showed increased abnormal interpretation and cancer detection rates and decreasing PPVs, with less than 70% of radiologists within acceptable ranges for PPV₂ and PPV₃. Conclusion These performance measures can serve as national benchmarks that may help transform the marked variation in radiologists' diagnostic performance into targeted quality improvement efforts. ^© RSNA, 2017 Online supplemental material is available for this article.

Patient and Radiologist Characteristics Associated With Accuracy of Two Types of Diagnostic Mammograms

OBJECTIVE

Earlier studies of diagnostic mammography found wide unexplained variability in accuracy among radiologists. We assessed patient and radiologist characteristics associated with the interpretive performance of two types of diagnostic mammography.

MATERIALS AND METHODS

Radiologists interpreting mammograms in seven regions of the United States were invited to participate in a survey that collected information on their demographics, practice setting, breast imaging experience, and self-reported interpretive volume. Survey data from 244 radiologists were linked to data on 274,401 diagnostic mammograms performed for additional evaluation of a recent abnormal screening mammogram or to evaluate a breast problem, between 1998 and 2008. These data were also linked to patients' risk factors and follow-up data on breast cancer. We measured interpretive performance by false-positive rate, sensitivity, and AUC. Using logistic regression, we evaluated patient and radiologist characteristics associated with false-positive rate and sensitivity for each diagnostic mammogram type.

RESULTS

Mammograms performed for additional evaluation of a recent mammogram had an overall false-positive rate of 11.9%, sensitivity of 90.2%, and AUC of 0.894; examinations done to evaluate a breast problem had an overall false-positive rate of 7.6%, sensitivity of 83.9%, and AUC of 0.871. Multiple patient characteristics were associated with measures of interpretive performance, and radiologist academic affiliation was associated with higher sensitivity for both indications for diagnostic mammograms.

CONCLUSION

These results indicate the potential for improved radiologist training, using evaluation of their own performance relative to best practices, and for improved clinical outcomes with health care system changes to maximize access to diagnostic mammography interpretation in academic settings.

Estimating the relative utility of screening mammography

BACKGROUND

The concept of diagnostic utility is a fundamental component of signal detection theory, going back to some of its earliest works. Attaching utility values to the various possible outcomes of a diagnostic test should, in principle, lead to meaningful approaches to evaluating and comparing such systems. However, in many areas of medical imaging, utility is not used because it is presumed to be unknown.

METHODS

In this work, we estimate relative utility (the utility benefit of a detection relative to that of a correct rejection) for screening mammography using its known relation to the slope of a receiver operating characteristic (ROC) curve at the optimal operating point. The approach assumes that the clinical operating point is optimal for the goal of maximizing expected utility and therefore the slope at this point implies a value of relative utility for the diagnostic task, for known disease prevalence. We examine utility estimation in the context of screening mammography using the Digital Mammographic Imaging Screening Trials (DMIST) data.

RESULTS

We show how various conditions can influence the estimated relative utility, including characteristics of the rating scale, verification time, probability model, and scope of the ROC curve fit. Relative utility estimates range from 66 to 227.

CONCLUSIONS

We argue for one particular set of conditions that results in a relative utility estimate of 162 (±14%). This is broadly consistent with values in screening mammography determined previously by other means. At the disease prevalence found in the DMIST study (0.59% at 365-day verification), optimal ROC slopes are near unity, suggesting that utility-based assessments of screening mammography will be similar to those found using Youden's index.