Consistency of breast density categories in serial screening mammograms: A comparison between automated and human assessment

Are mammography image acquisition factors, compression pressure and paddle tilt, associated with breast cancer detection in screening?

OBJECTIVES

To assess the associations between objectively measured mammographic compression pressure and paddle tilt and breast cancer (BC) detected at the same ("contemporaneous") screen, subsequent screens, or in-between screens (interval cancers).

METHODS

Automated pressure and paddle tilt estimates were derived for 80,495 mammographic examinations in a UK population-based screening programme. Adjusted logistic regression models were fitted to estimate the associations of compression parameters with BC detected at contemporaneous screen (777 cases).Nested case-control designs were used to estimate associations of pressure and tilt with: (a) interval cancer (148 cases/625 age-matched controls) and (b) subsequent screen-detected cancer (344/1436), via conditional logistic regression.

RESULTS

Compression pressure was negatively associated with odds of BC at contemporaneous screen (odds ratio (OR) for top versus bottom third of the pressure distribution: 0.74; 95% CI 0.60, 0.92; P-for-linear-trend (Pt) = 0.007). There was weak evidence that moderate pressure at screening was associated with lower odds of interval cancer (OR for middle versus bottom third: 0.63; 95% CI 0.38, 1.05; p = 0.079), but no association was found between pressure and the odds of BC at subsequent screen. There was no evidence that paddle tilt was associated with the odds of contemporaneous, subsequent screen or interval cancer detection.

CONCLUSIONS

Findings are consistent with compression pressure, but not paddle tilt, affecting the performance of mammographic screening by interfering with its ability to detect cancers.

ADVANCES IN KNOWLEDGE

Inadequate or excessive compression pressure at screening may contribute to a reduced ability to detect cancers, resulting in a greater number of interval cancer cases.

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study

Understanding the detectability of breast cancer using mammography is important when considering nation-wide screening programmes. Although the role of imaging settings on image quality has been studied extensively, their role in detectability of cancer at a population level is less well studied. We wish to quantify the association between mammographic screening sensitivity and various imaging parameters. Using a novel approach applied to a population-based breast cancer screening cohort, we specifically focus on sensitivity as defined in the classical diagnostic testing literature, as opposed to the screen-detected cancer rate, which is often used as a measure of sensitivity for monitoring and evaluating breast cancer screening. We use a natural history approach to model the presence and size of latent tumors at risk of detection at mammography screening, and the screening sensitivity is modeled as a logistic function of tumor size. With this approach we study the influence of compressed breast thickness, x-ray exposure, and compression pressure, in addition to (percent) breast density, on the screening test sensitivity. When adjusting for all screening parameters in addition to latent tumor size, we find that percent breast density and compressed breast thickness are statistically significant factors for the detectability of breast cancer. A change in breast density from 6.6 to 33.5% (the inter-quartile range) reduced the odds of detection by 61% (95% CI 48-71). Similarly, a change in compressed breast thickness from 46 to 66 mm reduced the odds by 42% (95% CI 21-57). The true sensitivity of mammography, defined as the probability that an examination leads to a positive result if a tumour is present in the breast, is associated with compressed breast thickness after accounting for mammographic density and tumour size. This can be used to guide studies of setups aimed at improving lesion detection. Compressed breast thickness-in addition to breast density-should be considered when assigning complementary screening modalities and personalized screening intervals.

Opinions on the Assessment of Breast Density Among Members of the Society of Breast Imaging

OBJECTIVE

Dense breast decreases the sensitivity and specificity of mammography and is associated with an increased risk of breast cancer. We conducted a survey to assess the opinions of Society of Breast Imaging (SBI) members regarding density assessment.

METHODS

An online survey was sent to SBI members twice in September 2020. The survey included active members who were practicing radiologists, residents, and fellows. Mammograms from three patients were presented for density assessment based on routine clinical practice and BI-RADS fourth and fifth editions. Dense breasts were defined as heterogeneously or extremely dense. Frequencies were calculated for each survey response. Pearson's correlation coefficient was used to evaluate the correlation of density assessments by different definitions.

RESULTS

The survey response rate was 12.4% (357/2875). For density assessments, the Pearson correlation coefficients between routine clinical practice and BI-RADS fourth edition were 0.05, 0.43, and 0.12 for patients 1, 2, and 3, respectively; these increased to 0.65, 0.65, and 0.66 between routine clinical practice and BI-RADS fifth edition for patients 1, 2, and 3, respectively. For future density grading, 79.0% (282/357) of respondents thought it should reflect both potential for masking and overall dense tissue for risk assessment. Additionally, 47.1% (168/357) of respondents thought quantitative methods were of use.

CONCLUSION

Density assessment varied based on routine clinical practice and BI-RADS fourth and fifth editions. Most breast radiologists agreed that density assessment should capture both masking and overall density. Moreover, almost half of respondents believed computer or artificial intelligence-assisted quantitative methods may help refine density assessment.

Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review

The advent of artificial intelligence (AI) represents a real game changer in today's landscape of breast cancer imaging. Several innovative AI-based tools have been developed and validated in recent years that promise to accelerate the goal of real patient-tailored management. Numerous studies confirm that proper integration of AI into existing clinical workflows could bring significant benefits to women, radiologists, and healthcare systems. The AI-based approach has proved particularly useful for developing new risk prediction models that integrate multi-data streams for planning individualized screening protocols. Furthermore, AI models could help radiologists in the pre-screening and lesion detection phase, increasing diagnostic accuracy, while reducing workload and complications related to overdiagnosis. Radiomics and radiogenomics approaches could extrapolate the so-called imaging signature of the tumor to plan a targeted treatment. The main challenges to the development of AI tools are the huge amounts of high-quality data required to train and validate these models and the need for a multidisciplinary team with solid machine-learning skills. The purpose of this article is to present a summary of the most important AI applications in breast cancer imaging, analyzing possible challenges and new perspectives related to the widespread adoption of these new tools.

Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk

We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.

Mammographic density changes following BC treatment

BACKGROUND

Mammographic density (MD) reduction is associated with lower risk of breast cancer (BC) recurrence and may be used as a marker of treatment outcome; however, trends in MD following BC therapies and the factors associated with such trends are poorly understood. The aim of this study was to investigate MD changes following BC treatment and the factors associated with these changes.

METHODS

A total of 226 BC-affected patients who received BC treatments were examined. MD was assessed by the Laboratory for individualized Radiodensity Assessment (LIBRA) software. A Wilcoxon ranked signed test was used to investigate the differences in MD before and after treatment and median independent test to assess the associated factors.

RESULTS

Significant differences in MD between baseline and follow-up mammograms were observed for all MD measures: percent density (p ≤ 0.005), dense area (p ≤ 0.004), and nondense area (p ≤ 0.02). After adjustment, these differences were more pronounced among younger at BC diagnosis (p ≤ 0.001), premenopausal (p ≤ 0.003), and obese women (p ≤ 0.05). Changes in MD were evident regardless of the treatment regimen. MD reduction was observed among patients with high baseline MD (p < 0.001), younger at BC diagnosis (p ≤ 0.04), premenopausal (p < 0.001), and normal body mass index (p = 0.04). Patients who experienced an increase in nondense area had high percent density at baseline (p ≤ 0.001).

CONCLUSION

Two different MD changes were observed over time: MD increase and decrease. Baseline MD, menopausal status, age at BC diagnosis, and body mass index influenced these changes.

Left-right breast asymmetry and risk of screen-detected and interval cancers in a large population-based screening population

OBJECTIVES

To assess the associations between automated volumetric estimates of mammographic asymmetry and breast cancers detected at the same ("contemporaneous") screen, at subsequent screens, or in between (interval cancers).

METHODS

Automated measurements from mammographic images (N = 79,731) were used to estimate absolute asymmetry in breast volume (BV) and dense volume (DV) in a large ethnically diverse population of attendees of a UK breast screening programme. Logistic regression models were fitted to assess asymmetry associations with the odds of a breast cancer detected at contemporaneous screen (767 cases), adjusted for relevant confounders.Nested case-control investigations were designed to examine associations between asymmetry and the odds of: (a) interval cancer (numbers of cases/age-matched controls: 153/646) and (b) subsequent screen-detected cancer (345/1438), via conditional logistic regression.

RESULTS

DV, but not BV, asymmetry was positively associated with the odds of contemporaneous breast cancer (P-for-linear-trend (Pt) = 0.018). This association was stronger for first (prevalent) screens (Pt = 0.012). Both DV and BV asymmetry were positively associated with the odds of an interval cancer diagnosis (Pt = 0.060 and 0.030, respectively). Neither BV nor DV asymmetry were associated with the odds of having a subsequent screen-detected cancer.

CONCLUSIONS

Increased DV asymmetry was associated with the risk of a breast cancer diagnosis at a contemporaneous screen or as an interval cancer. BV asymmetry was positively associated with the risk of an interval cancer diagnosis.

ADVANCES IN KNOWLEDGE

The findings suggest that DV and BV asymmetry may provide additional signals for detecting contemporaneous cancers and assessing the likelihood of interval cancers in population-based screening programmes.

Adiposity Change Over the Life Course and Mammographic Breast Density in Postmenopausal Women

Mammographic breast density is a strong risk factor for breast cancer. We comprehensively investigated the associations of body mass index (BMI) change from ages 10, 18, and 30 to age at mammogram with mammographic breast density in postmenopausal women. We used multivariable linear regression models, adjusted for confounders, to investigate the associations of BMI change with volumetric percent density, dense volume, and nondense volume, assessed using Volpara in 367 women. At the time of mammogram, the mean age was 57.9 years. Compared with women who had a BMI gain of 0.1-5 kg/m2 from age 10, women who had a BMI gain of 5.1-10 kg/m2 had a 24.4% decrease [95% confidence interval (CI), 6.0%-39.2%] in volumetric percent density; women who had a BMI gain of 10.1-15 kg/m2 had a 46.1% decrease (95% CI, 33.0%-56.7%) in volumetric percent density; and women who had a BMI gain of >15 kg/m2 had a 56.5% decrease (95% CI, 46.0%-65.0%) in volumetric percent density. Similar, but slightly attenuated associations were observed for BMI gain from ages 18 and 30 to age at mammogram and volumetric percent density. BMI gain over the life course was positively associated with nondense volume, but not dense volume. We observed strong associations between BMI change over the life course and mammographic breast density. The inverse associations between early-life adiposity change and volumetric percent density suggest that childhood adiposity may confer long-term protection against postmenopausal breast cancer via its effect of mammographic breast density.

Changes in breast density over serial mammograms: A case-control study

PURPOSE

In addition to a breast density category, temporal changes in breast density have gained attention as a dynamic breast cancer risk marker. This case-control study aimed to investigate a potential change in breast density preceding tumor development and the relationship of this potential change to prognostic pathological tumor variables.

METHOD

A total of 51 consecutive, eligible-for-analyses, biopsy-proven breast cancers were diagnosed between 1 st of August and 31 st of December 2014 at Skåne University Hospital, Sweden. Mammogram data and patient- and tumor characteristics were retrieved retrospectively from medical charts. Breast density was quantitatively estimated using LIBRA (a free open source software package). The cases were matched for year of birth, number of screening rounds, and date for first and last mammograms with controls from the Malmö Breast Tomosynthesis Screening Trial in a 1:2 ratio, resulting in median time between mammograms of 4.5 (1.3-11.9) years for cases and 4.7 (1.4-11.1) years for controls, averaging approximately three screening rounds (1-6 rounds).

RESULTS

We detected a statistically significant difference in breast density change over time, with cases showing an increase in breast density (1.7 %) as compared to controls (-0.3 %) (p = 0.045). We found that in women with breast cancer, older women (≥ 55 years) experienced a higher breast density increase compared to younger women (5.1 % vs. 0.3 %, p = 0.002).

CONCLUSIONS

There was a statistically significant difference in density change, where women with breast cancer showed an increased density over time, which was particularly evident in women > 55 years of age.

Ethnic and age differences in right-left breast asymmetry in a large population-based screening population

OBJECTIVE

Exposure to sex hormones is important in the pathogenesis of breast cancer and inability to tolerate such exposure may be reflected in increased asymmetrical growth of the breasts. This study aims to characterize, for the first time, asymmetry in breast volume (BV) and radiodense volume (DV) in a large ethnically diverse population.

METHODS

Automated measurements from digital raw mammographic images of 54,591 cancer-free participants (aged 47-73) in a UK breast screening programme were used to calculate absolute (cm³) and relative asymmetry in BV and DV. Logistic regression models were fitted to assess asymmetry associations with age and ethnicity.

RESULTS

BV and DV absolute asymmetry were positively correlated with the corresponding volumetric dimension (BV or DV). BV absolute asymmetry increased, whilst DV absolute asymmetry decreased, with increasing age (P-for-linear-trend <0.001 for both). Relative to Whites, Blacks had statistically significantly higher, and Chinese lower, BV and DV absolute asymmetries. However, after adjustment for the corresponding underlying volumetric dimension the age and ethnic differences were greatly attenuated. Median relative (fluctuating) BV and DV asymmetry were 2.34 and 3.28% respectively.

CONCLUSION

After adjusting for the relevant volumetric dimension (BV or DV), age and ethnic differences in absolute breast asymmetry were largely resolved.

ADVANCES IN KNOWLEDGE

Previous small studies have reported breast asymmetry-breast cancer associations. Automated measurements of asymmetry allow the conduct of large-scale studies to further investigate these associations.

Comparison of subjective and fully automated methods for measuring mammographic density

Background Breast radiologists of the Norwegian Breast Cancer Screening Program subjectively classified mammographic density using a three-point scale between 1996 and 2012 and changed into the fourth edition of the BI-RADS classification since 2013. In 2015, an automated volumetric breast density assessment software was installed at two screening units. Purpose To compare volumetric breast density measurements from the automated method with two subjective methods: the three-point scale and the BI-RADS density classification. Material and Methods Information on subjective and automated density assessment was obtained from screening examinations of 3635 women recalled for further assessment due to positive screening mammography between 2007 and 2015. The score of the three-point scale (I = fatty; II = medium dense; III = dense) was available for 2310 women. The BI-RADS density score was provided for 1325 women. Mean volumetric breast density was estimated for each category of the subjective classifications. The automated software assigned volumetric breast density to four categories. The agreement between BI-RADS and volumetric breast density categories was assessed using weighted kappa (k_w). Results Mean volumetric breast density was 4.5%, 7.5%, and 13.4% for categories I, II, and III of the three-point scale, respectively, and 4.4%, 7.5%, 9.9%, and 13.9% for the BI-RADS density categories, respectively ( P for trend < 0.001 for both subjective classifications). The agreement between BI-RADS and volumetric breast density categories was k_w = 0.5 (95% CI = 0.47-0.53; P < 0.001). Conclusion Mean values of volumetric breast density increased with increasing density category of the subjective classifications. The agreement between BI-RADS and volumetric breast density categories was moderate.

Breast density: why all the fuss?

The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.

Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad

Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.