Soujanya Gadde

Local mammographic density as a predictor of breast cancer

High overall mammographic density is associated with both an increased risk of developing breast cancer and the risk of cancer being masked. We compared local density at cancer sites in diagnostic images with corresponding previous screening mammograms (priors), and matched controls. VolparaTM density maps were obtained for 54 mammograms showing unilateral breast cancer and their priors which had been previously read as normal. These were each matched to 3 controls on age, menopausal status, hormone replacement therapy usage, body mass index and year of prior. Local percent density was computed in 15mm square regions at lesion sites and similar locations in the corresponding images. Conditional logistic regression was used to predict case-control status. In diagnostic and prior images, local density was increased at the lesion site compared with the opposite breast (medians 21.58%, 9.18%, p<0.001 diagnostic; 18.82%, 9.45%, p <0.001 prior). Women in the highest tertile of local density in priors were more likely to develop cancer than those in the lowest tertile (OR 42.09, 95% CI 5.37-329.94). Those in the highest tertile of VolparaTM gland volume were also more likely to develop cancer (OR 2.89, 95% CI 1.30-6.42). Local density is increased where cancer will develop compared with corresponding regions in the opposite breast and matched controls, and its measurement could enhance computer-aided mammography.

Same task, same observers, different values: the problem with visual assessment of breast density

The proportion of radio-opaque fibroglandular tissue in a mammographic image of the breast is a strong and modifiable risk factor for breast cancer. Subjective, area-based estimates made by expert observers provide a simple and efficient way of measuring breast density within a screening programme, but the degree of variability may render the reliable identification of women at increased risk impossible. This study examines the repeatability of visual assessment of percent breast density by expert observers. Five consultant radiologists and two breast physicians, all with at least two years’ experience in mammographic density assessment, were presented with 100 digital mammogram cases for which they had estimated density at least 12 months previously. Estimates of percent density were made for each mammographic view and recorded on a printed visual analogue scale. The level of agreement between the two sets of estimates was assessed graphically using Bland-Altman plots. All but one observer had a mean difference of less than 6 percentage points, while the largest mean difference was 14.66 percentage points. The narrowest 95% limits of agreement for the differences were -11.15 to 17.35 and the widest were -13.95 to 40.43. Coefficients of repeatability ranged from 14.40 to 38.60. Although visual assessment of breast density has been shown to be strongly associated with cancer risk, the lack of agreement shown here between repeat assessments of the same images by the same observers questions the reliability of using visual assessment to identify women at high risk or to detect moderate changes in breast density over time.

Visual assessment of breast density using Visual Analogue Scales: observer variability, reader attributes and reading time

Breast density is a strong risk factor for breast cancer and has potential use in breast cancer risk prediction, with subjective methods of density assessment providing a strong relationship with the development of breast cancer. This study aims to assess intra- and inter-observer variability in visual density assessment recorded on Visual Analogue Scales (VAS) among trained readers, and examine whether reader age, gender and experience are associated with assessed density. Eleven readers estimated the breast density of 120 mammograms on two occasions 3 years apart using VAS. Intra- and inter-observer agreement was assessed with Intraclass Correlation Coefficient (ICC) and variation between readers visualised on Bland-Altman plots. The mean scores of all mammograms per reader were used to analyse the effect of reader attributes on assessed density. Excellent intra-observer agreement (ICC>0.80) was found in the majority of the readers. All but one reader had a mean difference of <10 percentage points from the first to the second reading. Inter-observer agreement was excellent for consistency (ICC 0.82) and substantial for absolute agreement (ICC 0.69). However, the 95% limits of agreement for pairwise differences were -6.8 to 15.7 at the narrowest and 0.8 to 62.3 at the widest. No significant association was found between assessed density and reader age, experience or gender, or with reading time. Overall, the readers were consistent in their scores, although some large variations were observed. Reader evaluation and targeted training may alleviate this problem.

Factors Affecting Agreement between Breast Density Assessment Using Volumetric Methods and Visual Analogue Scales

Mammographic density in digital mammograms can be assessed visually or using automated volumetric methods; the aim in both cases is to identify women at greater risk of developing breast cancer, and those for whom mammography is less sensitive. Ideally all methods should identify the same women as having high density, but this is not the case in practice. 6422 women were ranked from the highest to lowest density by three methods: QuantraTM, VolparaTM and visual assessment recorded on Visual Analogue Scales. For each pair of methods the 20 cases with the greatest agreement in rank were compared with the 20 with the least agreement. The presence of microcalcifications, skin folds, suboptimally positioned inframammary folds, and whether or not the nipple was in profile were found to affect agreement between methods (p<0.05). Careful positioning during mammographic imaging should reduce discrepancy, but a greater understanding of the relationship between methods is also required.

PB.17: Inter-observer agreement in visual analogue scale assessment of percentage breast density

Breast density is an important risk factor for breast cancer. Assessment of density at screening could help identify women at increased risk of cancer, who may benefit from screening with shorter intervals or different modalities. Visual analogue scale (VAS) assessment of percentage density by observers is straightforward to implement and strongly associated with cancer risk. However, using VAS assessment for stratification would require reproducibility between observers. We examine agreement between observers assessing VAS density.

False-negative MRI breast screening in high-risk women

AIM To determine the frequency of and reasons for false-negative breast magnetic resonance imaging (MRI) examinations in high-risk women undergoing annual screening. MATERIALS AND METHODS The family history clinic database was interrogated and women at high risk of breast cancer who had undergone screening MRI and been diagnosed with breast cancer within 2 years of the MRI examination were identified. All available MRI examinations were reviewed and classified by two radiologists. RESULTS Of 32 women diagnosed with breast cancer, 23 had MRI images available for review. Fourteen were diagnosed at MRI, four at interim mammography, two symptomatically, one incidentally on ultrasound, and two at risk-reducing mastectomy. Ten women (43%) had potentially avoidable delays in diagnosis. The preceding MRIs were classified as false-negative screens in five women (one prevalent, four incident), false-negative assessment in seven and minimal signs in three (three women were assigned dual classifications). Common reasons for diagnostic delay included small enhancing masses that were overlooked, areas of non-mass enhancement that showed little or no change between screens, false reassurance from normal conventional imaging at assessment, and overreliance on short-interval repeat MRI. CONCLUSION Small enhancing foci, masses, and areas of segmental non-mass enhancement are common MRI features of early breast cancer. Lack of change of non-mass enhancement on serial examinations does not exclude malignancy. Double reading of both screening and assessment examinations is recommended. Ready access to MRI biopsy is essential. Short-interval repeat MRI should be limited to reassessing low suspicion areas likely to be benign glandular enhancement. Annual mammography remains important in these women.

Reader performance in visual assessment of breast density using visual analogue scales: are some readers more predictive of breast cancer?

Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

The impact of using weight estimated from mammographic images vs. self-reported weight on breast cancer risk calculation

Personalised breast screening requires assessment of individual risk of breast cancer, of which one contributory factor is weight. Self-reported weight has been used for this purpose, but may be unreliable. We explore the use of volume of fat in the breast, measured from digital mammograms. Volumetric breast density measurements were used to determine the volume of fat in the breasts of 40,431 women taking part in the Predicting Risk Of Cancer At Screening (PROCAS) study. Tyrer-Cuzick risk using self-reported weight was calculated for each woman. Weight was also estimated from the relationship between self-reported weight and breast fat volume in the cohort, and used to re-calculate Tyrer-Cuzick risk. Women were assigned to risk categories according to 10 year risk (below average <2%, average 2-3.49%, above average 3.5-4.99%, moderate 5-7.99%, high ≥8%) and the original and re-calculated Tyrer-Cuzick risks were compared. Of the 716 women diagnosed with breast cancer during the study, 15 (2.1%) moved into a lower risk category, and 37 (5.2%) moved into a higher category when using weight estimated from breast fat volume. Of the 39,715 women without a cancer diagnosis, 1009 (2.5%) moved into a lower risk category, and 1721 (4.3%) into a higher risk category. The majority of changes were between below average and average risk categories (38.5% of those with a cancer diagnosis, and 34.6% of those without). No individual moved more than one risk group. Automated breast fat measures may provide a suitable alternative to self-reported weight for risk assessment in personalized screening.

Does the prediction of breast cancer improve using a combination of mammographic density measures compared to individual measures alone

High mammographic density is associated with an increased risk of breast cancer, however whether the association is stronger when there is agreement across measures is unclear. This study investigates whether a combination of density measures is a better predictor of breast cancer risk than individual methods alone. Women recruited to the Predicting Risk of Cancer At Screening (PROCAS) study and with mammographic density assessed using three different methods were included (n=33,304). Density was assessed visually using Visual Analogue Scales (VAS) and by two fully automated methods, Quantra and Volpara. Percentage breast density was divided into (high, medium and low) and combinations of measures were used to further categorise individuals (e.g. ‘all high’). A total of 667 breast cancers were identified and logistic regression was used to determine the relationship between breast density and breast cancer risk. In total, 44% of individuals were in the same tertile for all three measures, 8.6% were in non-adjacent (high and low) or mixed categories (high, medium and low). For individual methods the strongest association with breast cancer risk was for medium and high tertiles of VAS with odds ratios (OR) adjusted for age and BMI of 1.63 (95% CI 1.31-2.03) and 2.33 (1.87-2.90) respectively. For the combination of density methods the strongest association was for ‘all high’ (OR 2.42, 1.77-3.31) followed by “two high” (OR 1.90, 1.35-3.31) and “two medium” (OR 1.88, 1.40-2.52). Combining density measures did not affect the magnitude of risk compared to using individual methods.

Mammographic Density Over Time in Women With and Without Breast Cancer

This study compared mammographic density over time between women who developed breast cancer cases and women who did not controls. Cases had an initial negative mammographic screen and another three years later when cancer was diagnosed. Cases were matched to three controls with two successive negative screens by age, year of mammogram, BMI, parity, menopausal status and HRT use. Mammographic density was measured by VolparaTM. There was a significant reduction in percentage density in the affected breast for cases 5.2 to 4.8i?ź%, pi?ź<i?ź0.001 and for the same matched breast in controls 4.9 to 4.5, pi?ź<i?ź0.001. Similar results were found for the unaffected breast. After adjusting for density measures at the initial screen, case-control status was only significantly associated with fibroglandular volume in the unaffected breast adjusted mean 45.8i?źcm3 in cases, 44.0i?źcm3 in controls, pi?ź=i?ź0.008. The results suggest changes in mammographic density may be less important than initial mammographic density.