Increase in perceived case suspiciousness due to local contrast optimisation in digital screening mammography

Item does not contain fulltextOBJECTIVES: To determine the influence of local contrast optimisation on diagnostic accuracy and perceived suspiciousness of digital screening mammograms. METHODS: Data were collected from a screening region in the Netherlands and consisted of 263 digital screening cases (153 recalled,110 normal). Each case was available twice, once processed with a tissue equalisation (TE) algorithm and once with local contrast optimisation (PV). All cases had digitised previous mammograms. For both algorithms, the probability of malignancy of each finding was scored independently by six screening radiologists. Perceived case suspiciousness was defined as the highest probability of malignancy of all findings of a radiologist within a case. Differences in diagnostic accuracy of the processing algorithms were analysed by comparing the areas under the receiver operating characteristic curves (A(z)). Differences in perceived case suspiciousness were analysed using sign tests. RESULTS: There was no significant difference in A(z) (TE: 0.909, PV 0.917, P = 0.46). For all radiologists, perceived case suspiciousness using PV was higher than using TE more often than vice versa (ratio: 1.14-2.12). This was significant (P <0.0083) for four radiologists. CONCLUSIONS: Optimisation of local contrast by image processing may increase perceived case suspiciousness, while diagnostic accuracy may remain similar. KEY POINTS: Variations among different image processing algorithms for digital screening mammography are large. Current algorithms still aim for optimal local contrast with a low dynamic range. Although optimisation of contrast may increase sensitivity, diagnostic accuracy is probably unchanged. Increased local contrast may render both normal and abnormal structures more conspicuous.1 april 201

Comparing Visually Assessed BI-RADS Breast Density and Automated Volumetric Breast Density Software: A Cross-Sectional Study in a Breast Cancer Screening Setting

<div><p>Introduction</p><p>The objective of this study is to compare different methods for measuring breast density, both visual assessments and automated volumetric density, in a breast cancer screening setting. These measures could potentially be implemented in future screening programmes, in the context of personalised screening or screening evaluation.</p><p>Materials and Methods</p><p>Digital mammographic exams (N = 992) of women participating in the Dutch breast cancer screening programme (age 50–75y) in 2013 were included. Breast density was measured in three different ways: BI-RADS density (5^th edition) and with two commercially available automated software programs (Quantra and Volpara volumetric density). BI-RADS density (ordinal scale) was assessed by three radiologists. Quantra (v1.3) and Volpara (v1.5.0) provide continuous estimates. Different comparison methods were used, including Bland-Altman plots and correlation coefficients (e.g., intraclass correlation coefficient [ICC]).</p><p>Results</p><p>Based on the BI-RADS classification, 40.8% of the women had ‘heterogeneously or extremely dense’ breasts. The median volumetric percent density was 12.1% (IQR: 9.6–16.5) for Quantra, which was higher than the Volpara estimate (median 6.6%, IQR: 4.4–10.9). The mean difference between Quantra and Volpara was 5.19% (95% CI: 5.04–5.34) (ICC: 0.64). There was a clear increase in volumetric percent dense volume as BI-RADS density increased. The highest accuracy for predicting the presence of BI-RADS c+d (heterogeneously or extremely dense) was observed with a cut-off value of 8.0% for Volpara and 13.8% for Quantra.</p><p>Conclusion</p><p>Although there was no perfect agreement, there appeared to be a strong association between all three measures. Both volumetric density measures seem to be usable in breast cancer screening programmes, provided that the required data flow can be realized.</p></div

Bland-Altman plots comparing Quantra and Volpara absolute dense volume (a) and percent dense volume (b).

<p>Bland-Altman plots comparing Quantra and Volpara absolute dense volume (a) and percent dense volume (b).</p

Association between BI-RADS density measures and volumetric density in other studies.^a

<p>SD = standard deviation, IQR = inter-quartile range</p><p>^a The results in italics were not published, but were based on estimates from the published boxplots.</p><p>^b Values were rounded off.</p><p>^c Also presented means, which were: 12.2 for BI-RADS 1, 18.0 for BI-RADS 2, 28.5 for BI-RADS 3, and 34.5 for BI-RADS 4.</p><p>Association between BI-RADS density measures and volumetric density in other studies.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136667#t005fn002" target="_blank">^a</a></p

Volumetric breast density estimates in overall population (n = 992).

<p>IQR = inter-quartile range</p><p>Volumetric breast density estimates in overall population (n = 992).</p

BI-RADS density scores: inter-rater agreement and reliability (n = 992).

<p>ᴋ_w = weighted kappa scores (Fleiss-Cohen, quadratic weights), CI = confidence interval</p><p>BI-RADS density scores: inter-rater agreement and reliability (n = 992).</p

Percent dense volume and absolute dense volume by BI-RADS density category and by age group.

<p>IQR = inter-quartile range</p><p>Percent dense volume and absolute dense volume by BI-RADS density category and by age group.</p

BI-RADS density scores: intra-rater agreement and reliability (n = 992).

<p>ᴋ_w = weighted kappa scores (Fleiss-Cohen, quadratic weights)</p><p>^a With the exception of the last row, which consists of kappa values with 95% confidence intervals.</p><p>^b The overall classification is based on the agreement between all three radiologists. The three radiologists all agreed in 570 of the cases. The other values were based on at least two radiologists agreeing (n = 413) or the middle value (n = 9).</p><p>^c The % agreement and the κ_w values were based on a subset (n = 250) that was scored twice by each radiologist.</p><p>BI-RADS density scores: intra-rater agreement and reliability (n = 992).</p

Polytomous latent scales for the investigation of the ordering of items

We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering property may be important in, for example, intelligence testing and person-fit analysis. We derive observable properties of the three latent scales that can each be used to investigate in real data whether the particular model adequately describes the data. We also propose a methodology for analyzing test data in an effort to find support for a latent scale, and we use two real-data examples to illustrate the practical use of this methodology