Impact of Breast Reader Assessment Strategy on mammographic radiologists' test reading performance

Suleiman, Wasfi; Rawashdeh, Mohammad; Lewis, Sarah; McEntee, Mark F.; Lee, Warwick; Tapia, Kriscia; Brennan, Patrick C.

doi:10.1111/1754-9485.12461

Cited by 32 publications

(17 citation statements)

References 28 publications

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“…The image test set used was the Sydney test set, which is part of the BreastScreen Reader Assessment Strategy (BREAST) . The Sydney test set contains 20 biopsy‐proved malignancies, including ductal carcinomas in situ and invasive cancers; and 40 normal and benign cases, including calcified duct ectasia, simple cysts, fibroadenomas and intra‐mammary lymph nodes.…”

Section: Methodsmentioning

confidence: 99%

Career caseload predicts interobserver agreement on the final classification of a mammogram

et al. 2019

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Introduction: Differences in radiologists' experience can potentially introduce interobserver variability in reading mammograms. This work investigated the effect of radiologists' experience on agreement on mammographic final classification. Methods: This was a cross-sectional study. Seventeen radiologists were asked to provide their final impression on 60 mammogram cases. Experience parameters included breast subspecialty, years reading mammograms, cases read per year and career caseload. Career caseload was calculated by multiplying years reading mammograms by the average number of cases read per year. The interobserver agreement was calculated using Cohen kappa (j). The difference in j between radiologists' groups was compared using the independent-sample t-test and analysis of variance. Results: The average interobserver agreement was 0.25 (fair). A small difference was found in favour of breast radiologists against general radiologists (j = 0.21 and 0.29, respectively, P = 0.019). Years reading mammograms and cases read per year did not seem to significantly affect the interobserver agreement (P = 0.056 and 0.273 respectively). Radiologist who had career caseload of at least 2500 cases showed significantly higher consistency than those who read less. j for radiologists who had career caseload of 2500-4000 cases and >4000 cases was 0.33 and 0.28, respectively, whereas for <2500 j was 0.17 (P = 0.001). Conclusion: A fair level of interobserver agreement on the final classification of a mammogram was demonstrated. Career caseload was the most important experience parameter to associate with the interobserver agreement. Training strategies aiming to increase radiologists' career caseload may be beneficial.

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Section: Methodsmentioning

confidence: 99%

Career caseload predicts interobserver agreement on the final classification of a mammogram

et al. 2019

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“…It assesses the performance of clinicians on enriched sets of 60 digital mammography cases via a web-based application. 15,16 Due to the large number of participants and data generated by BREAST, it provides an opportunity to validate the effect of breast density on cancer detection. The aim of this work is to utilize BREAST-generated data to investigate the effect of breast density on the diagnostic efficacy of screening radiologists reading digital mammograms.…”

Section: Introductionmentioning

confidence: 99%

Impact of breast density on cancer detection: observations from digital mammography test sets

Tapia¹,

Rickard²,

McEntee³

et al. 2020

IJRRT

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Introduction: The aim of this study is to investigate the impact of breast density on the diagnostic efficacy of 273 breast screening radiologists reading 1 of 5 test sets of digital mammograms within the BREAST program. Methods: Retrospective data was collected from two hundred and seventy-three breast screening radiologists who participated in BREAST test sets between 2012 and 2017. Radiologists reviewed one of five test sets (labeled T1-T5) each containing 60 digital mammographic cases with 20 cancers and 40 normal cases. The cases also had varying mammographic densities based on BI-RADS density ratings. Cases were grouped into lowdensity (LD) and high-density (HD), and sensitivity, lesion sensitivity, specificity, receiver operating characteristic (ROC) and jackknifing free response operating characteristic (JAFROC) figures of merit were compared using Mann-Whitney U or unpaired t-tests. Results: Readers in three out of five test sets showed better case sensitivity and lesion sensitivity in LD compared with HD cases (T2, T4, and T5: P≤0.001). One out of five test sets showed the same trend with specificity (T4: P<0.0001) and another set followed this pattern for ROC (T5: P<0.0001) and JAFROC (T5: P<0.0001) values. One test set (T3) demonstrated better performance in HD versus LD cases (ROC P<0.0001 and JAFROC P<0.0001). Reader experience, training, and set features may account for the variation in the results between the test sets. Conclusion: Overall, radiologists taking the BREAST test sets perform better on cases with low mammographic density thus highlighting diagnostic advantage for women with lower breast density and reiterating the challenges presented by mammograms of dense breasts.

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“…For example, the BREAST test assesses BreastScreen radiologists' ability to correctly identify cases from mixed case-control test sets, with radiologists scored for peer comparison and able to review the accuracy of their own responses. This has been shown to improve screen-readers' performance on test sets 22 to a degree that may improve overall screening program performance.…”

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confidence: 99%

Improving breast cancer screening in Australia: a public health perspective

Nickson¹,

Velentzis²,

Brennan³

et al. 2019

Public Health Res Pract

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There are currently no single disruptors to breast cancer screening akin to the impact of human papillomavirus testing and vaccination on cervical cancer screening. However, there is a groundswell of interest to review the BreastScreen Australia program to consider more risk-based screening protocols and to establish whether to routinely inform women about their breast density. We propose a framework for a considered, evidence-based review.Population-level effectiveness of breast cancer screening is ultimately measured through its impact on breast cancer mortality, and this has been realised in Australia. Effectiveness can also be measured through treatment intensity, estimated overdiagnosis, false-positive screens and health economics measures. Key levers to improve such population-level outcomes include screening participation, screening test sensitivity and specificity, risk assessment and screening protocols.The proposed framework assesses screening effectiveness in the era of personalised medicine, allows review of multiple factors that may together warrant change, and gives full, evidence-based consideration of the benefits, harms and costs of various approaches to breast cancer screening. To be effective, the framework requires a coordinated approach to generating the evidence required for policy makers, with time to prepare appropriate health services.

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Impact of Breast Reader Assessment Strategy on mammographic radiologists' test reading performance

Cited by 32 publications

References 28 publications

Career caseload predicts interobserver agreement on the final classification of a mammogram

Career caseload predicts interobserver agreement on the final classification of a mammogram

Impact of breast density on cancer detection: observations from digital mammography test sets

Improving breast cancer screening in Australia: a public health perspective

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