Test-set training is linked to increased breast screening cancer detection rates

Basel A. Qenam; Tong Li; Patrick C. Brennan

doi:10.1117/12.2611621

4 April 2022 Test-set training is linked to increased breast screening cancer detection rates

Basel A. Qenam, Tong Li, Patrick C. Brennan

Proceedings Volume 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment; 120350S (2022) https://doi.org/10.1117/12.2611621
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Mammographic test sets are a simulation-based training methodology for radiologists to assess and improve their performance. However, while test-set records have indicated over-time improvements in participants' performance within the tests, little is known about how those improvements translate into breast-screening readers’ performance in the clinic. This study investigated how the performance of readers who completed test-set training in the BreastScreen Reader Assessment Strategy (BREAST) platform have evolved in comparison to readers who have no history of test-set participation. Investigating 10-year clinical audit data of 46 breast screening readers in New South Wales, Australia indicated that BREAST readers improved their positive predictive value (PPV) (p=0.001) in association with their testset participation. They also had higher detection rates for invasive cancers (p=0.01), ductal carcinoma in situ (DCIS) (p=0.03), and the detection rate of all cancers and DCIS (p=0.01). In comparison, non-BREAST readers improved their recall rate in subsequent screens (p=0.03) and PPV (p=0.02). In conclusion, test-set participation is linked to enhanced capability of cancer detection, which can be due to the high proportion of cancer cases in the test sets in comparison to normal practice.

Conference Presentation

Citation Download Citation

Basel A. Qenam, Tong Li, and Patrick C. Brennan "Test-set training is linked to increased breast screening cancer detection rates", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 120350S (4 April 2022); https://doi.org/10.1117/12.2611621

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