Workload reduction
Performance benchmarking

ECR 2020

15 July 2020


Mann R et al

Reading breast tomosynthesis examinations with an AI decision support system: improving cancer detection accuracy

Mann R, Rodriguez-Ruiz A, Gubern-Merida A, Karssemeijer N, Sechopoulos I.


To compare the breast cancer detection accuracy of radiologists reading breast tomosynthesis (DBT) examinations unaided versus supported by an artificial intelligence (AI) system.


A cancer-enriched, retrospective, fully-crossed, multi-reader, multicase, HIPAA-compliant study was performed. Four-view DBT examinations from 240 women were included. All examinations (in total 71 breasts with cancer lesions, 70 breasts with benign findings, and 339 normal breasts) were interpreted by 9 qualified radiologists (median experience 8 years, range 4-23), once with and once without AI support (Transpara, ScreenPoint Medical). The readers provided a level of suspicion for each breast. When using AI support, radiologists were shown an examination-based cancer likelihood score as well as marked lesions and lesion-based cancer likelihood scores. The area under the receiver operating characteristic curve (AUC) was compared between both reading conditions at a per breast-level, using mixed-models analysis of variance for multiple repeated clustered measurements. Reading time differences for normal examinations were also measured.


On average, the AUC was higher with AI support than with unaided reading (0.861 vs 0.820, respectively; P = .001). The AUC of the stand-alone AI system was similar to the average AUC of the radiologists unaided (0.840 vs 0.820, 95% CI: -0.038, +0.078). Reading time per normal case showed an average reduction of -21% when using AI, with 8 of 9 readers having shorter reading time.


Radiologists improved their cancer detection in breast tomosynthesis examinations when using an AI system for support, while simultaneously reducing reading time.

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