Performance benchmarking

Breast Cancer

12 February 2020


Michiro Sasaki et al

Artificial intelligence for breast cancer detection in mammography: experience of use of the ScreenPoint Medical Transpara system in 310 Japanese women

Michiro Sasaki, Mitsuhiro Tozaki, Alejandro Rodríguez-Ruiz, Daisuke Yotsumoto, Yumi Ichiki, Aiko Terawaki, Shunichi Oosako, Yasuaki Sagara, Yoshiaki Sagara


To compare the breast cancer detection performance in digital mammograms of a panel of three unaided human readers (HR) versus a stand-alone artificial intelligence (AI)-based Transpara system in a population of Japanese women.


The subjects were 310 Japanese female outpatients who underwent digital mammographic examinations between January 2018 and October 2018. A panel of three HR provided a Breast Imaging Reporting and Data System (BI-RADS) score, and Transpara system provided an interactive decision support score and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared under each of reading conditions.


The AUC was higher for human readers than with stand-alone Transpara system (human readers 0.816; Transpara system 0.706; difference 0.11; P < 0.001). The sensitivity of the unaided HR for diagnosis was 89% and specificity was 86%. The sensitivity of stand-alone Transpara system for cutoff scores of 4 and 7 were 93% and 85%, and specificities were 45% and 67%, respectively.

Key Finding:

Transpara showed a high performance in a study population of women with dense breasts

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