PURPOSE
In this study we evaluated the potential of a computer system to select exams with low likelihood of
containing cancer.
METHOD AND MATERIALS
We collected a representative set of 1649 referrals with different screening outcome from the Dutch
breast cancer screening. The dataset comprised 489 true positives (TP) exams and 1160 false
positive (FP) exams. In addition, we collected 1000 true negative (TN) exams from the same
screening population. All exams were automatically analyzed with Transpara v1.2.0 (ScreenPoint
Medical, Nijmegen, The Netherlands). Transpara uses deep learning algorithms to, based on
soft-tissue lesions and calcifications findings, categorize every mammogram on a 10-point scale. This
computerized score represents the likelihood that a cancer is present in the exam at hand, where 10
represents the highest likelihood that a cancer is present. It is defined in such a way that, in a
screening setting, the number of mammograms in each category is roughly equal.
In this study, we determined the distribution of the computerized cancer likelihood scores for the TP