Personalised breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence

Dahlblom V, Tingberg A, Zackrisson S, Dustler M
Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115130C (22 May 2020); doi: 10.1117/12.2564344

Aim and Objective
To investigate whether Transpara®, a deep learning-based artificial intelligence (AI) decision support system, could be used to analyse DM and select highly suspicious cases that would benefit from additional DBT imaging at the same screening occasion.

Materials and Method
This study used data from Malmö Breast Tomosynthesis Screening Trial, where all women prospectively were examined with separately read DM and DBT. We retrospectively analysed DM examinations (n=14768) with Transpara (version 1.4.0), which assigned a 1-10 score to each screening exam denoting the likelihood of cancer/risk score. We tested how different score thresholds for adding DBT to an initial DM affects the number of detected cancers, additional DBT examinations needed, detection rate, and false positives.

Results
– Using Transpara Exam Score 10 to determine which women would benefit from DBT, 25 (26 %) more cancers would be detected compared to using DM alone.
– Of the 41 cancers only detected on DBT, 61 % would be detected, with only 1797 (12%) of the women examined with both DM and DBT.
– The detection rate for the added DBT would be 14/1000 women, while the false positive recalls would be increased with 58 (21 %).

Conclusion
Using DBT only for selected highly suspicious cases in mammography could be an alternative to a complete DBT screening.