Computer-aided detection for architectural distortion: a comparison of digital breast tomosynthesis and digital mammography

Architectural distortion (AD) is one of the breast abnormal signs in digital breast tomosynthesis (DBT) and digital mammography (DM). It is hard to be detected because of its subtle appearance and similar intensity with surrounding tissue. Since DBT is a three-dimensional imaging, it can address the problem of tissue superimposition in DM, so as to reduce false positives and false negatives. Several clinical studies have confirmed that radiologists can detect more ADs in DBT than in DM. These conclusions are based on subjective experience. To explore whether the engineering model and the experience of radiologists are consistent in AD detection tasks, this study compared the AD detection performance of a deep-learning-based computer-aided detection (CADe) model in DBT and DM images of the same group of cases. 394 DBT volumes and their corresponding DM images were collected retrospectively from 99 breast cancer screening cases. Among them, 203 DBT volumes and DM images contained ADs and the remaining 191 ones were negative group without any AD. Ten-fold cross-validation was used to train and evaluate the models and mean true positive fraction (MTPF) was used as figure-of-merit. The results showed that the CADe model achieved significantly better detection performance in DBT than DM (MTPF: 0.7026±0.0394 for DBT vs. 0.5870±0.0407 for DM, p=0.002). Qualitative analysis illustrated that DBT indeed had the ability to overcome tissue superimposition and showed more details of breast tissue. It helped the CADe model detect more ADs, which was consistent with clinical experience.