Longitudinal in silico imaging study comparing digital mammography and digital breast tomosynthesis systems

In silico clinical trials are becoming more sophisticated and allow for realistic assessment and comparisons of medical image system models. These fully computational models enable fast and affordable trial designs that can closely capture trends seen on real clinical trials.

To evaluate three breast imaging system models for digital mammography (DM) and digital breast tomosynthesis (DBT) in a fully-in-silico longitudinal study.

We developed in silico models for three different breast imaging systems by modeling relevant characteristics such as detector technology, pixel size, number of projections, and angular span. We use a computational image reader to detect masses at different growing stages to compute the relative system performance. Similarly, we compare calcification cluster detectability across systems. The Detectability area under the ROC curve (AUC) was calculated for each combination of breast density, device model, lesion size and type, and search area. We compared the absolute and relative AUC values for DM and DBT. The trial consisted of 45 000 simulated images corresponding to 750 virtual digital patient models.

We observed proportional AUC values with increasing mass size. On the other hand, higher breast densities showed lower AUC values. For masses, we found significant performance differences between device models. The highest average AUC difference between DBT and DM was 0.109, benefiting DBT. For calcifications, DM showed higher performance than DBT, especially in highly dense breasts. The highest AUC difference on a model was -0.055, benefiting DM.

In this fully-in-silico imaging trial, we compared three imaging systems with different detector technologies on the same cohort of virtual digital patient models. We found that breast device systems can lead to visibility differences in masses and calcifications. Our longitudinal, multi-device in silico study was possible because of the versatility and flexibility of in silico methods. This study shows the advantages of this in silico methodology in lowering the resources needed for device development, optimization, and regulatory evaluation.