Applying radiomics-based risk prediction models from digital mammography to digital breast tomosynthesis: a preliminary reliability survey

Abstract

Aim: This project is part of a long-term goal to apply radiomics-based risk prediction models designed for twodimensional (2D) digital mammography (DM) to three-dimensional (3D) digital breast tomosynthesis (DBT), using either the DBT projection views (PV) or the reconstructed planes. In this work, 2 fundamental aspects related to PVs were explored: (1) finding robust radiomic features for both DM and PV, and (2) selecting robust and informative radiomic features for both 2D and 3D modalities by requiring respectively invariance and noninvariance of these features across DBT projections. Methods: DM and PVs from combined DM and DBT acquisitions of phantom and patients were used in this study. Robust radiomic features in these images were identified by the intra-class correlation coefficient (ICC) between DM and the central PV for DBT. Then, projection invariant and noninvariant radiomic features of PVs for different projection angles were also characterized by ICC. Finally, selected projection invariant features of PVs were applied on a DM breast density classifier and their predictive power was compared to the results of DM. Results: A total of 70 out of 93 extracted radiomic features (75%) showed at least moderate reliability (ICC>0.5) between DM and the central PV. In addition, a decrease of feature reliability along increasing angular range was observed on both real and simulated datasets. With projection angle invariance as the feature selection method, overfitting of a DM density classifier was reduced. Conclusions: A large portion of radiomic features was robust between DM and the central PV without specific harmonization, suggesting that some parts of the radiomic features of DM can be applied to the DBT projection dataset. Additionally, 3D DBT could also benefit 2D DM through the projection angle variation test. Projectioninvariant features with better robustness could be selected for 2D DM which was preliminary validated by a density classification task, while projection non-invariant features which incorporate 3D information in the PVs may be suitable for 3D DBT.