Digital breast tomosynthesis (DBT) is an emerging imaging modality for improved breast cancer detection and diagnosis [1-5]. Numerous efforts have been made to find quantitative metrics associated with mammographic image quality assessment, such as the exponent β of anatomical noise power spectrum, glandularity, contrast noise ratio, etc. [6-8]. In addition, with the use of Fourier-domain detectability for a task-based assessment of DBT, a stationarity assumption on reconstructed image statistics was often made [9-11], resulting in the use of multiple regions-of-interest (ROIs) from different locations in order to increase sample size. While all these metrics provide some information on mammographic image characteristics and signal detection, the relationship between these metrics and detectability in DBT evaluation has not been fully understood. In this work, we investigated spatial-domain detectability trends and levels as a function of the number of slices Ns at three different ROI locations on the same image slice, where background statistics differ in terms of the aforementioned metrics. Detectabilities for the three ROI locations were calculated using multi-slice channelized Hotelling observers with 2D/3D Laguerre-Gauss channels. Our simulation results show that detectability levels and trends as a function of Ns vary across these three ROI locations. They also show that the exponent β, mean glandularity, and mean attenuation coefficient vary across the three ROI locations but they do not necessarily predict the ranking of detectability levels and trends across these ROI locations.