Cross-digitizer robustness of a knowledge-based CAD system for mass detection in mammograms

Multiplatform application of CAD systems in mammography is often limited due to image preprocessing steps that are tailored to the acquisition protocol such as the digitizer. The purpose of this study was to validate our knowledge-based CAD system across two different digitizers. Our system relies on the similarity of a query image with known cases stored in a knowledge database. Image similarity is assessed using information theory, without any image preprocessing. Therefore, we hypothesize that our CAD system can operate robustly across digitizers. We tested the hypothesis using two different datasets of mammographic regions of interest (ROIs) for mass detection. The two databases consisted of 1,820 and 1,809 ROIs extracted from DDSM mammograms digitized using a Lumisys and a Howtek scanner respectively. Three experiments were performed. First, we evaluated the CAD system on each dataset independently. Then, we evaluated the system on each dataset when the other dataset was used as the knowledge database. Finally, we assessed the CAD detection performance when the knowledge database contained mixed cases. Our CAD system had similar performance across digitizers (A _z=O.87±0. 01 for Lumisys vs. A _z=0.86±0.01 for Howtek) when assessed independently. When the system was tested on one dataset while the other was used as the knowledge database, ROC performance declined marginally, mainly based on the partial ROC area index. This result suggests that blind translation of the system without some experience with cases digitized with the same digitizer is not recommended when the system is expected to operate at high sensitivity decision thresholds. When the system operated with a knowledge database of mixed cases, its performance across digitizers was robust yet slightly inferior to what observed independently.