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Updated: Jun 3, 2025
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Traditional versus modern approaches to screening mammography: a comparison of computer-assisted detection for
Manisha Bahl1, Ashwini Kshirsagar2, Scott Pohlman2
1Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA. mbahl1@mgh.harvard.edu.
Purpose:
Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods:
Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.0, Hologic, Inc.) and an AI algorithm (Genius AI Detection v2.0, Hologic, Inc.). Synthetic 2D images were used for CADe analysis, and DBT images were used for AI analysis. For each algorithm, an overall case score was defined as the highest score of all lesion marks, which was used to determine the area under the receiver operating characteristic curve (AUC).
Results:
The overall AUC was higher for 3D AI than 2D CADe (0.873 versus 0.693, P < 0.001). Lesion-specific sensitivity of 3D AI was higher than 2D CADe (94.3 versus 72.6%, P = 0.002). Specificity of 3D AI was higher than 2D CADe (54.3 versus 16.7%, P < 0.001), and the rate of false marks on non-cancer cases was lower for 3D AI than 2D CADe (0.91 versus 3.24 per exam, P < 0.001).
Conclusion:
A deep learning-based AI algorithm applied to DBT images significantly outperformed a traditional machine learning CADe algorithm applied to synthetic 2D mammographic images, with regard to AUC, sensitivity, and specificity.
