Retrospective multi-reader evaluation of a Deep-Learning-based cancer detection model in Contrast-Enhanced Mammography
Amr Farouk Ibrahim Moustafa, Clement Jailin, Laurence Vancamberg, Sara Mohamed, Pablo Milioni De Carvalho, Razvan Iordache, Eman Faker Kamal, Mohammed Mohammed Mohammed Gomaa, Rasha Mohammed KamalAbstract
Objectives
To evaluate the clinical impact of a deep-learning-based computer-aided diagnostic tool applied to contrast-enhanced mammography on radiologists’ diagnostic accuracy and reading efficiency.
Methods
In this retrospective single-centre study, 150 contrast-enhanced mammography examinations were interpreted by five radiologists. Each case was read and annotated twice in two sequential sessions: first without assistance and then with the computer-aided diagnostic tool, with a minimum 4-week washout period. Diagnostic performance was assessed at lesion and breast levels using free-response receiver operating characteristic analysis, area under the curve, and false-positive rate. Reading time for one radiologist was recorded as an exploratory measure.
Results
The computer-aided diagnostic tool was associated with improved lesion-level performance for most radiologists, with a mean increase in the area under the free-response curve of 0.030 ± 0.018. At 89% sensitivity, false-positive findings decreased by an average of 11.4% ± 24% across all radiologists. No improvement was observed at the breast level, possibly related to the tool’s single-view analysis and dataset characteristics. An exploratory analysis showed a 31% reduction in reading time for one radiologist.
Conclusion
Integration of a deep learning–based computer-aided diagnostic tool into contrast-enhanced mammography was associated with improved lesion-level diagnostic performance and reduced false-positive findings, without measurable improvement at the breast level.
Advances in knowledge
To our knowledge, this study provides one of the early clinical evaluations of a deep learning–based computer-aided diagnostic tool for contrast-enhanced mammography, suggesting potential improvements in lesion-level diagnostic performance and workflow efficiency while highlighting current limitations at the breast level.