Learning general cancer distribution: generalization of AI models to diagnostic images

Diagnostic mammography, conducted to assess symptoms or screen-detected lesions in women, often involves extra views beyond standard ones. Utilization of these additional views may vary across radiologists and healthcare settings. Overall, the aim of such a mammographic work-up is to provide extra imaging data, thus improving result accuracy. While artificial intelligence (AI) has demonstrated promising outcomes in cancer detection through mammographic screening, there remains a lack of evidence concerning its utilization in the diagnostic mammography context. This study aimed to investigate if using an AI-based model for diagnostic mammography could provide advantages beyond its use solely for screening mammograms. We applied an AI system, trained and validated on screening mammograms, to a dataset of diagnostic mammograms. Performance were compared to the same system applied to screening mammogram of the same patient. The findings indicate that the AI model performs similarly well when applied to non-standard views compared to standard digital mammograms. Specifically, the model demonstrates higher accuracy than the baseline and greater specificity at a given sensitivity level. This suggests that the model generalizes well on diagnostic mammograms. Understanding this generalization was important for comprehending the model's performance on diagnostic images and determining the feasibility of developing a specifically trained algorithm.