Improving DCIS diagnosis and predictive outcome by applying artificial intelligence

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that is considered to be a precursor to invasive breast cancer. Nevertheless, not all DCIS will progress to invasion. Current histopathological classification systems are unable to predict which cases will or will not progress, and therefore many women with DCIS may be overtreated. Artificial intelligence (AI) image-based analysis methods have potential to identify and analyze novel features that may facilitate tumor identification, prediction of disease outcome and response to treatment. Indeed, these methods prove promising for accurately identifying DCIS lesions, and show potential clinical utility in the therapeutic stratification of DCIS patients. Here, we review how AI techniques in histopathology may aid diagnosis and clinical decisions in regards to DCIS, and how such techniques could be incorporated into clinical practice.

Automated summary

Breast ductal carcinoma in situ (DCIS) is a preinvasive lesion that can potentially progress to invasive breast cancer, but not all cases do so. Current histopathological classification systems cannot reliably predict progression, resulting in potential overtreatment of women with DCIS. AI image-based analysis methods show promise in accurately identifying and potentially stratifying treatment for DCIS patients.