Improving DCIS diagnosis and predictive outcome by applying artificial intelligence

Abstract

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.

Keywords: Breast cancer; DCIS; Image analysis; Pathology.

Fig. 1.

AI approaches to improve prediction of DCIS progression. a, A schematic depicting normal breast, ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC). b, Overview of quantitative histological approaches that utilize hematoxylin and eosin (H&E)-stained slides. These may be used to classify tissue features or segment cells and nuclei within tumor and stromal regions. c, Cartoon depiction of features that may be extracted from multiplex analysis of tissues, including; cell phenotypes, cell-relationships and cell-neighborhoods. d, Schematic to show the types of collagen features extracted from second harmonic generation (SHG) images, such as collagen density and collagen fiber diameter, and how these stromal features may change with distance from DCIS lesions. e, Workflow to reflect that clinical features and omics data (genomics, transcriptomics, epigenomics) may be incorporated with these AI image-based findings to develop a classifier that could stratify women with DCIS into low- and high-risk groups that would determine their clinical management.