Abstract PD6-03: Clinical-grade detection of breast cancer in biopsies and excisions using machine learning

Background: Pathologists reviewing breast tissue slides must identify the presence of many salient features within each slide, including invasive and in situ breast cancer as well as various forms of atypia. In breast pathology particularly, the large volume of slides poses significant challenges for workload management and pathologist productivity (Johnson et al. 2019). The shift to a digital workflow in pathology, augmented by machine learning algorithms, has the potential to increase the efficiency, and productivity of pathologists by identifying cancer and pre-cancerous lesions in digitized slides. While there has been extensive work using machine learning algorithms to detect breast cancer metastasis in lymph nodes (Liu et al. 2018; Steiner et al. 2018), almost no research has been done on using such systems to detect breast cancer in biopsies and excisions. Methods: We created and assessed Paige Breast Alpha, a machine learning system for the detection of breast cancer in hematoxylin and eosin (HE 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-03.