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

• Published in: Cancer research (Chicago, Ill.) Ill.), 2021-02, Vol.81 (4_Supplement), p.PD6-03-PD6-03
• Main Authors: Hanna, Matthew G, Raciti, Patricia, Godrich, Ran, Casson, Adam, Viret, Julian, Lee, Donghun, Lee, Matthew, Bozkurt, Alican, Sue, Jillian, Dogdas, Belma, Rothrock, Brandon, Grady, Leo, Kanan, Christopher, Fuchs, Thomas
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.SABCS20-PD6-03

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 (H&E) stained whole slide images (WSIs) of glass slides. The system is a binary classifier, intended to draw a pathologist’s attention to concerning features. Concerning features (the positive category) consisted of invasive breast cancer, in situ breast cancer, and various forms of atypia (atypical ductal hyperplasia, atypical lobular hyperplasia, etc. ). The deep learning system is based on the method proposed in Campanella et al. (2019). It learns directly from diagnosis using multiple instance learning, without the need for pixel-wise annotations. Paige Breast Alpha was trained on 17354 images from 3378 patients, and was assessed on 7921 images from 2443 patients. All slides were scanned on a Leica Aperio AT2. Results: For detecting invasive or in situ cancer at the part level, the system achieved an overall sensitivity of 97.3% and a specificity of 98.0% in biopsies and 96.1% sensitivity and 91.5% specificity in excisions. Each part had between 1—10 slides. Conclusions: We hypothesized that a machine learning system trained to detect predefined types of breast cancer and pre-cancerous lesions could be applied to a range of breast biopsy and resection WSIs to detect for the presence of these lesions. Herein we showed that the presence of these predefined features can be detected with high accuracy. Future studies are being initiated to assess the potential benefits of such a system when used by a pathologist. Additional systems are under development that wo