Abstract P6-04-16: ART: Automated Region segmentation of Tumor on HER2-stained breast cancer tissue
Authors: Kapil Ansh , Shumilov Anatoliy , Wortmann Philipp , Khelifa Sihem , Chan Jessica , Vandenberghe Michel , Barker Craig , Gustavson Mark , Carroll Danielle , Sade Hadassah , Schmidt Günter 
Published: March 2023

Abstract
Background Computational pathology-based methods, eg, Quantitative Continuous Scoring (QCS) [Gustavson, SABCS 2020], are built to provide objective and quantitative methods to assess HER2 expression in breast cancer (BC). For accurate HER2 quantification, it is important to exclude non-invasive epithelium from analysis since HER2 overexpression could be more frequent in ductal carcinoma in situ (DCIS) and pleomorphic lobular carcinoma in situ (PLCIS) than in invasive BC [Lari, J Cancer 2011]. Generally, computational pathology-based approaches require experts to delineate the invasive BC regions of interest for analysis and exclude all benign/non-invasive epithelium. Developing a tool that delineates the invasive BC regions automatically without human intervention to avoid subjectivity of manual annotation by pathologists is ideal. We developed a novel, deep-learning–based system, called DualScaleNet, to perform Automated Region segmentation of Tumor (ART) by automatically identifying the invasive BC regions and excluding benign/non-invasive epithelium on HER2-stained digitized images. Identification and diagnosis of these regions, especially the in situ tumors are a challenge as they can mimic benign and invasive lesions causing wrong HER2 evaluation. Additional stains (eg, p63 for myoepithelial cells or laminin for basement membrane) are often required for diagnosis [Pinder, Mod Pathol 2010] but were not available for this study. Methods DualScaleNet works simultaneously on HER2-stained immunohistochemistry (IHC) image patches at 2 different resolutions. The target branch uses a higher resolution RGB image (0.5 μm/pixel) to learn accurate local details; the context branch uses a lower resolution image (4.0 μm/pixel) to incorporate more context in visual learning. The algorithm generates 4 output image layers representing probabilities of 4 classes: invasive tumor, ductal/lobular carcinoma in situ, benign epithelium, and other tissue. The final segmentation result is generated by assigning each image pixel the class with the largest probability value. The algorithm was trained using ground truth (GT) annotations generated by 5 pathologists using 6157 square field of views (FOVs), 200-500 μm in size. These FOVs were collected from 850 whole slide images (WSI), spanning 9 commercial BC sample cohorts stained with different HER2 assays and scanned by several versions of the Aperio AT2 scanner. The samples included a mixture of biopsies and resections and covered different BC histologies and HER2 staining intensities. To evaluate the reproducibility of tumor area detection by human pathologists, an interpathologist comparison in detection of invasive tumor regions was performed using 225 FOVs annotated by multiple pathologists. Results Analysis generated an average Dice/F1 Score of 81.6% among different pathologists for invasive cancer. On the same sample set (independent of the ART training set), the invasive cancer detection by the ART algorithm was on par with human pathologists, achieving a similar average Dice/F1 score of 80.7%. Conclusions Novel deep learning-based ART algorithm provides accurate segmentation of invasive cancer on HER2-stained IHC images. The performance was verified against the GT annotations provided by multiple pathologists. Since the algorithm is trained using annotations from multiple pathologists, it is not possible to generate higher accuracy with computational pathology than is achievable between independent pathologists. Importantly, the same WSI read by the ART will consistently output the exact same tumor region identification result thus removing the inherent human subjectivity and variability, while improving the turnaround time for analysis. This development serves as the necessary foundation upon which a computational pathology-based diagnostic can be built.
Citation Format: Ansh Kapil, Anatoliy Shumilov, Philipp Wortmann, Sihem Khelifa, Jessica Chan, Michel Vandenberghe, Craig Barker, Mark Gustavson, Danielle Carroll, Hadassah Sade, Günter Schmidt. ART: Automated Region segmentation of Tumor on HER2-stained breast cancer tissue [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-16.