LAUSR

Colorectal, breast, and skin cancer are among the most common and deadly diseases in the United States, according to the American Cancer Society. Despite the pressing need for a fast, accurate and data-driven approach to diagnose and prognose cancer, a current solution does not exist. This project presents PocketOnco, an iOS mobile app developed in CoreML and XCode that uses multilayered convolutional neural networks (CNN) to automatically diagnose and prognose colorectal, breast, and skin cancer through tumor feature segmentation and prediction within seconds. Users can either import a histopathological tissue image or take an external dermoscopic picture and select crop for the region of interest. Leveraging unique phenotypic features such as nuclear pleomorphism, glandular/tubule formation, mitotic activity, and molecular subtype, the CNN is trained through a dataset of over 5,000 images acquired from University of Porto, University Hospitals Coventry Warwickshire, and International Skin Imaging Collaboration (ISIC) to produce an accuracy of 100% for validation for all cancers, identification/diagnosis accuracy of 96%, 78% and 75% and prognosis accuracy of 76%, 97%, and 80% for skin, colon, and breast cancer, respectively. Following prognosis, the app then provides potential treatments (chemotherapy, radiation, immunotherapy, targeted therapy, etc.) and clinical trials based on the location of the user, with data retrieved from the U.S. National Library of Medicine. PocketOnco is high-speed, low-cost, and user-friendly with significantly improved accuracy over the current gold standard, advancing precision medicine through a multi-cancer diagnosis-prognosis app, bridging the gap between patients and experts as diagnostics are now available at the touch of users9 fingertips within seconds. Citation Format: Stephanie Zhang, Patrick Cui. PocketOnco: A CoreML based app for diagnosis and prognosis of colorectal, breast and skin cancer using multilayered convolutional neural network algorithms [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2084.