NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images.

ABSTRACT: Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates s

SUBMITTER: Jimenez-Sanchez D 

PROVIDER: S-EPMC9972483 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature