SHIFT: speedy histological-to-immunofluorescent translation of a tumor signature enabled by deep learning.

ABSTRACT: Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep lear

SUBMITTER: Burlingame EA 

PROVIDER: S-EPMC7566625 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature