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Nucleai’s ATOM spatial biology platform leverages computer vision and deep neural networks to structure and characterize tissue and cell architecture in cancer pathology images – helping identify spatial characteristics that predict response to therapy and inform treatment decisions so you can better understand cancer biology, optimize patient stratification and improve patient care.
Structure and characterize tissue and cell architecture from H&E, IHC, and Multiplex images.
Calculate spatial features to better explain mechanism of action and biological phenomena.
Leverage spatial analysis to predict outcomes, genomic status, and protein signatures.
Nucleai ATOM offers powerful multiplex analysis. The platform is agnostic to staining and scanning platforms and is currently available in a comprehensive service mode, providing fast turnaround times for high plex, large datasets.
The latest publications were presented at the Society for Immunotherapy of Cancer (SITC) in Boston in November 2022.
Predicting response to immune checkpoint inhibitors (ICI) in non-small-cell lung cancer (NSCLC) by combining spatial analysis of cells and RNA sequencing data from biopsies using deep learning (DL)
A deep learning analysis pipeline for multiplex imaging identifies spatial features associated with clinical outcome in colorectal cancer
Predicting CD8+ cell density and tumor-immune phenotypes in non-small-cell lung cancer (NSCLC) from standard H&E slides using deep learning (DL)
