Lung cancer is the leading cause of cancer death, making up almost 25% of all cancer deaths. Each year, more people die of lung cancer than of colon, breast, and prostate cancers combined.
At Nucleai we are committed to making a difference for lung cancer patients and utilizing AI-powered Spatial Biology to make tangible contributions to drug development and clinical decision-making for lung cancer and beyond.
Nucleai ATOM, built and trained off large-scale proprietary datasets, leverages computer vision to structure and characterize tissue and cell architecture in order to identify spatial characteristics that predict response to therapy and inform treatment decisions.
Our vision is for every pathology image to run through the Nucleai platform, starting with pharma to augment biomarker discovery and development, and eventually as an FDA-approved diagnostics tool in the hands of physicians.
Predicting Response to Pembrolizumab in Non-Small-Cell Lung Cancer by Analyzing the Spatial Arrangement of Tumor-Infiltrating Lymphocytes (TILs) Using Deep Learning
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)
Predicting CD8+ cell density and tumor-immune phenotypes in non-small-cell lung cancer (NSCLC) from standard H&E slides using deep learning (DL)
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.
Get in touch to schedule a meeting with our experts at the whos or to request a demo of our platform.
