Evaluate Artera’s Deep Learning clinical-histologic system as a prognostic classifier for colorectal cancer patients
Artera is a company focused on developing AI approaches to personalized cancer therapy. They are led by an AI team who have previously published dozens of papers, including seminal works in Nature (PMID 28117445), Cell (PMID: 29656897), and Nature Medicine (PMID: 30617335), among many others. They utilize the Deep Learning method to develop prognostic and predictive biomarkers. Recent publications and presentations by Artera and collaborators show that their prognostic and predictive models, developed from H&E image data along with minimal clinical features, have higher AUC compared to clinical classifiers in prostate cancer. These H&E image-based methods have the advantage of increased availability compared to archival tissue, conserving scarce tissue resources, lower processing cost, and faster turn-around time.
Artera is currently rapidly expanding in other cancer indications and disease settings, such as colorectal cancer. For this project, we propose to advance the field by utilizing Artera’s AI approach to develop and validate AI-based tests that provide prognostic and predictive insights for colorectal cancer patients.
We propose to apply Artera’s DL methods, using baseline clinical variables, high resolution scans of available histology/pathology slides, and outcomes data from PLCO cohorts to evaluate prognostic and predictive capabilities for colorectal cancer.
Erin Stewart, Artera Inc.
Douglas Peters, Artera Inc.
Katie O’Shaughnessy, Artera Inc.
Megan Coy, Artera Inc.
Tamara Todorovic, Artera Inc.
Alexandra Kraft, Artera Inc.
Alexander Piehler, Artera Inc.
Ivy Zhang, Artera Inc.
Rebecca Huang, Artera Inc.
Erik Rosten, Artera Inc.
Ali Moatadelro, Artera Inc.
Tunai Marques, Artera Inc.
Songwan Joun, Artera Inc.
Alicia Yang, Artera Inc.