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Principal Investigator
Erin Stewart
Artera Inc
Position Title
Head of Clinical Research Network and Senior Scientific Director
About this CDAS Project
NLST (Learn more about this study)
Project ID
Initial CDAS Request Approval
May 30, 2023
Evaluate Artera’s Deep Learning clinical-histologic system as a prognostic classifier for lung cancer patients
Lung cancer is the 2nd most common cause of cancer death worldwide, with 2.2 million new cases and 1.8 million deaths estimated in 2020 (GLOBOCAN 2020). The optimal treatment of lung cancer is increasingly complex, with evolving evidence, treatment options, and toxicity profiles. Startifying patients based on their risks and predicted benefit from more aggressive treatments would help improve patient outcomes while sparing patients from unnecessary treatments and associated toxicities. Recent advances in risk stratification have leveraged clinical variables such as PET-based insights to estimate prognosis for lung cancer, but these strategies are not optimal for long-term outcomes and predictive treatment selection/optimization. Improved prognostic and predictive risk stratification models are needed to better inform personalized treatment decisions and guide selection for treatment intensification and future clinical trials.

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 lung cancer. For this project, we propose to advance the field by utilizing Artera’s AI approach to develop and validate AI-based models that provide prognostic and predictive insights for lung cancer patients.

We propose to apply Artera’s deep learning architecture, using baseline clinical variables, high resolution scans of available histology/pathology slides, and outcomes data from NLST cohorts to evaluate prognostic capabilities for lung cancer.


Huei-Chung (Rebecca) Huang, Artera
Rikiya Yamashita, Artera