A top-tier biopharma company was interested in incorporating an Artificial Intelligence (AI)-based model that predicts treatment response to immune checkpoint inhibitors in NSCLC using baseline chest CT scans.
Identifying patients more likely to respond might help to improve the design of future clinical studies by maximizing the efficacy of the treatment arm in a specific disease phenotype. In a post-approval scenario, the predictive algorithm would also be useful as a companion diagnostic (CDx) to treat patients likely to benefit from the treatment.
As pay-for-performance reimbursement models grow in the immunotherapy space, it is more important than ever for practitioners to administer the right treatment for the right patient at the right time.
A large database of 1000+ chest CT scans from a failed phase-III study was available to extract useful insights for future studies. The dataset included information about patient outcomes, such as treatment response and overall survival. The biopharma partner engaged in a value-based strategic collaboration with Quibim to design and create an AI model that could predict treatment response from pre-treatment CT scans.
Quibim performed an initial feasibility analysis, finding a high variability in the image quality across the 50+ sites involved in the study. The image quality of a few sites was not enough to qualify. All the images were transformed into a standard image quality by GAN-based image harmonization algorithms implemented by Quibim.
After the image quality harmonization of the entire dataset, Quibim developed and validated an AI algorithm based on radiomics, deep features, and deep learning techniques, predicting whether the patient will respond to immunotherapy in 80% of cases.
What’s in it for the biopharma partner and Quibim?
