Onc.AI, a digital health company developing AI-powered oncology clinical management solutions, today announced that new validation study results from research collaborations with Pfizer, Baylor Scott & White and the University of Rochester Medical Center will be presented at the 2025 American Society of Clinical Oncology (ASCO) Annual Meeting, held May 30–June 3, 2025, in Chicago, IL.
Onc.AI’s poster presentation showcases its FDA-breakthrough designated deep learning radiomics model, Serial CTRS, which evaluates changes across routine CT scans over time to predict overall survival in late-stage non–small cell lung cancer (NSCLC) and other solid tumor types. In collaboration with Baylor Scott & White and Pfizer, Serial CTRS has demonstrated:
- Superior prediction of overall survival (OS): Hazard ratios (HRs) for OS improvement and stratification exceed those of the conventional imaging approach (RECIST 1.1).
- Generalizability across real-world and clinical trial cohorts: Robust performance in both routine real-world datasets and a Pfizer-sponsored PD-1 checkpoint inhibitor trial.
- Actionable insights for early treatment adaptation: Dynamic monitoring identifies non-responders months before conventional criteria would signal poor prognosis.
At the ASCO Innovation Hub (IH13), Onc.AI will share latest results from its pipeline of deep learning radiomic models to customers and partners spanning medical oncologist investigators and biopharma companies looking to accelerate oncology clinical development.
Program Highlights
Poster Presentation:
Abstract #253138: Validation of Serial CTRS for Early Immunotherapy Response Prediction in Metastatic NSCLC – https://meetings.asco.org/abstracts-presentations/253138
- Presenter: Ronan Kelly, MD, Baylor Scott & White
- Date & Time: June 1, 2025; 9:00 am–12:00 pm CDT
- Location: Hall A, Poster Board 325
Abstract #251996: Retrospective Single-Institution Application of a Deep Learning–Based Radiomic Score in Metastatic NSCLC: Potential Impact on First-Line Treatment Decisions – https://meetings.asco.org/abstracts-presentations/251996
- Lead Author: Nicholas Love, MD, University of Rochester
Abstract #245837: Image Harmonization for PD-(L)1 Immune Checkpoint Inhibitor Response Prediction Using Deep Learning Radiomic Features in Advanced NSCLC – https://meetings.asco.org/abstracts-presentations/245837
- Lead Author: Taly Gilat-Schmidt, PhD, Onc.AI
“These strong validation study results spanned both RWD and a pharma-sponsored clinical trial. Serial CTRS could represent a high-potential tool for medical oncologists and for optimizing pharma clinical development,” said Dr. Ronan Kelly, MD, Director of Oncology at the Charles A. Sammons Cancer Center, Baylor University Medical Center, Dallas Texas
“Our retrospective study highlights how Onc.AI’s Deep Learning Radiomic baseline score can be extremely helpful to medical oncologists as a prognostic marker for first line mutation negative NSCLC patients,” added Arpan Patel, MD and Associate Professor of Medical Oncology at the University of Rochester Medical Center.
About Onc.AI
Onc.AI is a digital health company developing AI-driven oncology clinical management solutions using advanced Deep Learning applied to routine diagnostic images. The company’s platform is applied at the point of care by medical oncologists and is also leveraged by global pharmaceutical leaders to accelerate oncology drug development.
Onc.AI is backed by premier institutional investors, including Blue Venture Fund, Action Potential Venture Capital (GSK) and MassMutual Alternative Investments. Onc.AI is also supported by the National Cancer Institute SBIR program (1R44CA291456-01A1). For more information, please visit: www.onc.ai
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Deep learning radiomics to transform oncology clinical development and improve upon RECIST 1.1 (#better_recist)
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