On May 30, 2025 Onc.AI, a digital health company developing AI-powered oncology clinical management solutions, reported 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) (Free ASCO Whitepaper) Annual Meeting, held May 30–June 3, 2025, in Chicago, IL (Press release, Onc AI, MAY 30, 2025, View Source [SID1234653537]).
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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 (Free ASCO Whitepaper) 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 – View Source
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 – View Source
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 – View Source
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.