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Business Wire
3 days ago
- Health
- Business Wire
Onc.AI to Present Breakthrough Deep Learning Radiomic Biomarker Results at 2025 ASCO Annual Meeting
CHICAGO--(BUSINESS WIRE)-- 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. Deep learning radiomics to transform oncology clinical development and improve upon RECIST 1.1 (#better_recist) Share 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), 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 – 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 – 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 – Lead Author: Taly Gilat-Schmidt, PhD, '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 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 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. is backed by premier institutional investors, including Blue Venture Fund, Action Potential Venture Capital (GSK) and MassMutual Alternative Investments. is also supported by the National Cancer Institute SBIR program (1R44CA291456-01A1). For more information, please visit:
Business Wire
25-04-2025
- Business
- Business Wire
Onc.AI Announces Presentation of Breakthrough-Designated AI Model Evaluated in Clinical Trial Data at AACR 2025
CHICAGO--(BUSINESS WIRE)-- a digital health company developing advanced AI-driven clinical management solutions for oncology, today announced that findings from a recent collaboration with global biopharma company GSK using FDA breakthrough-designated Serial CTRS AI model will be presented at the 2025 American Association for Cancer Research (AACR) Annual Meeting. The study externally evaluated Serial CTRS in GSK's GARNET Phase I clinical trial (NCT02715284) Cohort E, that enrolled patients with advanced non-small cell lung cancer (NSCLC) treated with dostarlimab, GSK's anti-PD-1 checkpoint inhibitor. Results, highlighted in a poster presentation during the Predictive Biomarkers 2 session (poster #21, April 27, 2025, 2-5pm), demonstrated that the Serial CTRS biomarker, leveraging routine CT imaging without manual annotations, improved prediction of overall survival (OS) compared to traditional surrogates including RECIST 1.1 response criteria and tumor volume. The analysis was conducted through an independent and blinded retrospective validation study carried out by GSK. In particular, Serial CTRS showed the ability to distinguish patients with intermediate versus high probabilities of 12-month OS (HR: 2.91, 95% CI: 1.16–7.31), outperforming RECIST 1.1 (HR: 1.34, 95% CI: 0.57–3.13) and tumor volume change assessments (HR: 1.00, 95% CI: 0.43–2.34). This enhanced predictive performance supports commitment to establishing Serial CTRS as a new standard for automated, AI-based imaging endpoints in the early assessment of treatment response, seamlessly integrating into standard imaging workflows across diverse therapeutic regimens. Key findings include: Serial CTRS improved discrimination between intermediate and high probabilities of overall survival compared to RECIST 1.1 and tumor volume changes. Serial CTRS remained a significant predictor of overall survival after adjusting for known prognostic factors, such as age, baseline tumor volume, and PD-L1 Tumor Proportion Score (TPS). 'This important milestone for Serial CTRS builds on a recent breakthrough-designation from FDA,' said Akshay Nanduri, CEO of 'The success of this validation study and ongoing continued collaboration with GSK reflects the strength and robustness of model performance. This can only be achieved using advanced Deep Learning combined with methods for harmonizing diverse imaging data and the breadth of our training data. high-quality data on thousands of patients has been sourced from dozens of healthcare systems representing hundreds of clinics across the United States and other international cancer centers, ensuring unparalleled diversity in training, test and validation.' 'As the past head of multiple Phase I clinics at top cancer centers, I believe that innovation with Serial CTRS could transform the pharma clinical development process from Phase I to Phase III studies,' said George R. Simon, MD, FACP, FCCP, Vice President of Oncology at OhioHealth. About 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. is backed by premier institutional investors, including: Sandbox/Blue Venture Fund, Action Potential Venture Capital, MassMutual Alternative Investments, Accomplice, Digitalis, KdT, and Life Extension Ventures. is also supported by the National Cancer Institute SBIR program (1R44CA291456-01A1). For more information, please visit:
