Discover How Targeted Radiotherapy Induced Toxicity May Be Identified Using Imaging

Jones Nauseef M.D., PhD
Assistant Professor of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine
Tim Perk, PhD
VP of Research, AIQ Solutions

The current standards of quantifying treatment response for oncology studies such as RECIST and blood tests neglect the impact of intra-patient heterogeneity.

Artificial intelligence technologies continue to gain traction by delivering in depth assessments of treatment response and relationship to adverse event burden. Weill Cornell Medicine applied AIQ Solutions’ technology to patients with prostate cancer treated with prostate-specific membrane antigen (PSMA)-targeted radiotherapy (177Lu-PSMA-617). Automated quantification of 68Ga-PSMA PET tracer uptake was found to be associated with treatment response and adverse events.

Join this webinar to learn how AI-assisted analysis of radiological scans enhances understanding of treatment efficacy and adverse effect burden through spatiotemporal patient response assessments

How to Use Novel Imaging Analysis Technology to Redefine Oncology Treatment

Elisabeth I. Heath, MD, FACP
Associate Center Director of Translational Sciences of Karmanos Cancer Institute, Wayne State University
Katharina Modelska, MD, PhD
Vice President, Clinical Development, FibroGen
Tim Perk, PhD
VP of Research, AIQ Solutions

Karmanos Cancer Institute and Pfizer together with AIQ address how they were able to decrease the time and cost of assessing patient response to immunotherapy assets, including efficacy and toxicity risk, using an imaging analysis software platform. They present a discussion on a novel imaging analysis technology collaboration redefining intra-patient treatment response heterogeneity and how that heterogeneity impacts patient clinical response.
This collaboration was recently included in the Best of Journal of Clinical Oncology 2021 Genitourinary Cancer Edition. You will hear directly from the researchers and technologists that made this collaboration possible and learn:

Discovering Amyloidosis: How AI Imaging Application Shed Light on Rare Disease

Jonathan Wall, PhD
Professor and Assistant Dean for Research, University of Tennessee
Amy Weisman, PhD
Lead Project Scientist, AIQ Solutions

Systemic amyloidosis is a rare disease resulting in organ malfunction from build-up of the protein amyloid. Detection and quantification of amyloid burden in the body is an unmet clinical need with approximately 80% of patients undiagnosed.

Dr. Jonathan Wall of the University of Tennessee, together with AIQ Solutions, used an AI-driven, fully automated 3D segmentation and quantitation of a novel radiotracer to quantify amyloid burden in patients’ hearts and other organs.

This automated approach to image analysis could be an invaluable tool for novice readers, especially to quantify disease burden over time without introducing reader bias. This case study was in the rare disease amyloidosis, but this method has broad implications for meeting unmet needs in metastatic cancers and other complex diseases.

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Information on this page is intended for research use only. Not all applications and/or claims listed are currently cleared for use in treatment of patients. TRAQinform IQ software has been cleared by the FDA for clinical use under 510(k) K173444.

Information on this page is intended for research use only. Applications and/or claims listed are not currently cleared for use in treatment of patients.