Webinars

Discover How Targeted Radiotherapy Induced Toxicity May Be Identified Using Imaging

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

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:

• How interlesion response heterogeneity can impact clinical progression for patients
• How quantitative imaging can improve treatment response assessment
• How to use imaging analytics for early and rapid determination of pharmacodynamic effects with a small cohort of patients

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

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.

Harnessing the Power of Advanced Imaging to Elevate Treatment Response Assessments & Optimize Trial Outcomes

Alessandra Cesano, MD, PhD
Chief Medical Officer, ESSA Pharma
Giovanni Selvaggi, PhD
Chief Medical Officer, Xcovery
Eric Horler
President and Chief Executive Officer, AIQ Solutions

Advanced medical imaging is fueling new opportunities to improve understanding and quantification of cancer treatment response. Until now, the use of imaging (e.g.CT, PET, SPECT) in clinical trials has been limited to measuring a subset of lesions (usually 3-5) per scan and generalizing the results to the total disease. It has been demonstrated in published studies; however, that resistance in less than 10% of lesions can drive a poor overall clinical outcome. To comprehensively quantify treatment response, all lesions should be measured which is clinically infeasible without automation.

Join us for this 60-minute webinar as we address the above challenges and delve inside recent clinical research applications using novel technology to automatically detect and measure lesion-wise response over multiple scans.

Learning Objectives:

• Detecting and quantifying lesions of change in patients with a variety of metastatic cancers
• Analyzing the importance of including heterogeneity in clinical studies and treatment assessments, the gaps it creates when not taken into account, and how this problem is handled now
• Discussing ways to properly utilize additional information obtainable from imaging
• Looking into the practicality of manually assessing all lesions in patients
• Addressing the need to compare lesions identified on different imaging modalities