Bio IT World MM DH
February 25, 2020

Imaging Data: Missing Component Of Real-World Data

By Matthew Michela, President and CEO, Life Image and Dan Housman, Co-founder and CTO, Graticule  

Radiology is increasingly the key to synthesizing real world evidence with sufficient quality to shape drug performance. As a diagnostic toolset, imaging is one of the oldest and most widely used and clearly interpreted methods for measurement of outcomes. It is regularly used to measure disease progression and to determine the effectiveness of medical interventions. For example, in neurology, imaging is used to determine progression in Multiple Sclerosis by establishing plaque accumulation over time. In oncology, imaging is used to measure solid tumor size and burden in response to or preparation for therapy. Imaging is also a common mechanism for early identification of disease that can be automated or extended through artificial intelligence (AI). This is one reason the industry has seen a fair amount of development activity in the use of AI in mammography for breast cancer and chest CTs to identify lung cancer, as these are two high-volume screening methods for catching cancer at a point early enough to successfully treat it.

The Demand for Data

With the advent of digitization of medical records, Real World Data (RWD) became a critical part of drug development and commercialization. While medical insurance claims were historically the primary source of information for analyzing treatment patterns and economic outcome, they lack the clinical details unrelated to the quantification of payment activity, which are needed to establish regulatory-grade medical evidence. The 21st Century Cures Act includes provisions for approving new indications for existing approved drugs based on RWD. But the burden of proof for regulatory submissions requires regulatory-grade data at a level similar to clinical trials. New drugs such as Amgen’s Blicyto and Roche’s Alecensa have successfully used synthetic control arms to obtain approval without needing a placebo, but these synthetic approaches require regulatory-grade data that can be compared with validated information from studies. In addition to approvals for drugs, biomarkers are now increasingly developed through AI training, and imaging can help identify patients through complex unstructured data. Biomarkers also have similar requirements for regulatory approvals to translate their use into clinical practice.

Click here to continue reading. Originally published by Bio IT World on February 20, 2020.

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