Yunu CEO Highlights Imaging Workflow as a Missing Link in Clinical Trial Acceleration

Leading off afternoon sessions, Sorenson engaged LSI attendees by highlighting the role and importance of imaging in clinical trials while detailing new possibilities for pristinely captured and maintained trial imaging data. The Yunu CEO noted, “The state of affairs in clinical trial imaging is at odds with industry initiatives for increasing diversity, broadening access, and improving trial outcomes. At a time when sponsors increasingly rely on imaging as an important early signal of treatment efficacy, sites that serve as the source of these data are struggling, creating adverse effects on the entire clinical trial ecosystem. Research sites are facing longstanding workflow inefficiencies, staffing shortages, and outdated systems, all of which contribute to image assessment error rates of 25% to 50%. As a result, there is wasted time and expense in enrolling patients who don’t meet trial criteria, as well as protocol violations and site-central discordance, which result in inappropriate patient treatments. This constitutes a crisis, as sponsors cannot directly rely on these results to assess outcomes. Yunu addresses these challenges holistically, connecting sponsors, CROs, imaging CROs, and research sites.” 

Yunu’s team of medical imaging and life sciences innovators was purposefully assembled to bring expertise and new ideas to this area of significant need. Yunu manages over 4,200 active clinical trials on the platform and was recently selected by a prestigious nationwide cancer center, a large professional medical society imaging core lab supporting pharmaceutical research, and a leading children’s research hospital. The company’s cloud-based ecosystem creates a framework for intelligent workflow and extends trial access while improving sponsor and CRO visibility. Trial stakeholders now have real-time data access for earlier decision-making and data science initiatives that create more impactful trial outcomes. 

“Well-orchestrated imaging workflow that efficiently accomplishes all of the required complex measurements, calculations, tracking, and signoffs is key to creating a future where prospective source data is accurately captured, labeled, and widely available,” noted Sorenson. “If you want to unleash the potential of AI in drug development, you must start with clean and complete data — and of course, it must include imaging. An important thing we can do today for the future of research is to start handling imaging workflows well.”

Click here to watch Jeff Sorenson's presentation at LSI USA '24.