MERIT Webinar

Effective Reader Monitoring Plans & Variability QC for ADC, Immuno-oncology, Cancer Cell Therapy & Radiopharmaceutical Studies


Effective Reader Monitoring Plans & Variability QC for ADC, Immuno-oncology, Cancer Cell Therapy & Radiopharmaceutical Studies

Date: May 22, 2024
Time: 11 AM EST
Duration: 45 Minutes

Maintaining consistency and data integrity in imaging assessment can be very challenging in studies with imaging outcomes. There are various sources of variability in imaging assessment, particularly when qualitative assessment is involved in the reading process. In this MERIT webinar, we will discuss assessor variability and using reader monitoring plans to mitigate data variability in oncology clinical trials with imaging outcomes such as RECIST 1.1, RANO and other response criteria.

4 key questions that are answered:

  • What is inter- and intra-observer variability?
  • What are the sources of variability across assessments?
  • What parameters are used to examine the variability of assessment?
  • How do reader monitoring plans increase consistency and data integrity?
View Webinar Video
Featured Speaker:

LIN Chengyu, MDAssociate Medical Director

Dr Lin has been exploring the value of radiology in clinical research ever since obtaining his MD degree. During this time, he has worked with multiple sponsors, providing high-quality independent reading data to support the regulatory approval of various products in multiple countries and regions. Currently, Dr. Lin serves as the Associate Medical Director at MERIT, where his work continues to focus closely on protocol design, aiming to minimize risks and biases, while conducting quality monitoring of independent reads.



Gene is MERIT’s Associate Director of Medical Affairs & Innovation for our oncology division. He provides subject matter expertise and helps design and configure MERIT’s flexible oncology reading platform to accommodate various reading paradigms. Gene has experience in evidence-based patient care and academic research, a background in pharmacy, and 6 years supporting clinical trials. He implements software workflows to support oncology trials with response criteria such as RECIST 1.1, iRECIST, Lugano, and RANO, and helps to increase the efficiency and accuracy of clinical data collection for imaging endpoints. Gene received his Doctor of Pharmacy (Pharm.D.) from the University of Wisconsin-Madison and has a bachelor’s degree in Neurobiology.


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