Response evaluation criteria in solid tumours (RECIST) 1.1 and other similar criteria for tumour burden assessments have been used conventionally for the investigation of the tumour-inhibitory effect of cancer drugs. A large sample size is commonly required for both a reliable estimation of overall survival from such criteria, and to differentiate from the control arm. However, the ‘g’ value obtained through effective TGR modelling may provide a more efficient alternative to understanding drug efficacy using a lower sample size.
Radiomics is the extraction of quantitative metrics from medical images that characterise tumour heterogeneity. Radiomic features (both cross-sectional and longitudinal changes) have been associated with tumour aggressiveness and may predict clinical and clinical trial endpoints like survival.
In this webinar, we will discuss the basics of TGR modelling from oncological imaging data, as well as radiomics. We will also look at how AI can be harnessed for a more automated tumour volume detection, and how this can aid with simultaneous assessment of both ‘g’ as well as radiomics features.
Join experts from ICON, Paul McCracken, PhD, Vice President, Global Head of Medical Imaging; Ramkumar Krishnamurthy, PhD, Medical Imaging Scientist II; and Meena Makary, PhD, Medical Imaging Scientist II, for the live webinar on Thursday, October 12, 2023, at 10am EDT (4pm CEST/EU-Central).
For more information, or to register for this event, visit Advances in oncological imaging: Tumour growth rate modelling and radiomics.
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