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Xtalks, online
2023 -10-12
The use of medical imaging biomarkers to better understand the efficacy of cancer drugs, aided by the power of AI.
Oncology clinical trials would benefit greatly from new biomarkers that could help to better understand the efficacy of drug treatments. Growth rate (‘g’) of tumour derived by appropriate tumour growth rate (TGR) modelling, as well as quantitative estimation of tumour heterogeneity through radiomics are two processes that offer such oncological imaging biomarkers.
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.
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Organized by:
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Xtalks |
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Invited Speakers:
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Paul McCracken, PhD, Vice President, Global Head of Medical Imaging, ICON Ramkumar Krishnamurthy, PhD, Medical Imaging Scientist II, ICON Meena Makary, PhD, Medical Imaging Scientist II, ICON
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Deadline for Abstracts:
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2023 -10-12
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Registration:
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Free registration
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E-mail:
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ajuurinen@xtalks.com
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