20 min
Regulatory grade oncology RWD from NTT DATA and Vidence Graticule Podcast Series
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- Technology
In this podcast Mitchell Goldburgh, SR. Director of Solutions – Enterprise Imaging & Advocate AI Services from NTT DATA, and Rob Hauser, the Chief Business & Analytics Officer from Vidence, discuss applications of datasets that extend oncology data from a large national cancer center with radiology studies and reports such as chest CT scans of lung cancer patients. The discussion explains why synthetic control arms and label expansion studies are important and explores how advanced oncology data can reduce the need to recruit patients into clinical trials. The panel also provides insight into the process to build new digital diagnostic tools based on radiomics data that are being developed to predict which patients will respond to personalized therapies. You will also learn about the multiple approaches NTT DATA has put in place to make the most of unstructured data within the NTT DATA environment including feasibility studies, abstraction, machine learning/AI, and annotation by radiologists.
In this podcast Mitchell Goldburgh, SR. Director of Solutions – Enterprise Imaging & Advocate AI Services from NTT DATA, and Rob Hauser, the Chief Business & Analytics Officer from Vidence, discuss applications of datasets that extend oncology data from a large national cancer center with radiology studies and reports such as chest CT scans of lung cancer patients. The discussion explains why synthetic control arms and label expansion studies are important and explores how advanced oncology data can reduce the need to recruit patients into clinical trials. The panel also provides insight into the process to build new digital diagnostic tools based on radiomics data that are being developed to predict which patients will respond to personalized therapies. You will also learn about the multiple approaches NTT DATA has put in place to make the most of unstructured data within the NTT DATA environment including feasibility studies, abstraction, machine learning/AI, and annotation by radiologists.
20 min