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Radiomics and Radiogenomics Neuro-Oncology: An Artificial Intelligence Paradigm - Volume 1: Flow Using
Barnes and Noble
Radiomics and Radiogenomics Neuro-Oncology: An Artificial Intelligence Paradigm - Volume 1: Flow Using
Current price: $200.00
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Barnes and Noble
Radiomics and Radiogenomics Neuro-Oncology: An Artificial Intelligence Paradigm - Volume 1: Flow Using
Current price: $200.00
Size: Paperback
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Radiomics and Radiogenomics in Neuro-Oncology: An Artificial Intelligence Paradigm, Volume One: Radiogenomics Flow Using Artificial Intelligence
broadly encompasses the study of life-threatening brain and spinal cord malignancies, including primary lesions and those metastasizing to the central nervous system. Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Sections in this book discuss several AI approaches that have been applied to conventional and advanced medical imaging data. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility.
broadly encompasses the study of life-threatening brain and spinal cord malignancies, including primary lesions and those metastasizing to the central nervous system. Radiomics is an approach to medical imaging used to extract many quantitative imaging features using different data characterization algorithms, while radiogenomics, which has recently emerged as a novel mechanism in neuro-oncology research, focuses on the relationship of imaging phenotype and genetics of cancer. Sections in this book discuss several AI approaches that have been applied to conventional and advanced medical imaging data. These AI-based R-n-R tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and permit better dynamic treatment monitoring in this period of personalized medicine. While extremely promising, the clinical acceptance of R-n-R methods and approaches will primarily hinge on their resilience to non-standardization across imaging protocols and their capability to show reproducibility.