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Radiomics and Radiogenomics in Neuro-Oncology - An Artificial Intelligence Paradigm Volume 1: Radiogenomics Flow Using Artificial Intelligence
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Radiomics and Radiogenomics in Neuro-Oncology - An Artificial Intelligence Paradigm Volume 1: Radiogenomics Flow Using Artificial Intelligence
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Neuro-oncology broadly encompasses life-threatening brain and
spinal cord malignancies, including primary lesions and lesions
metastasizing to the central nervous system. It is well suited for
diagnosis, classification, and prognosis as well as assessing
treatment response. Radiomics and Radiogenomics (R-n-R) have become
two central pillars in precision medicine for
neuro-oncology.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. Due to the exponential progress of different computational
algorithms, AI methods are composed to advance the precision of
diagnostic and therapeutic approaches in neuro-oncology.The field
of radiomics has been and definitely will remain at the lead of
this emerging discipline due to its efficiency in the field of
neuro-oncology. Several AI approaches applied to conventional and
advanced medical imaging data from the perspective of radiomics are
very efficient for tasks such as survival prediction, heterogeneity
analysis of cancer, pseudo progression analysis, and infiltrating
tumors. Radiogenomics advances our understanding and knowledge of
cancer biology, letting noninvasive sampling of the molecular
atmosphere with high spatial resolution along with a systems-level
understanding of causal heterogeneous molecular and cellular
processes. 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
across large multi-institutional cohorts.Radiomics and
Radiogenomics in Neuro-Oncology: An Artificial Intelligence
Paradigm provides readers with a broad and detailed framework for
R-n-R approaches with AI in neuro-oncology, the description of
cancer biology and genomics study of cancer, and the methods
usually implemented for analyzing. Readers will also learn about
the current solutions R-n-R can offer for personalized treatments
of patients, limitations, and prospects. There is comprehensive
coverage of information based on radiomics, radiogenomics, cancer
biology, and medical image analysis viewpoints on neuro-oncology,
so this in-depth coverage is divided into two Volumes.Volume 1:
Radiogenomics Flow Using Artificial Intelligence provides coverage
of genomics and molecular study of brain cancer, medical imaging
modalities and analysis in neuro-oncology, and prognostic and
predictive models using radiomics.Volume 2: Genetics and Clinical
Applications provides coverage of imaging signatures for brain
cancer molecular characteristics, clinical applications of R-n-R in
neuro-oncology, and Machine Learning and Deep Learning AI
approaches for R-n-R in neuro-oncology.
General
Imprint: |
Academic Press Inc
|
Country of origin: |
United States |
Release date: |
2024 |
First published: |
2024 |
Authors: |
Sanjay Saxena
• Jasjit Suri
|
Dimensions: |
278 x 215mm (L x W) |
Pages: |
622 |
ISBN-13: |
978-0-443-18508-3 |
Categories: |
Books
|
LSN: |
0-443-18508-5 |
Barcode: |
9780443185083 |
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