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Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model
Published on: April 13, 2015
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Radiomics in immuno-oncology.
Z Bodalal1,2, I Wamelink1,3, S Trebeschi1,2
1Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Immuno-Oncology Technology
|June 27, 2022
Summary
Radiomics, a quantitative imaging analysis, extracts hidden predictive information from medical scans. This review explores its use with artificial intelligence (AI) in cancer therapy, highlighting potential and challenges.
Area of Science:
- Medical Imaging
- Quantitative Imaging Analysis
- Artificial Intelligence in Oncology
Background:
- Advances in imaging generate vast clinical data.
- Quantitative imaging and radiomics are emerging fields.
- Radiomics extracts high-dimensional features from medical images.
Purpose of the Study:
- To provide an overview of radiomics and artificial intelligence (AI).
- To discuss applications in immunotherapy and targeted therapy.
- To highlight the potential and challenges of these technologies in oncology.
Main Methods:
- Review of radiomics principles and AI integration.
- Exploration of use cases in immunotherapy response and adverse event prediction.
- Examination of radiogenomics in targeted therapy.
Main Results:
- Radiomics and AI show promise in predicting treatment outcomes.
- Applications include immunotherapy response and adverse event prediction.
- Radiogenomics offers insights into targeted therapy.
Conclusions:
- Radiomics and AI hold significant potential in immuno-oncology.
- The technology is still evolving with challenges to address.
- Novel AI algorithms are expanding the scope of applications in cancer care.
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