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Published on: January 8, 2018
Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework
A Ibrahim1, S Primakov2, M Beuque3
1The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands; Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium; Department of Nuclear Medicine and Comprehensive Diagnostic Center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.
Quantitative imaging analysis using radiomics and deep learning offers potential for clinical decision support systems (cDSS). Challenges in explainability and reproducibility must be addressed for successful clinical translation.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Quantitative Imaging Analysis
Background:
- Medical imaging is transitioning from qualitative to quantitative data with AI advancements.
- Radiomics and deep learning are key quantitative imaging techniques for clinical decision support systems (cDSS).
- These methods offer benefits like data reuse, automation, minimal invasiveness, and cost-effectiveness.
Purpose of the Study:
- To review the current status of quantitative medical image analysis using radiomics and deep learning.
- To identify and discuss the challenges hindering clinical translation of these techniques.
- To propose a framework for robust radiomics analysis and explore future prospects.
Main Methods:
- Narrative review of radiomics and deep learning in quantitative medical image analysis.
- Analysis of challenges including model explainability, feature reproducibility, and sensitivity to acquisition parameters.
- Development of a proposed framework for robust radiomics analysis.
Main Results:
- Quantitative imaging analysis shows significant potential for developing clinical decision support systems.
- Key challenges identified include explainability, reproducibility, and sensitivity to imaging parameters.
- A framework for robust analysis and future directions are discussed.
Conclusions:
- Radiomics and deep learning hold promise for advancing quantitative medical imaging and cDSS.
- Addressing challenges in explainability, reproducibility, and standardization is crucial for clinical adoption.
- Further research and development are needed to fully realize the potential of these quantitative imaging techniques.
