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Article summary:
1. Radiomics is an emerging research field that involves extracting quantitative features from medical images to develop predictive and/or prognostic models.
2. The conventional radiomics workflow typically uses predesigned (handcrafted or engineered) features from a segmented region of interest (ROI).
3. Recent advancements in deep learning have inspired trends toward deep-learning-based radiomics (DLRs), as well as hybrid solutions that exploit the potential of multiple data sources.
Article analysis:
The article provides a comprehensive overview of the current state of radiomics, including both handcrafted and deep-learning-based approaches. It is written in an objective manner, providing a balanced view of the advantages and disadvantages of each approach. The article also provides references to relevant research papers, which adds to its credibility.
However, there are some areas where the article could be improved upon. For example, it does not discuss any potential risks associated with using either handcrafted or DLR approaches for radiomics, such as privacy concerns or accuracy issues. Additionally, while the article does provide references to relevant research papers, it does not provide any evidence for the claims made in the article itself. Finally, while the article does provide a balanced view of both approaches, it does not explore any counterarguments or alternative perspectives on either approach.