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Radiomics: from qualitative to quantitative imaging.

William Rogers1,2, Sithin Thulasi Seetha1,2, Turkey A G Refaee1,3

  • 1The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.

The British Journal of Radiology
|February 27, 2020
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Summary
This summary is machine-generated.

Radiomics transforms medical images into quantitative data for predictive outcomes. This review covers handcrafted and deep learning approaches in radiomics for diagnosis, treatment, and monitoring.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Medical imaging traditionally lacks quantitative analysis capabilities.
  • Extracting predictive information from images has been a significant challenge.

Purpose of the Study:

  • To review the emerging field of radiomics, which quantifies medical images.
  • To explore both handcrafted and deep learning radiomics methodologies.
  • To discuss clinical applications, limitations, and future directions of radiomics.

Main Methods:

  • Extraction of quantitative features (shape, intensity, texture) from medical images.
  • Correlation of radiomic features with clinical and biological outcomes.
  • Application of machine learning algorithms for predictive modeling.

Main Results:

  • Radiomics enables radiologic phenotypic profiling for diagnosis, theranosis, and monitoring.
  • Handcrafted radiomics involves a multistage feature extraction process.
  • Deep learning radiomics offers an alternative approach within the radiomics workflow.

Conclusions:

  • Radiomics holds significant potential for improving medical decision-making.
  • The field is rapidly advancing with applications in oncology and beyond.
  • Further research is needed to address limitations and fully realize radiomics' future potential.