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Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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Radiomics: A Primer on Processing Workflow and Analysis.

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Radiomics uses quantitative analysis of medical images to extract features for disease characterization. This review covers radiomics principles, methods, and future directions for objective medical diagnosis.

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Area of Science:

  • Medical Imaging Analysis
  • Quantitative Pathology
  • Radiomics

Background:

  • Quantitative medical image analysis offers objective tools for diagnosis, prognostication, and disease monitoring.
  • Radiomics is an emerging field focused on automated extraction of numerous quantitative features from medical images.

Purpose of the Study:

  • To review the fundamental principles of radiomics.
  • To discuss the technical workflow involved in radiomics, including image preprocessing and feature extraction.
  • To address the statistical analysis, limitations, and future prospects of radiomics.

Main Methods:

  • Review of existing literature on radiomics principles and methodologies.
  • Discussion of image preprocessing techniques relevant to radiomics.
  • Explanation of feature extraction workflows and statistical analysis approaches.

Main Results:

  • Radiomics enables the characterization of underlying pathologies through quantitative feature extraction.
  • The review outlines a comprehensive workflow from image acquisition to data analysis.
  • Key limitations and potential future research avenues are identified.

Conclusions:

  • Radiomics provides a powerful framework for objective, data-driven insights from medical images.
  • Standardization of radiomics workflows is crucial for clinical translation.
  • Further research is needed to fully realize the potential of radiomics in healthcare.