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Radiomics in non-oncologic musculoskeletal diseases: from pixels to practice.

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Radiomics, extracting quantitative data from medical images, is expanding beyond cancer to musculoskeletal conditions. Standardization and AI integration are key for its reliable clinical use in precision medicine.

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Area of Science:

  • Medical Imaging
  • Quantitative Analysis
  • Radiology

Background:

  • Radiomics extracts high-dimensional data from medical images.
  • It reveals quantitative relationships between image features and pathology.
  • Applications are expanding from oncology to musculoskeletal conditions.

Purpose of the Study:

  • Provide an overview of radiomics principles and workflows.
  • Emphasize radiomics applications beyond oncology.
  • Highlight challenges and advancements in radiomics.

Main Methods:

  • Image acquisition and region-of-interest segmentation.
  • Feature extraction and model building.
  • Review of standardization needs and AI advancements.

Main Results:

  • Radiomics shows potential for early diagnosis and disease monitoring.
  • Segmentation variability and lack of standardization are key challenges.
  • Artificial intelligence and deep learning improve consistency and efficiency.

Conclusions:

  • Standardized, reproducible radiomics methodologies are crucial for clinical integration.
  • AI and open-source platforms facilitate wider adoption and research.
  • Actionable insights from radiomics can improve patient care in precision medicine.