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Harmonization Strategies in Multicenter MRI-Based Radiomics.

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Radiomics analysis extracts patient data from images but faces bias from patient and scanner differences. This review guides radiomics harmonization techniques to improve diagnostic accuracy and reliability.

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MRIbatch effectfeature harmonizationfeature stabilityimage harmonizationmulticenter studiesradiomicsstandardizationvariability

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Area of Science:

  • Medical imaging analysis
  • Quantitative imaging biomarkers
  • Radiomics and artificial intelligence

Background:

  • Radiomics analysis decodes medical images into quantitative features for diagnostic and prognostic insights.
  • Despite its promise for digital diagnostics, radiomics is vulnerable to biases from patient demographics and scanner variations.
  • Existing harmonization methods lack established effectiveness, creating a need for optimized radiomics analysis pipelines.

Purpose of the Study:

  • To review the challenges in optimizing radiomics analysis.
  • To summarize key radiomics harmonization techniques.
  • To provide a guide for the radiomics harmonization process.

Main Methods:

  • Review of existing literature on radiomics harmonization.
  • Analysis of challenges in radiomics data processing.
  • Synthesis of various image and feature-based harmonization strategies.

Main Results:

  • Identified key challenges in radiomics analysis, including inter-patient and inter-scanner variability.
  • Summarized diverse harmonization techniques aimed at mitigating these biases.
  • Highlighted the lack of a universally established best practice for radiomics harmonization.

Conclusions:

  • Optimizing radiomics analysis requires addressing inherent biases through effective harmonization.
  • Further research and standardization of harmonization techniques are crucial for reliable radiomics applications.
  • This review offers a comprehensive overview to guide researchers and clinicians in selecting appropriate radiomics harmonization methods.