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A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials.

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This study developed a radiomics data harmonization model to reduce scanner variability in computed tomography images. The model successfully removed scanner effects, improving classification model performance for liver metastases.

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Area of Science:

  • Medical Imaging
  • Radiomics
  • Data Science

Background:

  • Scanner variability in computed tomography (CT) images hinders reproducible radiomics analysis.
  • Harmonization of radiomics data is crucial for developing generalizable models for lesion assessment.

Purpose of the Study:

  • To develop and evaluate a novel radiomics data harmonization model.
  • To assess the efficacy of the model in reducing scanner-associated variability.

Main Methods:

  • Analysis of radiomic features from 380 hepatic metastases across multiple cancer types.
  • Inclusion of normal liver tissue and hepatic cyst data as references.
  • Application of a linear mixed-effects model to identify scanner effects and a multivariate analysis with six machine learning models to test harmonization efficacy.

Main Results:

  • The proposed model effectively removes scanner-associated effects while preserving tumor size-dependent radiomic features.
  • Data harmonization significantly improved classification model performance, reducing scanner variability.
  • Specific improvements included a 15%-40% sensitivity increase for liver metastases, a 5% overall model accuracy increase, and an 8% increase in the kappa coefficient.

Conclusions:

  • The developed model successfully harmonizes radiomics data by removing scanner-specific effects.
  • Preservation of cancer-specific functional dependencies on tumor size is maintained.
  • Harmonization enhances the reliability and generalizability of radiomics-based models for clinical applications.