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A generative adversarial network (GAN) improved radiomic feature (RF) reproducibility across different medical imaging manufacturers. This deep learning method enhanced diagnostic accuracy for congestive heart failure (CHF) detection.

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Deep Learning for Image Analysis
  • Radiomics and Quantitative Imaging

Background:

  • Intermanufacturer variability in radiomic features (RFs) poses a challenge for consistent analysis of medical images.
  • Differences in imaging acquisition protocols and equipment can lead to significant variations in extracted RFs.
  • Standardization of RFs is crucial for reliable clinical application and machine learning model development.

Purpose of the Study:

  • To evaluate the effectiveness of a generative adversarial network (GAN) in enhancing intermanufacturer reproducibility of radiomic features (RFs).
  • To assess the impact of GAN-based texture translation on the diagnostic performance of RFs for congestive heart failure (CHF).

Main Methods:

  • A cycle-generative adversarial network (cycle-GAN) was developed to translate texture information between chest radiographs from different manufacturers (Siemens and Philips).
  • The study prospectively evaluated the cycle-GAN's ability to reduce intermanufacturer variability in lung parenchyma RFs.
  • Machine learning classifiers and radiologists assessed the cycle-GAN's effectiveness in obscuring image manufacturer origin; impact on CHF diagnostic accuracy was also tested.

Main Results:

  • Cycle-GAN texture translation significantly decreased intermanufacturer variability of extracted radiomic features.
  • Machine learning classifiers and experienced radiologists were unable to reliably distinguish between original and GAN-generated (fake) chest radiographs.
  • The use of cycle-GAN improved the discriminative power of RFs for identifying patients with congestive heart failure (CHF), increasing accuracy from 55% to 73.5% (P < .001).

Conclusions:

  • The cycle-GAN effectively improved radiomic feature intermanufacturer reproducibility, making them less sensitive to acquisition differences.
  • The deep learning approach demonstrated improved discriminative power for diagnosing congestive heart failure (CHF).
  • This GAN-based method shows promise for standardizing radiomic analysis and enhancing its clinical utility.