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. 2020 May 27;2(3):e190035. doi: 10.1148/ryai.2020190035

Figure 1:

Radiomic feature (RF) reproducibility analysis pipeline. A, A cycle-GAN was first trained to translate textures between manufacturers: Philips DigitalDiagnost (DD) and Siemens Fluorospot Compact FD (FCFD). B, Following texture translation, 92 RFs were extracted from lung parenchyma for each native and fake chest radiograph of an independent testing dataset. C, The intermanufacturer RF variability was compared between pairs of native and translated chest radiographs in this independent dataset computing the concordance correlation coefficient for each RF. fDD = fake DD image, fFCFD = fake FCFD image, GAN = generative adversarial network, nDD = native DD image, nFCFD = native FCFD image.

Radiomic feature (RF) reproducibility analysis pipeline. A, A cycle-GAN was first trained to translate textures between manufacturers: Philips DigitalDiagnost (DD) and Siemens Fluorospot Compact FD (FCFD). B, Following texture translation, 92 RFs were extracted from lung parenchyma for each native and fake chest radiograph of an independent testing dataset. C, The intermanufacturer RF variability was compared between pairs of native and translated chest radiographs in this independent dataset computing the concordance correlation coefficient for each RF. fDD = fake DD image, fFCFD = fake FCFD image, GAN = generative adversarial network, nDD = native DD image, nFCFD = native FCFD image.