  This study investigates the foundational characteristics of image-to-image
translation networks, specifically examining their suitability and
transferability within the context of routine clinical environments, despite
achieving high levels of performance, as indicated by a Structural Similarity
Index (SSIM) exceeding 0.95. The evaluation study was conducted using data from
794 patients diagnosed with Prostate cancer. To synthesize MRI from Ultrasound
images, we employed five widely recognized image to image translation networks
in medical imaging: 2DPix2Pix, 2DCycleGAN, 3DCycleGAN, 3DUNET, and
3DAutoEncoder. For quantitative assessment, we report four prevalent evaluation
metrics Mean Absolute Error, Mean Square Error, Structural Similarity Index
(SSIM), and Peak Signal to Noise Ratio. Moreover, a complementary analysis
employing Radiomic features (RF) via Spearman correlation coefficient was
conducted to investigate, for the first time, whether networks achieving high
performance, SSIM greater than 0.85, could identify low-level RFs. The RF
analysis showed 75 features out of 186 RFs were discovered via just 2DPix2Pix
algorithm while half of RFs were lost in the translation process. Finally, a
detailed qualitative assessment by five medical doctors indicated a lack of low
level feature discovery in image to image translation tasks.
