  Radiomics is an active area of research in medical image analysis, the low
reproducibility of radiomics has limited its applicability to clinical
practice. This issue is especially prominent when radiomic features are
calculated from noisy images, such as low dose computed tomography (CT) scans.
In this article, we investigate the possibility of improving the
reproducibility of radiomic features calculated on noisy CTs by using
generative models for denoising.One traditional denoising method - non-local
means - and two generative models - encoder-decoder networks (EDN) and
conditional generative adversarial networks (CGANs) - were selected as the test
models. We added noise to the sinograms of full dose CTs to mimic low dose CTs
with two different levels of noise: low-noise CT and high-noise CT. Models were
trained on high-noise CTs and used to denoise low-noise CTs without
re-training. We also test the performance of our model in real data, using
dataset of same-day repeat low dose CTs to assess the reproducibility of
radiomic features in denoised images. The EDN and the CGAN improved the
concordance correlation coefficients (CCC) of radiomic features for low-noise
images from 0.87 to 0.92 and for high-noise images from 0.68 to 0.92
respectively. Moreover, the EDN and the CGAN improved the test-retest
reliability of radiomic features (mean CCC increased from 0.89 to 0.94) based
on real low dose CTs. The results show that denoising using EDN and CGANs can
improve the reproducibility of radiomic features calculated on noisy CTs.
Moreover, images with different noise levels can be denoised to improve the
reproducibility using these models without re-training, as long as the noise
intensity is equal or lower than that in high-noise CTs. To the authors'
knowledge, this is the first effort to improve the reproducibility of radiomic
features calculated on low dose CT scans.
