  As a means to extract biomarkers from medical imaging, radiomics has
attracted increased attention from researchers. However, reproducibility and
performance of radiomics in low dose CT scans are still poor, mostly due to
noise. Deep learning generative models can be used to denoise these images and
in turn improve radiomics' reproducibility and performance. However, most
generative models are trained on paired data, which can be difficult or
impossible to collect. In this article, we investigate the possibility of
denoising low dose CTs using cycle generative adversarial networks (GANs) to
improve radiomics reproducibility and performance based on unpaired datasets.
Two cycle GANs were trained: 1) from paired data, by simulating low dose CTs
(i.e., introducing noise) from high dose CTs; and 2) from unpaired real low
dose CTs. To accelerate convergence, during GAN training, a slice-paired
training strategy was introduced. The trained GANs were applied to three
scenarios: 1) improving radiomics reproducibility in simulated low dose CT
images and 2) same-day repeat low dose CTs (RIDER dataset) and 3) improving
radiomics performance in survival prediction. Cycle GAN results were compared
with a conditional GAN (CGAN) and an encoder-decoder network (EDN) trained on
simulated paired data.The cycle GAN trained on simulated data improved
concordance correlation coefficients (CCC) of radiomic features from 0.87 to
0.93 on simulated noise CT and from 0.89 to 0.92 on RIDER dataset, as well
improving the AUC of survival prediction from 0.52 to 0.59. The cycle GAN
trained on real data increased the CCCs of features in RIDER to 0.95 and the
AUC of survival prediction to 0.58. The results show that cycle GANs trained on
both simulated and real data can improve radiomics' reproducibility and
performance in low dose CT and achieve similar results compared to CGANs and
EDNs.
