TITLE: Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study 

ABSTRACT: Radiomics is an active area of research focusing on high throughput feature
extraction from medical images with a wide array of applications in clinical
practice, such as clinical decision support in oncology. However, noise in low
dose computed tomography (CT) scans can impair the accurate extraction of
radiomic features. In this article, we investigate the possibility of using
deep learning generative models to improve the performance of radiomics from
low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics
and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival
prediction and lung cancer diagnosis. We used encoder-decoder networks and
conditional generative adversarial networks (CGANs) trained in a previous study
as generative models to transform low dose CT images into full dose CT images.
Radiomic features extracted from the original and improved CT scans were used
to build two classifiers - a support vector machine (SVM) and a deep attention
based multiple instance learning model - for survival prediction and lung
cancer diagnosis respectively. Finally, we compared the performance of the
models derived from the original and improved CT scans. Encoder-decoder
networks and CGANs improved the area under the curve (AUC) of survival
prediction from 0.52 to 0.57 (p-value<0.01). On the other hand, Encoder-decoder
network and CGAN can improve the AUC of lung cancer diagnosis from 0.84 to 0.88
and 0.89 respectively (p-value<0.01). Moreover, there are no statistically
significant differences in improving AUC by using encoder-decoder network and
CGAN (p-value=0.34) when networks trained at 75 and 100 epochs. Generative
models can improve the performance of low dose CT-based radiomics in different
tasks. Hence, denoising using generative models seems to be a necessary
pre-processing step for calculating radiomic features from low dose CTs.
Radiomics is an active area of research focusing on high throughput feature
extraction from medical images with a wide array of applications in clinical
practice, such as clinical decision support in oncology. However, noise in low
dose computed tomography (CT) scans can impair the accurate extraction of
radiomic features. In this article, we investigate the possibility of using
deep learning generative models to improve the performance of radiomics from
low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics
and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival
prediction and lung cancer diagnosis. We used encoder-decoder networks and
conditional generative adversarial networks (CGANs) trained in a previous study
as generative models to transform low dose CT images into full dose CT images.
Radiomic features extracted from the original and improved CT scans were used
to build two classifiers - a support vector machine (SVM) and a deep attention
based multiple instance learning model - for survival prediction and lung
cancer diagnosis respectively. Finally, we compared the performance of the
models derived from the original and improved CT scans. Encoder-decoder
networks and CGANs improved the area under the curve (AUC) of survival
prediction from 0.52 to 0.57 (p-value<0.01). On the other hand, Encoder-decoder
network and CGAN can improve the AUC of lung cancer diagnosis from 0.84 to 0.88
and 0.89 respectively (p-value<0.01). Moreover, there are no statistically
significant differences in improving AUC by using encoder-decoder network and
CGAN (p-value=0.34) when networks trained at 75 and 100 epochs. Generative
models can improve the performance of low dose CT-based radiomics in different
tasks. Hence, denoising using generative models seems to be a necessary
pre-processing step for calculating radiomic features from low dose CTs.
Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study
http://arxiv.org/abs/2109.02252v1