Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features

Abstract

Objectives: This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features. Materials and Methods: This study was conducted using an abdominal phantom with liver nodules. We developed an image conversion algorithm using a residual feature aggregation network to reproduce radiomics features with CT images under various CT protocols and reconstruction kernels. External validation was performed using images from different scanners, consisting of 8 different protocols. To evaluate the variability of radiomics features, regions of interest (ROIs) were drawn by targeting the liver parenchyma, vessels, paraspinal area, and liver nodules. We extracted 18 first-order, 68 second-order, and 688 wavelet radiomics features. Measurement variability was assessed using the concordance correlation coefficient (CCC), compared with the ground-truth image. Results: In the ROI-based analysis, there was an 83.3% improvement of CCC (80/96; 4 ROIs with 3 categories of radiomics features and 8 protocols) in synthetic images compared with the original images. Among them, the 56 CCC pairs showed a significant increase after image synthesis. In the radiomics feature-based analysis, 62.0% (3838 of 6192; 774 radiomics features with 8 protocols) features showed increased CCC after image synthesis, and a significant increase was noted in 26.9% (1663 of 6192) features. In particular, the first-order feature (79.9%, 115/ 144) showed better improvement in terms of the reproducibility of radiomics feature than the second-order (59.9%, 326/544) or wavelet feature (61.7%, 3397/5504). Conclusions: Our study demonstrated that a deep learning model for image conversion can improve the reproducibility of radiomics features across various CT protocols, reconstruction kernels, and CT scanners.

Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study

“…Our results confirm limitations in the use of many features in conjunction with FBP and iterative reconstruction but also reveal novel opportunities with deep learning reconstruction that may be considered in retrospective data collection and future protocol implementations for radiomics [26]. Moreover, our results underline that the integration of deep learning into image processing has high potential to improve radiomics research, supporting conclusions from previous reproducibility studies [22,23].…”

“…We independently assessed four reconstruction algorithms for radiomics feature extraction, and our investigation encompassed the entire imaging chain including raw data acquisition. Our study thus differed from previous work, in which an image conversion filter was applied to reconstructed image data [22,23]. The phantom we used had the advantage of featuring complex textures similar to human tissues, enabling us to evaluate feature stability and discriminative power in a realistic setting.…”

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation

“…Lee et al [ 13 ] proposed a GAN to improve the reproducibility of CT-based radiomics features. We employed a generator network (G) architecture, including a hierarchical feature synthesis module from a prior study [ 13 ].…”

“…A recent study reported a deep learning algorithm capable of converting various computed tomography (CT) images derived from diverse CT protocols into target CT images [ 13 ]. This study used a generative adversarial network (GAN) to standardize CT images to improve the reproducibility of radiomics features.…”

Deep learning-based reconstruction on cardiac CT yields distinct radiomic features compared to iterative and filtered back projection reconstructions

“…DLR improves image quality compared with FBP or IR 18 – 20 and facilitates dose reduction while maintaining the image quality and diagnostic performance of CT scans 21 . The effect of other deep learning-based technology, for example, deep learning-based conversion of reconstruction kernel, on the reproducibility of radiomic features has been studied before 22 , 23 . However, the effects of DLR on radiomic features have not yet been compared to other reconstruction methods.…”