Abstract
Radiomics models have been extensively used to predict clinical outcomes across various applications. However, their generalizability is often limited by undesirable feature values variability due to diverse imaging conditions. To address this issue, we previously developed a dual-domain deep learning approach to recover ground truth feature values in the presence of known blur and noise. The model consists of a differentiable approximation for radiomics calculation and a dual-domain loss function. We demonstrated model performance for gray-level co-occurrence matrix (GLCM) and histogram-based features. In this work, we extend the method to gray-level run length matrix (GLRLM) feature recovery. We introduce a novel algorithm for the differentiable approximation of GLRLMs. We assessed the performance of the GLRLM feature restoration network using lung CT image patches, with a focus on the accuracy of recovered feature values and classification performance between normal and COVID-positive lungs. The proposed network outperformed the baselines, achieving the lowest MSE in GLRLM feature recovery. Furthermore, a classification model based on the recovered GLRLM features reached an accuracy of 86.65%, closely aligning with the 88.85% accuracy of models using ground truth features, compared to 82.00% accuracy from degraded features. These results demonstrate the potential of our method as a robust tool for radiomics standardization.