Extracting Multimodal Imaging Features for Accurate Diagnosis

For an accurate diagnosis, more texture features and digital information needed to be extracted from PET/CT images. All data were standardized and normalized to facilitate the statistical analysis of index evaluation values. In total, 2662 features were extracted, including 2436 CT-features of primary tumor and lymph node extracted using the PyRadiomics platform. The features were developed to standardize the calculation of the radiomic feature algorithms and ease the feature extraction process to improve reproducibility of the findings (Griethuysen et al. 2017 (link)). Additionally, 216 PET-features of primary tumors and lymph nodes were automatically extracted using the Chang Gung Image Texture Analysis package in MATLAB 2012a (MathWorks Inc., Natick, MA, USA) (Fang et al. 2014 (link)). The CT-features were extracted based on the original image and by applying Laplacian of Gaussian and wavelet filters. To extract the PET-features, the SUV values contained within the ROIs were relatively resampled to 64 different values to yield a limited range of values; this was done to reduce the noise and to normalize the images (Yang et al. 2017 (link)).