Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters

Abstract

The reliability of radiomics features (RFs) is crucial for quantifying tumour heterogeneity. We assessed the influence of imaging, segmentation, and processing conditions (quantization range, bin number, signal-to-noise ratio [SNR], and unintended outliers) on RF measurement. Low SNR and unintended outliers increased the standard deviation and mean values of histograms to calculate the first-order RFs. Variations in imaging processing conditions significantly altered the shape of the probability distribution (centre of distribution, extent of dispersion, and segmentation of probability clusters) in second-order RF matrices (i.e. grey-level co-occurrence and grey-level run length), thereby eventually causing fluctuations in RF estimation. Inconsistent imaging and processing conditions decreased the number of reliably measured RFs in terms of individual RF values (intraclass correlation coefficient ≥0.75) and inter-lesion RF ratios (coefficient of variation <15%). No RF could be reliably estimated under inconsistent SNR and inclusion of outlier conditions. By contrast, with high SNR and no outliers, all first-order RFs, 11 (42%) grey-level co-occurrence RFs and five (42%) grey-level run length RFs showed acceptable reliability. Our study suggests that optimization of SNR, exclusion of outliers, and application of relevant quantization range and bin number should be performed to ensure the robustness of radiomics studies assessing tumor heterogeneity.

Table_1.docx

“…Interestingly, we found no significant correspondence between tumor volume (ml) and mutational status (Supplemental Table 3). Our results are consistent with those from Park et al (34) and Lee et al (35), testing the stability and reliability of radiomic features to evaluate tumor heterogeneity. Notably, Lee and collaborators (35), by studying the variability of radiomics features and their relationship with tumor size and shape upon 260 lung nodules, found that only a few features-including spherical disproportion and dissimilarity-showed high reproducibility in correlation with nodule status.…”

Tumor Heterogeneity: A Great Barrier in the Age of Cancer Immunotherapy

“…Another example is the use of radiomics, which is an emerging field of medical image analysis that utilizes radiological images to predict patient outcomes. It has been proposed that radiomics could be used to quantify tumor heterogeneity [ 84 , 85 ]. Since radiological images are frequently taken for diagnostic purposes, the ability to track tumor heterogeneity throughout tumor progression would be extremely valuable for furthering our understanding of tumor heterogeneity.…”

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

“…In feature selection, we selected 2 CTR features, Skewness and Kurtosis (6) based on histogram, and 2 PETR features, SUVmean and SUVmax 7, with high reproducibility for slice thickness condition changes. The study of stability and reproducibility of the radiomics features (6,7,(24)(25)(26)(27)(28)(29)(30)(31) shows multiple parameter changes (e.g., slice thickness) in general produces greater measurement errors. In this case, the selected 4 features only have good reproducibility against slice thickness.…”