Optimal Statistical Incorporation of Independent Feature Stability Information into Radiomics Studies

Abstract

Conducting side experiments termed robustness experiments, to identify features that are stable with respect to rescans, annotation, or other confounding effects is an important element in radiomics research. However, the matter of how to include the finding of these experiments into the model building process still needs to be explored. Three different methods for incorporating prior knowledge into a radiomics modelling process were evaluated: the naïve approach (ignoring feature quality), the most common approach consisting of removing unstable features, and a novel approach using data augmentation for information transfer (DAfit). Multiple experiments were conducted using both synthetic and publicly available real lung imaging patient data. ignoring additional information from side experiments resulted in significantly overestimated model performances meaning the estimated mean area under the curve achieved with a model was increased. Removing unstable features improved the performance estimation, while slightly decreasing the model performance, i.e. decreasing the area under curve achieved with the model. the proposed approach was superior both in terms of the estimation of the model performance and the actual model performance. our experiments show that data augmentation can prevent biases in performance estimation and has several advantages over the plain omission of the unstable feature. the actual gain that can be obtained depends on the quality and applicability of the prior information on the features in the given domain. this will be an important topic of future research.

Radiomics in medical imaging—“how-to” guide and critical reflection

“…Different than image-wise dependency corrections, post-reconstruction batch harmonization has been proposed in order to harmonize radiomic feature sets originating from different institutes, which is a method called ComBat [ 124 – 126 ]. Furthermore, a recent study investigated the performance of data augmentation instead of feature elimination to incorporate the knowledge on influencing factors on radiomic features [ 127 ].…”

Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients

“…The acceptance of radiomics models and their translation into clinical routine depends on their performance in improving diagnostic accuracy, especially when conventional image evaluation leads to equivocal results. In this context, generating radiomics models and identifying reliable, repeatable, and generalizable features is essential [4].…”

“…Furthermore, feature repeatability may depend on phenotype differences in extracranial tumors [2,3]. Therefore, identifying robust features is essential to ensure the reliable performance of radiomics models for clinical diagnostics [4]. For example, robust image features can be identified using test-retest analyses in phantoms, which are repeatedly examined with the same acquisition protocol.…”

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.…”