UCLA

Radiomic features are quantitative metrics calculated over regions of interest on medical images. Tumor-specific radiomic features can describe tumor characteristics such as shape, attenuation, and tissue heterogeneity. The promise of radiomics to link with tumor biology, treatment outcome, and pathology has been explored extensively. However, radiomics is not yet fully validated as a clinical biomarker. Two crucial steps in validation of radiomics are the assessment of its clinical utility and technical validity. Large multicenter trials are still required to ensure clinical utility of radiomics and the technical validity of radiomics has not been adequately addressed. Radiomic features are quantitative metrics calculated over regions of interest on medical images. Tumor-specific radiomic features can describe tumor characteristics such as shape, attenuation, and tissue heterogeneity. The promise of radiomics to link with tumor biology, treatment outcome, and pathology has been explored extensively. However, radiomics is not yet fully validated as a clinical biomarker. Two crucial steps in validating radiomics are the assessment of its clinical utility and technical validity. Large multicenter trials are still required to ensure the clinical utility of radiomics, and the technical validity of radiomics has not been adequately addressed. Radiomics is data-driven and can get influenced by inconsistencies in image acquisition, image analysis, etc. While recent studies have demonstrated the susceptibility of radiomics to image acquisition, the reproducibility of CT radiomic features is not well established yet. Due to the unavailability of highly controlled datasets, previous efforts have been restricted to phantom data, limited patient cohorts representing narrow CT parameter ranges, or univariable analysis of a few CT parameters. Furthermore, enforcement of harmonization strategies is needed to handle related inconsistencies. Thus far, only a few limited efforts have explored such strategies; however, harmonization of radiomics is not resolved yet, and continued research and evaluations are necessary. This dissertation addressed the existing knowledge gap in understanding the variability of radiomic features and investigated potential strategies for harmonizing the radiomics approach. We investigated the effects of a wide range of CT acquisition and reconstruction parameters (dose, kernel, and slice thickness) on radiomic features in a realistic setting using clinical low-dose lung cancer screening cases. A computational pipeline was used that generated a unique and highly controlled dataset suitable for assessing the technical validity of radiomic features. We performed univariable and multivariable exploration of reproducibility of well-known radiomic features. Only a few features were reproducible in response to variation of dose and kernel, and the majority of radiomic features were impacted by slice thickness. Multivariable analyses revealed interactions among CT parameters, suggesting that selecting specific combinations of CT parameters can adjust for (or worsen) the impact of CT condition variations. We tested and compared two harmonization methods of Generative Adversarial Networks (GAN) and ComBat. A previously developed GAN model, Pix2Pix, was applied to sub-volumes surrounding lung nodules to transform lung nodule images at different CT conditions into harmonized images with radiomic features similar to a designated baseline CT condition. The ComBat method was applied separately to the radiomic feature data to estimate and adjust the deviations of radiomic features of non-baseline CT conditions to the baseline. The two mitigation techniques reduced radiomic feature variabilities at specific dose, kernel, and slice thickness ranges. Our findings advise on the inclusion of a harmonization procedure in the radiomics approach to avoid facing technical challenges in multicenter studies. Harmonization can be achieved via careful radiomic feature selection based on reproducibility or by applying an effective mitigation technique. While further evaluation remains a future, we illustrated the possibility of alleviating some variabilities due to CT image acquisition variations. Hence, there is a potential for the inclusion of these techniques in harmonization procedures. If validated, radiomics can be a valuable tool for clinical decision-making. Our explorations into the reproducibility and harmonization of radiomics contribute to enabling meaningful validation of radiomics.