Truth-based Radiomics for Prediction of Lung Cancer Prognosis

The purpose of this dissertation was to improve CT-based radiomics characterization by assessing and accounting for its systematic and stochastic variability due to variations in the imaging method. The anatomically informed methodologies developed in this dissertation enable radiomics studies to retrospectively correct for the effects CT imaging protocols and prospectively inform CT protocol choices. This project was conducted in three parts of 1) assessing of the bias and variability of morphologic radiomics features across a wide range of CT imaging protocols and segmentation algorithms, 2) assessing the applicability, sensitivity, and usefulness of applying bias correction factors retrospectively to patient data acquired with heterogenous CT imaging protocols, and 3) developing analytical techniques to reduce the variability of radiomics features by prospectively optimizing the CT imaging protocols.

In part 1 (chapters 2-4), the measurability of bias and variability of morphologic radiomcis features was assessed. In chapter 2, a theoretical framework was developed to guide the process of analyzing and utilizing quantitative features, including radiomics, derived from CT images. The framework outlined the key qualities necessary for successful quantification including biological and clinical relevance, objectivity, robustness, and implementability. In chapter 3, a method was developed to use anatomically informed lung lesion models to assess the bias and variability of morphology radiomics features as a function of CT imaging protocols and segmentation algorithms. The results showed that bias and variability of radiomics features are dependent on a complicated interplay of anatomical, imaging protocol, and segmentation effects. In chapter 4, the bias and variability of radiomics due to segmentation algorithms was explored in-depth for three segmentation algorithms across a range of image noise magnitudes. The segmentation algorithms were assessed by comparing their performance to an ideal radiomics estimator for a range of image quality characteristics. The results showed that the optimal segmentation algorithm was function of the specific noise magnitude and the radiomics features of interest.

In part 2 (chapter 5), an analysis was carried out using a Non-Small Cell Lung Cancer patient dataset to assess the applicability, sensitivity, and usefulness of correcting radiomics features for imaging protocol effects. The applicability was assessed by calculating bias correction factors from one set of anatomically informed lesion models and applying the correction factors to another set of anatomically informed lesion models. The sensitivity was assessed by applying idealized bias correction factors to the patient dataset with increasing bias correction magnitudes to determine the sensitivity of predictive models to the magnitude of the bias correction factors. Finally, the usefulness was assessed by applying the anatomically informed protocol-specific bias correction factors to the patient dataset and quantifying the change in the performance predictive model. The results showed that the bias correction factors are applicable when the bias correction factors are derived from and applied to lesion models with similar anatomical characteristics. The feature-specific sensitivity of prediction to bias correction factors was found to be as low as 1-5% and was typically in the range of 20-50%. The bias correction factors were applied to a patient population and were found to result in a small statistically significant improvement in the performance.

In part 3 (chapter 6), a method was developed and implemented to assess the minimum detectable difference of morphologic radiomics features as a function of protocol and anatomical characteristics. The analysis of the data was carried out to allow for evaluating and informing the recommendations of the Quantitative Imaging Biomarkers Alliance (QIBA) for lung nodule volumetry. The results showed that the minimum detectable difference for QIBA compliant protocols was a lower median value than the minimum detectable difference among all possible CT protocols. The techniques developed in this analysis can be used to prospectively optimize CT imaging protocols for improved quantitative characterization of radiomics features.

In conclusion, this dissertation developed methods to assess and account for the variability of radiomics features across CT imaging protocols and segmentation algorithms using anatomically informed lesion models.