Background
tumour
RENAL, TUMOUR
TRACERx Renal (NCT03226886) [Radiomic analysis of pre-surgical contrast-enhanced CT (CE-CT) scans presents an opportunity to non-invasively identify imaging features that can predict prognostic genomic and histopathological metrics, potentially enabling clinical translation of molecular markers via radiological scans. Here we leverage TRACERx Renal to develop accurate radiomic predictors of an array of clinically relevant genomic and histopathological metrics in ccRCC and propose a novel group feature selection method that facilitates radiomic signature interpretability.There is growing consensus in the radiomics community that whilst obtaining properly validated measures of radiomics model performance is essential, it is also necessary to develop a meaningful interpretation of the predictive model [Feature selection methods are known to improve machine learning model performance and while many studies have assessed the impact of different feature selection methods in the context of radiomic studies [Determining the interpretability of radiomics features is somewhat subjective, but features derived from the tumour shape (such as tumour volume and surface area) can be easily understood by a lay person, whereas features derived from pixel grey level distributions (such as mean value or entropy) require some specialist knowledge, and features relating to image texture features are not well understood, even by image processing experts.We hypothesise that using selection procedures that do not account for individual feature interpretability may lead to the discovery of radiomics signatures that are harder to interpret, and so we have developed a novel feature selection strategy designed to nudge the ML pipeline towards discovering more interpretable signatures. Such a biased selection procedure could theoretically compromise model performance, so we compare the prediction performance of models built using different feature selection strategies.To improve the interpretability of the final signature we employed three approaches: (1) extending the correlation-based feature reduction approach by retaining features with simpler over more complex interpretations; (2) a related supervised feature group selection step that treats the inclusion or exclusion of feature groups from the model as a tuning parameter that can be optimised using cross-validation; (3) use of tumour sub-segmentations to generate augmented radiomic feature sets to relate features in the radiomics signature back to their corresponding sub-segmented regions.This study aims to:Evaluate the performance of CE-CT radiomics predictions of a range of novel prognostic genetic features and clinical targets in the TRACERx Renal cohort (101 patients), and determine which targets warrant further study in the final cohort (320 patients).Compare the interpretability of radiomics signatures obtained in these data using standard feature selection methods and the proposed feature selection methods.Evaluate the impact of tumour sub-segmentation on radiomics signature interpretability.