The impact of radiomic feature selection on clinical modelling

Abstract

Purpose: Software choice often dictates what radiomic features can be extracted from images. This study analyses how different feature sets affect clinical models in two cancer cohorts: PET oesophagus (in-house dataset) and CT lung (open dataset). Methods and materials: 95 patients (65 training and 30 validation cohorts) who had undergone pre-treatment 18F-FDG-PET studies were included and classified as those who achieved complete pathological response (pCR) and those who did not. The primary tumours were segmented using a fixed threshold approach, radiomic features were extracted, features were annotated according to the international biomarker standardisation initiative. Feature reduction with the minimum redundancy maximum relevance method was performed. Logistical regression and random forest models for clinical outcomes were constructed and compared using ROC curves, decision curves, and McNemar's test. Results: Of the 5 feature sets tested, AUC values for the two models ranged between 0.6 and 0.87. Reduced feature sets did not have much overlap (10- 25%) and showed substantial differences in their ability to predict outcomes with our models and datasets. A combined superset showed the best performance (AUC 0.87) but did not show much reduction in feature correlation. Conclusion: High-dimensional data requires extensive feature reduction and increased scrutiny for overfitting, but if a proper methodology is applied, the results of combining multiple feature sets may be beneficial for modelling with radiomics in cancer. Limitations: The PET dataset is not very large (low incidence and expensive modality) and the event frequency is slightly below 40%. The fixed threshold segmentation may be less accurate for lesion size, but it was chosen to maximise radiomics stability. Ethics committee approval: The study was approved by the local ethics committee in accordance with the Helsinki Declaration and all patient information was anonymised prior to analysis.