Exploring the complexities of radiomics: an in-depth analysis of a machine learning pipeline for predicting rectal cancer therapy response using MRI

Developing radiomic biomarkers remains challenging due to the variability in imaging protocols across centres and the lack of standardised methodologies. This study evaluates the impact of different technical decisions in radiomics pipelines using multiparametric magnetic resonance imaging data from six centres for predicting therapy response in rectal cancer. Preprocessing, feature extraction, normalisation strategies, and machine learning (ML) models were assessed for robustness and generalisability. Key findings demonstrated that the preprocessing significantly enhanced the feature reproducibility and the importance of the selected ones over the complexity of the ML classifier. This analysis highlights the necessity of a unique pipeline and the complexity of radiomics biomarkers in real-world settings, particularly when handling highly imbalanced datasets. Several insights and methodologies have been presented that may support towards more conscious decisions when implementing radiomic systems. Future efforts should focus on integrating clinical/genomic/pathomics data to improve the predictive capabilities and facilitate the introduction into clinical practice.