Abstract: Rectal cancer (RC), one of the most common malignant tumors, has a high incidence rate and mortality rate worldwide. Radiomics turns medical images into high-dimensional mineable data through high-throughput extraction algorithms, where the methods include filter-based algorithms and texture analysis. All these features are then combined with machine learning or deep learning algorithms to provide objective evidence to facilitate accurate diagnosis, radiation staging, radiotherapy planning, or prognosis prediction. Multi-parametric magnetic resonance imaging has been considered as one of the best modalities for performing radiomics analysis on rectal cancer because it can capture most features about tumor heterogeneity and micro-environment information. In the past few years, magnetic resonance imaging (MRI)-based radiomics has shown great promise in a variety of fields, including tumor-node-metastasis staging, monitoring pathological high-risk factors, predicting genetic markers, neoadjuvant therapy response evaluation, and prognostic survival analysis in rectal cancer. In this paper, we provide an overview of the current state-of-the-art on MRI radiomics for rectal cancer and present a comparison between the available methods of feature extraction, and provide a critical discussion of current issues and possible developments that might be pursued in future research on this topic.
Zhu et al. (Tue,) studied this question.