Radiomics in Medical Imaging: Methods, Applications, and Challenges

TL;DR

This survey critically analyzes the entire radiomics pipeline in medical imaging, highlighting methodological influences on robustness, reproducibility, and clinical translation, and discusses future research directions.

Contribution

It provides an end-to-end review of radiomics methods, emphasizing how design choices impact feature stability and model reliability, addressing gaps in existing literature.

Findings

Methodological decisions significantly affect feature stability.

Validation protocols are crucial for model reliability.

Open challenges include standardization and domain shift.

Abstract

Radiomics enables quantitative medical image analysis by converting imaging data into structured, high-dimensional feature representations for predictive modeling. Despite methodological developments and encouraging retrospective results, radiomics continue to face persistent challenges related to feature instability, limited reproducibility, validation bias, and restricted clinical translation. Existing reviews largely focus on application-specific outcomes or isolated pipeline components, with limited analysis of how interdependent design choices across acquisition, preprocessing, feature engineering, modeling, and evaluation collectively affect robustness and generalizability. This survey provides an end-to-end analysis of radiomics pipelines, examining how methodological decisions at each stage influence feature stability, model reliability, and translational validity. This paper reviews radiomic feature extraction, selection, and dimensionality reduction strategies; classical machine and deep learning-based modeling approaches; and ensemble and hybrid frameworks, with emphasis on validation protocols, data leakage prevention, and statistical reliability. Clinical applications are discussed with a focus on evaluation rigor rather than reported performance metrics. The survey identifies open challenges in standardization, domain shift, and clinical deployment, and outlines future directions such as hybrid radiomics-artificial intelligence models, multimodal fusion, federated learning, and standardized benchmarking.