Radiomics in Medical Imaging: Methods, Applications, and Challenges

2026-01-24

AI Summary

This study addresses the core challenges in radiomics—feature instability, poor reproducibility, and limited clinical translation—by systematically evaluating how methodological choices across the end-to-end pipeline (including image acquisition, preprocessing, feature engineering, modeling, and evaluation) impact model robustness and generalizability. It is the first to comprehensively uncover the interdependencies among pipeline components and underscores the critical need for rigorous validation protocols to prevent data leakage and assessment bias. By integrating feature selection, dimensionality reduction, classical machine learning, and deep learning—and further exploring emerging paradigms such as hybrid AI, multimodal fusion, and federated learning—the work identifies key determinants of reliability and highlights persistent challenges related to standardization, domain shift, and clinical deployment, offering a systematic roadmap to enhance the quality and clinical applicability of radiomics research.

Technology Category

Machine Learning: Feature Construction/ReformulationComputer Vision: Interpretability, Explainability, and TransparencyReasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

Web Mining and Content Analysis: Robustness and generalizability of Web mining methodsUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• radiomics
• feature instability
• reproducibility
• clinical translation
• validation bias

Innovation

• radiomics
• end-to-end pipeline
• feature stability
• clinical translation
• federated learning