Radiomics in Medical Imaging: Methods, Applications, and Challenges
- 1Department of Computer Science, Kent State University, Kent, OH 44242, USA
- 2Rutgers Business School, Rutgers University, Newark, NJ 07102, USA
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.
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BibTeX
@article{neha2026radiomics,
title = {{Radiomics in Medical Imaging: Methods, Applications, and Challenges}},
author = {Fnu Neha and Deepak Kumar Shukla},
journal = {Journal of Imaging},
year = {2026},
month = may,
doi = {10.3390/jimaging12060220},
url = {https://doi.org/10.3390/jimaging12060220},
note = {CC BY},
}
RIS
TY - JOUR
TI - Radiomics in Medical Imaging: Methods, Applications, and Challenges
AU - Fnu Neha
AU - Deepak Kumar Shukla
PY - 2026
DA - 2026/05/23
JO - Journal of Imaging
DO - 10.3390/jimaging12060220
UR - https://doi.org/10.3390/jimaging12060220
AB - 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.
SN - 2313-433X
ER -