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Radiomics in Medical Imaging: Methods, Applications, and Challenges

Fnu Neha, Deepak kumar Shukla

TL;DR

This survey addresses the robustness and translational challenges of radiomics in medical imaging by offering an end-to-end, methodology-centered analysis of the entire pipeline—from image acquisition and ROI segmentation to feature extraction, selection, modeling, and validation. It contrasts classical machine learning, deep learning, and hybrid approaches, while emphasizing rigorous validation practices aimed at preventing data leakage and overfitting. The authors highlight open challenges such as standardization, domain shift, and clinical deployment, and propose future directions including hybrid radiomics–AI models, multimodal fusion, federated learning, and standardized benchmarks to improve reproducibility and clinical impact. Overall, the work clarifies how design choices across the radiomics pipeline influence feature stability, model reliability, and translational validity, guiding principled improvements for real-world application.

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.

Radiomics in Medical Imaging: Methods, Applications, and Challenges

TL;DR

This survey addresses the robustness and translational challenges of radiomics in medical imaging by offering an end-to-end, methodology-centered analysis of the entire pipeline—from image acquisition and ROI segmentation to feature extraction, selection, modeling, and validation. It contrasts classical machine learning, deep learning, and hybrid approaches, while emphasizing rigorous validation practices aimed at preventing data leakage and overfitting. The authors highlight open challenges such as standardization, domain shift, and clinical deployment, and propose future directions including hybrid radiomics–AI models, multimodal fusion, federated learning, and standardized benchmarks to improve reproducibility and clinical impact. Overall, the work clarifies how design choices across the radiomics pipeline influence feature stability, model reliability, and translational validity, guiding principled improvements for real-world application.

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.
Paper Structure (50 sections, 2 theorems, 10 equations, 8 figures, 9 tables, 1 algorithm)

This paper contains 50 sections, 2 theorems, 10 equations, 8 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Radiomics Pipeline
  3. Image Acquisition and Standardization
  4. Region of Interest (ROI) Segmentation
  5. Radiomic Feature Extraction
  6. Feature Selection and Dimensionality Reduction
  7. Feature Selection Strategies
  8. Dimensionality Reduction
  9. Validation Considerations
  10. Radiomics Modeling Approaches
  11. Classical Machine Learning (CML)-Based Radiomics
  12. Deep Learning (DL)--Based Radiomics
  13. Ensemble and Hybrid Radiomics Frameworks
  14. Radiomics vs. Deep Learning
  15. Related Work
  16. ...and 35 more sections

Key Result

Theorem 1

Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text. Example theorem text.

Figures (8)

  • Figure 1: End-to-end radiomics pipeline with representative sources of variability and bias.
  • Figure 2: Effect of ROI boundary perturbations on shape and texture feature values, illustrating segmentation-induced variability. Fig. 2(a) shows an ROI. Fig. 2(b) represents boundary perturbations. Fig. 2(c) and Fig. 2(d) show their downstream effects on radiomic features.
  • Figure 3: Schematic illustration of feature selection and dimensionality reduction in high-dimensional radiomic analysis.
  • Figure 4: Classical Machine learning Models in Radiomics
  • Figure 5: Model complexity vs. data size trade-off in radiomics
  • ...and 3 more figures

Theorems & Definitions (7)

  • Theorem 1: Theorem subhead
  • Proposition 2
  • Example 1
  • Remark 1
  • Definition 1: Definition sub head
  • proof
  • proof : Proof of Theorem \ref{['thm1']}