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Radiomics in Medical Imaging: Methods, Applications, and Challenges
Fnu Neha, Deepak kumar Shukla
TL;DR
Radiomics offers quantitative, interpretable image representations, but feature instability, limited reproducibility, validation weaknesses, and clinical translation barriers remain persistent. This survey analyzes radiomics pipelines end to end, linking acquisition, preprocessing, segmentation, feature engineering, modeling, and validation decisions to robustness and translational validity. It concludes that future impact depends on methodological rigor, standardized and leakage-free validation, stability-aware selection, and principled integration with deep learning and multimodal frameworks.
Problem
Radiomics remains limited by acquisition and preprocessing sensitivity, high-dimensional modeling with limited data, insufficient validation, and barriers to clinical translation.
Method
The survey conducts an end-to-end, methodology-centric analysis of radiomics pipelines across feature extraction, selection, dimensionality reduction, modeling, validation, and clinical applications.
Results
The survey identifies that radiomics and deep learning have complementary strengths, with radiomics suited to limited-cohort, interpretable settings and deep learning suited to large-scale, complex perceptual tasks.
Takeaways & Limitations
Future progress requires standardized pipelines, stability-aware feature selection, leakage-free validation, and principled integration with deep learning and multimodal frameworks.
Takeaways & Limitations
Clinical adoption remains limited because most studies lack prospective or longitudinal validation and provide insufficient evidence of clinical utility.
Abstract
from arXiv · showhide
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.
1 Introduction
Radiomics converts medical images into quantitative features that complement qualitative interpretation and support disease characterization across modalities and clinical domains. This survey frames the field through an end-to-end, dependency-aware analysis because variability, high dimensionality, limited cohorts, and insufficient validation constrain reproducibility and translation.
- Radiomics foundations: Radiomics extracts quantitative descriptors of morphology, intensity, and spatial heterogeneity from medical images for structured analysis across CT, MRI, PET, ultrasound, and digital pathology.These representations support data-driven disease characterization and precision-medicine approaches.
- Clinical relevance: Clinical applications span oncology, neurology, and cardiology, including tumor characterization, subtype differentiation, grading, outcome prediction, therapy response, disease progression, and functional assessment.
- Open challenges: Methodological variability, high-dimensional features, and limited cohort sizes increase instability, overfitting, and reproducibility concerns, while insufficient external and multicenter validation weakens confidence in generalizability.
- Survey gap: Existing surveys rarely examine how methodological dependencies, validation practices, and variability sources jointly affect reproducibility and translation.
- Survey scope and contributions: This survey provides a unified, methodology-centric view from acquisition and preprocessing through feature engineering, modeling, evaluation, and clinical application, emphasizing robustness, reproducibility, and validation design.Its stated contributions include comparative analysis of feature and modeling strategies, validation-centered application review, and discussion of standardization, generalization, and deployment challenges.
2 Radiomics Pipeline
Radiomics converts medical images into quantitative descriptors through interdependent acquisition, segmentation, feature-extraction, and analysis stages, each introducing variability that affects reproducibility and downstream reliability. The pipeline therefore requires acquisition standardization, segmentation robustness, feature characterization, and transparent implementation choices.
- 2.1 Image Acquisition and Standardization: Acquisition heterogeneity in scanners, protocols, resolution, and reconstruction shifts image characteristics and extracted features across datasets.Texture and higher-order features are especially sensitive to interpolation, discretization, noise, and voxel anisotropy.
- 2.1 Image Acquisition and Standardization: Shape features are comparatively robust, whereas texture and wavelet features show the greatest acquisition-related instability, commonly assessed using ICC.Standardization and harmonization can mitigate these effects but introduce additional hyperparameters and modeling assumptions requiring transparent reporting.
- 2.2 Region of Interest (ROI) Segmentation: Segmentation choices range from manual to automated approaches, but even minor ROI boundary perturbations can substantially alter shape and higher-order texture features.The pipeline therefore motivates robustness analysis and consensus-based strategies such as probabilistic segmentation and multi-observer aggregation.
- 2.3 Radiomic Feature Extraction: Radiomic features comprise first-order intensity statistics, shape descriptors, texture matrices, and higher-order transformed features that capture complementary tissue characteristics.First-order features summarize intensity distributions, shape features encode geometry, texture features quantify spatial heterogeneity, and higher-order features support multiscale or frequency-domain characterization.
- 2.3 Radiomic Feature Extraction: Standardized software frameworks support reproducible extraction, but parameter settings, discretization, and preprocessing configurations remain important sources of inter-study variability.PyRadiomics is described as an open-source, IBSI-compliant feature-extraction library spanning first-order, shape, texture, and higher-order categories.
3 Feature Selection and Dimensionality Reduction
Radiomics feature selection and dimensionality reduction address high-dimensional, correlated feature spaces, but their stability and evaluation depend on method choice and strict validation design.
- Feature selection and dimensionality reduction are essential because radiomics produces high-dimensional, correlated features from limited cohorts, increasing instability and reducing generalization.
- Feature Selection Strategies: Filter, wrapper, and embedded methods select informative features using statistical relevance, iterative predictive evaluation, or model-integrated procedures, respectively.
- Feature Selection Strategies: Feature subsets vary across resampling splits, so post-hoc importance scores do not establish stable or causal relevance.
- Dimensionality Reduction: PCA reduces redundancy and improves numerical stability by retaining variance-maximizing components, but reduces feature interpretability.
- Dimensionality Reduction: Alternative reductions such as ICA, PLS, autoencoders, and kernel PCA capture nonlinear or higher-order structure but add complexity and data requirements.
- Validation Considerations: Selection and reduction must occur inside cross-validation or nested validation because applying them before splitting leaks test-set information and biases performance estimates.
4 Radiomics Modeling Approaches
Radiomics modeling spans interpretable classical learners, data-intensive deep learning, and ensemble or hybrid frameworks. Model suitability depends on feature properties, cohort size, model complexity, and validation rigor.
- Classical Machine Learning: Classical models remain effective for handcrafted features and limited samples, offering interpretability through explicit decision functions and feature-importance measures.
- Classical Machine Learning: Their performance remains sensitive to feature redundancy, acquisition variability, preprocessing, and limited capacity for complex nonlinear interactions.
- Deep Learning (DL)–Based Radiomics: Deep learning learns hierarchical representations directly from images and can model complex patterns, but requires adequate data, annotations, and cohort diversity.
- Ensemble and Hybrid Frameworks: Ensembles and hybrids combine learners, feature spaces, or modalities to address individual-model limitations, while adding training, hyperparameter, reproducibility, and deployment complexity.
- Radiomics vs. Deep Learning: Radiomics suits limited cohorts and transparent feature interpretation, whereas deep learning suits large datasets and complex perceptual tasks; neither is universally optimal.
- Radiomics vs. Deep Learning: Predictive performance depends on matching model capacity to available data: simple models can underfit high-dimensional features, while complex models can overfit limited datasets.
5 Related Work
Related work applies classical, deep, and hybrid radiomics across diverse diseases and imaging modalities. The surveyed studies combine handcrafted or learned features with conventional classifiers, ensembles, and multimodal strategies.
- Radiomics applications span neurodegenerative, stroke, cardiovascular, pulmonary, musculoskeletal, and oncologic imaging tasks, including characterization, stratification, and outcome prediction.
- Classical Radiomics and Machine Learning: Classical studies use CT or MRI handcrafted intensity, texture, shape, histogram, and gradient features with SVM, RF, LR, boosting, and related classifiers.
- Classical Radiomics and Machine Learning: Feature reduction strategies in classical studies include sparsity-driven regularization, recursive feature elimination, correlation analysis, and greedy search.
- The reviewed work includes tumor subtype classification, grading, molecular characterization, malignancy prediction, and survival-risk stratification across CT, MRI, ultrasound, and PET/CT.
- Deep and Hybrid Radiomics: Hybrid studies combine handcrafted radiomics with deep representations or use transfer learning, pretrained networks, pooling, concatenation, and conventional classifiers.
6 Evaluation Protocols and Validation
Reliable radiomics evaluation requires leakage-free validation, independent testing, and metrics that capture discrimination, calibration, stability, and uncertainty. External and temporal validation remain limited, and reported performance often declines under independent testing.
- Rigorous evaluation requires strict train/test separation, principled resampling, external validation, and comprehensive performance assessment.
- Train/Test Leakage and Cross-Validation: Nested cross-validation uses an inner loop for model selection and an outer loop for unbiased performance estimation.
- External and Temporal Validation: External validation tests robustness to acquisition and institutional distribution shifts, while temporal validation assesses stability under evolving clinical practice.
- External and Temporal Validation: External and temporal validation remain underrepresented, and performance frequently declines during independent testing because of cohort and acquisition variability.
- Train/Test Leakage and Cross-Validation: Preprocessing, feature selection, and dimensionality reduction must occur independently within each training fold to prevent optimistically biased estimates.
- Performance, Stability, and Statistical Reliability: Radiomics evaluation should report discrimination, class-imbalance metrics, calibration, uncertainty, and feature or model stability rather than accuracy alone.
7 Challenges and Limitations
Radiomics remains constrained by acquisition-sensitive features, high-dimensional modeling with limited data, inconsistent standardization, validation bias, and domain shift. These weaknesses restrict reproducibility, generalization, and clinical adoption.
- Radiomics limitations arise from acquisition and preprocessing variability, high-dimensional modeling under limited data, and weaknesses in validation and standardization.
- Feature and Pipeline Variability: Minor changes in scanners, reconstruction, voxel resolution, discretization, or segmentation can substantially alter feature distributions and undermine robustness under domain shift.
- Feature and Pipeline Variability: Texture- and filter-based features are less repeatable than first-order and shape features, compromising interpretability and model reliability when robustness is ignored.
- High Dimensionality and Limited Sample Sizes: With p ≫ n, radiomics faces variance inflation, unstable estimates, spurious associations, and limited identifiability, statistical power, and uncertainty estimation.
- Standardization and Reproducibility: Uneven standardization across acquisition, preprocessing, feature definitions, software, and validation produces inconsistent representations and non-comparable results.
- Validation, Generalization, and Clinical Translation: Overfitting and domain shift inflate retrospective performance or cause deployment failure, while limited prospective evidence, interpretability, and uncertainty modeling restrict clinical adoption.
8 Discussion and Future Work
Future radiomics work should combine interpretable handcrafted features with learning-based and multimodal representations while explicitly addressing robustness, uncertainty, and site heterogeneity. Standardized benchmarks and reporting are needed to make comparisons reproducible and clinically informative.
- Hybrid Radiomics–AI Models: Hybrid radiomics–transformer models combine structured radiomic descriptors with learned representations and attention-based interaction modeling, but require strict regularization and external validation.
- Multimodal Fusion: Multimodal fusion should model modality uncertainty and conditional dependence because naive concatenation can amplify noise and correlation effects.
- Self-Supervised Learning: Self-supervised representations may improve robustness, but evaluation must test generalization under domain shift, acquisition variability, alignment, redundancy, and interpretability.
- Federated Learning: Federated radiomics must address non-identically distributed sites, client drift, communication efficiency, aggregation bias, and cross-site stability.
- Standard Benchmarks and Reporting Standards: Radiomics lacks standardized benchmarks, with variation in tasks, cohorts, preprocessing, and validation limiting cross-study comparison and cumulative evidence synthesis.
- Standard Benchmarks and Reporting Standards: Future benchmarks should fix data splits, preprocessing, and reference metrics while reporting feature definitions, discretization, validation nesting, and uncertainty estimates.
9 Conclusion
The survey frames radiomics as an interpretable but fragile quantitative imaging paradigm whose reliability depends on coordinated methodological choices across the full pipeline. It concludes that standardization, stability-aware selection, leakage-free validation, and principled integration with learning and multimodal methods are more important than complexity alone.
- Radiomics offers interpretable feature representations, while this survey links acquisition, preprocessing, segmentation, feature engineering, modeling, and validation to reproducibility and translational validity.
- Feature instability, pipeline variability, high dimensionality, validation bias, inconsistent standardization, and limited external evaluation restrict generalization and clinical adoption.
- The review emphasizes methodological analysis rather than quantitative performance aggregation because heterogeneous datasets, tasks, and validation protocols preclude meaningful meta-analysis.
- Future progress depends on standardized pipelines, stability-aware feature selection, leakage-free validation, and principled integration with deep learning and multimodal frameworks.
Declarations
The declarations state that the author reports no competing interests and that data generation or analysis is not applicable to this manuscript.
- The author declares no competing interests.
- Data generation or analysis is not applicable because the manuscript does not report them.