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Radiomics

Radiomics is a quantitative approach to medical imaging that involves the high-throughput extraction of numerous features from standard-of-care radiological images, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), to characterize tissue and lesion properties like shape, intensity, and texture that are often imperceptible to the human eye.^[1] This methodology converts traditional imaging data into mineable, high-dimensional datasets, enabling the development of predictive models for non-invasive diagnosis, prognosis, and treatment response assessment, particularly in oncology where it helps quantify tumor heterogeneity and phenotype.^[2] By integrating radiomic features with clinical, genomic, or proteomic data, radiomics supports personalized medicine and bridges imaging with other omics disciplines, such as radiogenomics.^[3] The radiomics workflow generally comprises several key steps: image acquisition and preprocessing to ensure consistency across scanners and protocols; segmentation of regions of interest, which can be manual, semi-automated, or AI-driven; feature extraction yielding hundreds to thousands of quantitative descriptors (e.g., first-order statistics, shape-based, and texture features); feature selection to reduce dimensionality and mitigate overfitting; and finally, model construction using machine learning or statistical methods for clinical application.^[4] Standardization efforts, such as those by the Image Biomarker Standardization Initiative (IBSI), aim to improve reproducibility by defining feature calculation protocols, addressing variability from imaging parameters and software.^[2] Since its formal conceptualization in 2012, radiomics has seen rapid growth, with annual publication volumes increasing from 254 in 2017 to 3,140 in 2023, driven by advancements in artificial intelligence and deep learning that enhance feature extraction and model accuracy.^[5] Applications extend beyond oncology to fields like neurology and cardiology, but challenges persist, including data quality inconsistencies, segmentation variability, and the need for prospective validation to translate models into routine clinical practice.^[5] Ongoing initiatives focus on interoperability and ethical AI integration to realize radiomics' potential in precision healthcare.^[3]

Definition and Fundamentals

Definition and Scope

Radiomics is defined as the high-throughput extraction of a large number of quantitative features from medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), to provide data-driven insights that extend beyond traditional visual assessment by radiologists.^[6] This approach converts imaging data into mineable high-dimensional datasets, enabling the identification of subtle patterns and phenotypes not discernible to the human eye.^[7] The scope of radiomics encompasses the integration of advanced imaging techniques with bioinformatics and machine learning to support precision medicine, particularly in oncology, by linking image-derived phenotypes to clinical outcomes. It distinguishes itself from qualitative radiology, which relies on subjective interpretation, and from radiogenomics, which specifically correlates imaging features with genomic or molecular data rather than focusing solely on phenotypic characterization from images.^[8] Key terms in radiomics include image-derived features such as shape (e.g., volume, sphericity), texture (e.g., heterogeneity measures like gray-level co-occurrence matrix), and intensity-based descriptors (e.g., histogram statistics), which collectively generate high-dimensional data for subsequent analysis.^[6] As an interdisciplinary field, radiomics involves collaboration among radiologists for image interpretation, oncologists for clinical integration, and data scientists for feature extraction and modeling, thereby facilitating personalized treatment strategies in precision medicine.^[9]

Core Principles

Radiomics is grounded in the principle of converting medical images into high-dimensional, quantitative data to uncover hidden patterns and relationships that extend beyond traditional visual assessment. At its core, radiomics assumes that imaging data encapsulate spatiotemporal information about underlying biological processes, enabling the extraction of features that reflect tumor heterogeneity, microenvironment, and response to therapy. This approach relies on rigorous mathematical and statistical methods to ensure that extracted features are not only reproducible but also clinically meaningful, facilitating the transition from descriptive radiology to predictive analytics.^[6] A fundamental tenet of radiomics is the emphasis on reproducibility, which necessitates standardized protocols to mitigate variability introduced during image acquisition, processing, and analysis. Without standardization, inter- and intra-observer differences, as well as equipment-specific artifacts, can compromise the reliability of radiomic features. The Image Biomarker Standardization Initiative (IBSI) addresses this by providing consensus-based definitions, nomenclature, and reference values for 169 common radiomic features, enabling calibration across software platforms and promoting consistent feature extraction. Studies evaluating IBSI-compliant tools have demonstrated high reproducibility rates, with up to 95% agreement in feature values across platforms when protocols are followed.^[10]^[11] Radiomics marks a paradigm shift from qualitative imaging, which depends on subjective interpretation by radiologists, to quantitative imaging that generates objective, mineable datasets for computational analysis. Qualitative assessments often overlook subtle tissue variations, whereas radiomics extracts hundreds of features—such as shape, intensity, and texture descriptors—from regions of interest, transforming images into analyzable vectors that support data-driven insights. This quantitative framework allows for the discovery of imaging biomarkers without requiring prior biological hypotheses, as high-throughput feature extraction can reveal associations with genomic profiles or clinical outcomes through correlative analyses.^[12]^[13]^[6] Despite these advances, standardization remains a significant challenge due to the influence of scanner variability and voxel size on feature robustness. Differences in CT scanner models, reconstruction kernels, and voxel resolutions can alter feature values by 10-50%, affecting metrics like gray-level co-occurrence matrix-based texture features. For instance, increasing voxel size from 1 mm to 5 mm can reduce the sensitivity of first-order statistics, while scanner-specific noise patterns introduce systematic biases. To counter this, resampling techniques and robust feature selection are employed, though full harmonization across multi-center datasets requires ongoing protocol refinements.^[14]^[15]^[3] Ethical considerations are paramount in radiomics, particularly regarding data privacy in handling high-dimensional datasets that often include sensitive patient information from large cohorts. The aggregation of thousands of features per image amplifies risks of re-identification, even after de-identification, necessitating privacy-preserving techniques like federated learning to enable multi-institutional collaboration without centralizing raw data. Compliance with regulations such as GDPR or HIPAA is essential to protect patient autonomy, while ensuring equitable access to radiomic tools avoids exacerbating healthcare disparities.^[16]^[17]

Historical Development

Origins and Early Milestones

The concept of radiomics emerged in the early 2000s, driven by advancements in medical imaging technologies such as multi-slice CT and FDG-PET, which enabled the quantitative extraction of tumor heterogeneity beyond traditional size and shape measurements. These developments built on earlier texture analysis techniques dating back to the 1970s but gained momentum in oncology as a means to non-invasively link imaging phenotypes to underlying biology. The field was formally defined and popularized in a seminal 2012 paper by Lambin et al. in European Journal of Cancer, which introduced "radiomics" as the high-throughput mining of hundreds of quantitative image features to support personalized medicine, emphasizing its potential to complement or replace invasive biopsies.^[18] Early milestones focused on proof-of-concept applications in lung cancer, where texture analysis of CT and PET images demonstrated associations with tumor aggressiveness and prognosis. In 2008, Al-Kadi et al. applied texture analysis to contrast-enhanced CT images of lung tumors, showing that features like uniformity and entropy could distinguish aggressive from nonaggressive lesions, improving staging accuracy.^[19] This was extended in 2009 by El Naqa et al., who used PET-based texture, shape, and intensity-volume histogram features in a cohort of 38 non-small cell lung cancer patients to predict radiotherapy response, achieving correlations with local control. By 2010, Ganeshan et al. further validated unenhanced CT texture features in 18 non-small cell lung cancer cases, linking them to tumor glucose metabolism (SUV) and clinical stage, providing initial evidence for radiomics' biological relevance.^[20] A key foundational publication was the 2014 study by Aerts et al., which developed a CT-based radiomic signature from 440 features (including shape, texture, and wavelet transforms) in 422 lung adenocarcinoma patients, identifying a 4-feature model that robustly predicted overall survival across independent cohorts (hazard ratio approximately 3.0 in validation set) and correlated with gene expression pathways related to cell cycling.^[21] In the mid-2010s, the release of PyRadiomics in 2017 as an open-source Python package standardized the extraction of over 100 features, enabling reproducible research and accelerating adoption by providing a reference implementation compliant with the Image Biomarker Standardization Initiative.^[22] Initial progress in radiomics was bolstered by funding from the National Cancer Institute (NCI) in the United States, supporting US-based studies like Aerts et al. at Dana-Farber Cancer Institute, and European Union consortia that backed Dutch efforts at the MAASTRO Clinic, where Lambin and colleagues advanced the field's conceptual and technical foundations.^[21]

Evolution and Key Advancements

Following the foundational work in the early 2010s, radiomics experienced significant growth from 2015 onward, driven by advancements in computational tools and machine learning integration. A key development was the rise of open-source software that democratized feature extraction, notably the 3D Slicer Radiomics extension, which encapsulates the PyRadiomics library to compute a wide array of features from medical images in a user-friendly platform. This tool, integrated into the widely used 3D Slicer environment, facilitated reproducible analysis and accelerated research adoption by enabling batch processing and visualization without extensive programming expertise. Concurrently, efforts to standardize radiomics workflows gained momentum with the launch of the Image Biomarker Standardization Initiative (IBSI) in 2016, an international collaboration that defined nomenclature, processing guidelines, and reference values for 169 common features to ensure interoperability across software and reduce variability in results.^[23]^[10] Integration with deep learning marked a pivotal shift post-2015, particularly from 2017, when convolutional neural networks (CNNs) began replacing or augmenting handcrafted feature extraction with end-to-end learning approaches. Early deep learning-based radiomics (DLR) models extracted hierarchical features directly from raw images, outperforming traditional methods in predictive accuracy. This hybrid paradigm addressed limitations in hand-engineered features by capturing subtle, non-linear patterns, leading to applications in tumor subtyping and treatment response prediction. By 2018, radiomics expanded to multi-modal imaging, combining modalities like CT, MRI, and PET to yield complementary features that enhanced model robustness; for instance, multi-modal signatures improved glioma grading by integrating structural and functional data, with studies reporting AUC values exceeding 0.90 in validation cohorts.^[24] Global adoption surged in the late 2010s, evidenced by an exponential rise in publications—over 1,500 radiomics-related entries on PubMed by 2020—reflecting its integration into clinical trials for oncology endpoints like survival prediction. During the 2020 COVID-19 pandemic, radiomics played a crucial role in chest CT analysis, with models extracting texture features to predict disease progression and severity, achieving sensitivities above 85% in distinguishing viral pneumonia from other etiologies and aiding resource allocation in overwhelmed healthcare systems.^[25]^[26] Recent milestones from 2023 to 2025 have focused on AI-radiomics hybrids, where transformer architectures and generative models fuse radiomic signatures with deep features for tasks like early detection, yielding improved generalizability across datasets; for example, the 2024 SNMMI AI Task Force Radiomics Challenge advanced machine learning models for survival prediction in lymphoma.^[27] Regulatory progress includes FDA clearances for AI-enhanced imaging tools incorporating radiomic principles, such as those for lung nodule assessment in oncology, signaling broader clinical translation. Publication growth continued exponentially, exceeding 13,000 PubMed entries by late 2024.^[28]^[29]^[30]

Methodology

Image Acquisition and Preprocessing

Image acquisition in radiomics begins with standardized protocols using common medical imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). For CT scans, parameters typically include slice thicknesses of 1-5 mm to balance resolution and noise, with in-plane resolutions around 1 mm; contrast agents are often employed to enhance tumor visibility. MRI protocols vary by sequence (e.g., T1-weighted, T2-weighted) but emphasize high spatial resolution (e.g., 1x1 mm in-plane, 3-5 mm slices), and may incorporate gadolinium-based contrast for better delineation. PET imaging focuses on standardized uptake value (SUV) quantification, with resolutions limited by positron range (typically 4-6 mm), and often combined with CT for attenuation correction. These protocols ensure sufficient image quality for subsequent feature extraction while minimizing artifacts.^[31] Preprocessing is essential to standardize images and mitigate technical variations before analysis. Key steps include resampling to isotropic voxel sizes, such as 1x1x1 mm³ using interpolation methods like B-spline, to enable consistent feature computation across modalities. Intensity normalization techniques, like z-score standardization (subtracting mean and dividing by standard deviation), adjust for differences in signal scales, particularly in MRI where intensities lack absolute units. Noise reduction is achieved through filters such as Gaussian smoothing with sigma values of 1-2 mm, which preserves edges while reducing random fluctuations. These steps enhance feature reproducibility without altering underlying biological information.^[31]^[32] Sources of variability in radiomic features arise primarily from inter-scanner and inter-protocol differences, which can significantly impact stability. In CT, variations in Hounsfield unit calibration across manufacturers lead to inconsistencies in texture features, with studies showing up to 20-30% feature variation between scanners. Similar issues occur in MRI due to field strength differences (1.5T vs. 3T) and sequence parameters, while PET suffers from reconstruction algorithm variations affecting SUV-based features. Such technical noise can confound biological signals, reducing model generalizability in multi-center studies.^[31]^[33] To address these challenges, best practices include harmonization techniques like ComBat, a statistical method originally developed in 2007 for genomics and later adapted for multi-center imaging data integration.^[34] ComBat adjusts feature distributions by modeling and removing site-specific effects while preserving biological variance, demonstrating improved reproducibility in CT features, such as higher concordance correlation coefficients.^[35]^[33] It is particularly effective for batch-effect correction in datasets from diverse scanners, ensuring robust radiomic pipelines. Following preprocessing, images proceed to segmentation for region-of-interest definition.

Image Segmentation

Image segmentation in radiomics involves delineating regions of interest (ROIs) or volumes of interest (VOIs) from medical images to define the boundaries from which quantitative features are extracted, ensuring that subsequent analyses reflect the relevant anatomical or pathological structures.^[3] This step is foundational, as inaccuracies in segmentation can propagate errors to feature computation and model performance.^[36] Manual segmentation, performed by experts using tools like 3D Slicer, relies on human judgment to trace ROIs slice by slice, but it is time-intensive and prone to subjective biases.^[3] In contrast, automated and semi-automated methods aim to reduce variability; thresholding separates regions based on intensity values, while edge detection identifies boundaries through gradient changes in pixel intensities.^[3] Semi-automated approaches, such as the GrowCut algorithm implemented in 3D Slicer, use interactive region-growing where users provide initial seed points for foreground and background, allowing the algorithm to iteratively expand based on similarity metrics, achieving higher reproducibility (intraclass correlation coefficient [ICC] of 0.85 ± 0.15) compared to manual methods (ICC of 0.77 ± 0.17).^[37] Key challenges in segmentation include inter-observer variability, which arises from differing interpretations among experts and is quantified using Cohen's kappa statistic, with reported values ranging from 0.23 to 0.70 in MRI-based tumor delineation.^[38] Tumor heterogeneity in oncology further complicates this process, as irregular microstructures and varying intensities within lesions hinder consistent boundary definition and affect the reliability of extracted features.^[39] Advanced methods address these issues through deep learning architectures like U-Net, introduced in 2015, which employs a U-shaped convolutional network with contracting and expanding paths to capture context and enable precise localization in biomedical images, often outperforming traditional approaches in segmentation accuracy.^[40] Active contours, or "snakes," provide another evolution by iteratively deforming an initial curve to minimize an energy function that aligns it with image edges, though they remain sensitive to noise and initial placement.^[41] Validation of segmentation accuracy typically employs the Dice similarity coefficient (DSC), which measures overlap between segmented and reference regions, with values ranging from 0.778 to 1.0 indicating good agreement in lung cancer CT studies, though it may overlook subtle variations better captured by radiomics features.^[42]

Feature Extraction and Selection

Feature extraction in radiomics involves the quantitative computation of a large set of imaging descriptors from regions of interest (ROIs), typically delineated through prior segmentation, to capture tumor heterogeneity and phenotype beyond visual assessment.^[6] These features are derived from medical images such as CT, MRI, or PET scans and encompass a variety of mathematical transformations that encode intensity distributions, spatial relationships, and geometric properties. The process standardizes image preprocessing steps, including resampling, discretization (e.g., fixed bin size for gray-level quantization), and normalization, to ensure comparability across datasets.^[10] Radiomic features are broadly categorized into first-order, shape-based, and texture-based types, with additional filtered and higher-order variants in standardized frameworks. First-order features describe the distribution of voxel intensities within the ROI via histogram statistics, independent of spatial information; examples include mean intensity, which quantifies average signal value, skewness, measuring asymmetry in the intensity distribution, and kurtosis, indicating peakedness.^[2] Shape-based features characterize the three-dimensional geometry of the ROI, such as volume, surface area, sphericity (ratio of surface area to a sphere of equivalent volume), and compactness, providing insights into tumor morphology without intensity dependence.^[43] Texture features quantify spatial patterns and heterogeneity, often using matrix-based methods; the gray-level co-occurrence matrix (GLCM) assesses pairwise voxel relationships in terms of contrast (local intensity variations), homogeneity (closeness of GLCM elements to the diagonal), and entropy (disorder in the matrix), while the gray-level run-length matrix (GLRLM) evaluates consecutive runs of similar intensities, yielding metrics like run-length non-uniformity (variation in run lengths) and gray-level non-uniformity (distribution of gray levels in runs).^[44] These categories align with the Image Biomarker Standardization Initiative (IBSI), which defines and benchmarks 169 core features to promote interoperability across tools. Extraction is typically performed using specialized software that automates the computation from segmented ROIs, yielding over 100 features per image in standard pipelines, though comprehensive suites can generate thousands when including filters (e.g., Laplacian of Gaussian for edge enhancement) and wavelet transformations.^[45] Popular open-source implementations include PyRadiomics, which supports IBSI-compliant extraction in Python; LIFEx, a user-friendly platform for multi-modality analysis; and MaZda, an earlier tool focused on texture metrics from MRI and CT.^[22] These tools handle discretization (e.g., 32 or 64 bins) and interpolation to mitigate variability from scanner differences.^[45] The high dimensionality of radiomic datasets—often thousands of features from small cohorts—introduces the curse of dimensionality, where feature redundancy and noise can lead to overfitting and reduced model generalizability.^[46] Feature selection mitigates this by identifying the most informative subset, balancing relevance, stability, and non-redundancy. Filter methods rank features based on intrinsic properties, such as Pearson correlation to assess linear relationships with outcomes or mutual information for non-linear dependencies, applied independently of classifiers for computational efficiency.^[47] Wrapper methods, conversely, evaluate feature subsets through iterative model training; recursive feature elimination (RFE), for instance, trains a classifier (e.g., support vector machine) on the full set and recursively removes the least important features based on weights or importance scores until an optimal subset remains.^[47] Hybrid approaches combine both for robustness, often retaining 5–20 features per model to adhere to empirical rules like 10 events per variable.^[47] To ensure reliability, extracted features undergo qualification for reproducibility, particularly against variations in segmentation or imaging protocols. Intraclass correlation coefficient (ICC) is the standard metric, with values >0.75 indicating good to excellent agreement across test-retest or inter-observer scenarios; for example, first-order features like energy often exceed ICC 0.90, while texture metrics such as GLRLM run entropy may require stricter preprocessing to achieve ICC >0.75.^[48] IBSI benchmarks and guidelines emphasize testing subsets of stable features (e.g., >70% ICC-qualified) to build robust signatures.

Data Analysis and Modeling

Data analysis in radiomics involves applying statistical and machine learning techniques to selected radiomic features to uncover associations with clinical outcomes and build predictive models. Univariate analysis typically examines individual features in isolation to assess their correlation with outcomes, such as using Kaplan-Meier survival curves to evaluate differences in patient survival based on feature thresholds. For instance, high-intensity run length emphasis, a texture feature, has been shown to stratify survival in non-small cell lung cancer cohorts via log-rank tests. These methods help identify promising features but are limited by ignoring interactions among variables. Multivariate modeling integrates multiple features to develop robust predictors, addressing the high dimensionality of radiomic datasets. Logistic regression is commonly employed for binary classification tasks, such as distinguishing responders from non-responders to therapy, by estimating odds ratios from feature coefficients. Machine learning approaches like random forests, which aggregate decision trees to reduce variance and handle non-linear relationships, and support vector machines (SVMs), which maximize margins in high-dimensional spaces, further enhance classification performance. For example, random forests have achieved areas under the receiver operating characteristic curve (AUCs) exceeding 0.80 in predicting treatment response across imaging modalities. Validation is essential to ensure model generalizability and mitigate overfitting, given the curse of dimensionality in radiomics. K-fold cross-validation, such as 10-fold procedures, partitions the dataset into k subsets, training on k-1 folds and testing on the held-out fold iteratively to estimate performance metrics like accuracy or AUC. External validation using independent cohorts from different institutions further confirms reproducibility, as internal validation alone often inflates performance due to dataset-specific biases. Studies adhering to these practices report more reliable hazard ratios and classification accuracies. Radiomic signatures are composite scores derived from multivariate models to prognosticate outcomes, often constructed using the Cox proportional hazards model for survival analysis. In this framework, the model assumes hazards are proportional over time, expressed as:

h(t|X) = h_0(t) \exp(\beta^T X)

where h(t|X) is the hazard at time t given features X, h_0(t) is the baseline hazard, and \beta are coefficients. The hazard ratio (HR) for a unit change in a feature is \exp(\beta), with values greater than 1 indicating increased risk; for example, an HR of 1.5 for a texture-based signature signifies a 50% higher hazard. These signatures, validated externally, have demonstrated prognostic value in oncology, such as stratifying recurrence risk in head and neck cancers.

Data Management

Database Creation

Radiomics databases encompass a variety of data types essential for quantitative image analysis. Unstructured data primarily consists of medical images stored in formats such as DICOM (Digital Imaging and Communications in Medicine), which supports radiology imaging with embedded metadata, and NIfTI (Neuroimaging Informatics Technology Initiative), commonly used for volumetric data in research pipelines. Structured data includes extracted radiomic features, such as shape, intensity, and texture metrics, along with metadata like patient demographics, clinical outcomes, and treatment details. These data types enable the integration of imaging with clinical and genomic information to support comprehensive analyses. The creation of radiomics databases involves systematic steps to ensure data quality and usability. Annotation pipelines typically begin with expert delineation of regions of interest (ROIs) or volumes of interest (VOIs) on images, often using semi-automated tools for tumor segmentation, followed by feature extraction and validation to generate standardized datasets. To address privacy concerns, federated learning approaches are increasingly employed, allowing multiple institutions to collaboratively train models on decentralized data without central sharing, thereby preserving patient confidentiality while aggregating insights from diverse sources. Standardization is critical for interoperability in radiomics database creation. The Cancer Imaging Archive (TCIA), established in 2011 by the National Cancer Institute, serves as a prominent example, hosting de-identified cancer imaging collections in DICOM format with associated clinical data to facilitate public access and reproducibility. Annotations are often aligned with ontologies like RadLex, developed by the Radiological Society of North America, which provides a lexicon of standardized radiology terms for consistent reporting and data mining. Scalability poses significant challenges for radiomics databases due to the volume of imaging data. Modern infrastructures handle petabyte-scale repositories by leveraging cloud platforms, such as Amazon Web Services (AWS) integrations in the 2020s, which offer secure, elastic storage and processing capabilities for large cohorts without on-premises hardware limitations. These systems support efficient querying and analysis of stored data for subsequent research applications.

Database Applications

Radiomics databases facilitate research by enabling data sharing that enhances reproducibility and supports multi-center validation studies. For instance, the Quantitative Imaging Network (QIN) challenges, sponsored by the National Cancer Institute, provide standardized imaging datasets that allow researchers to test radiomics pipelines across diverse cohorts, ensuring consistent feature extraction and model performance evaluation.^[49] These initiatives address variability in imaging protocols by promoting open-access repositories like The Cancer Imaging Archive (TCIA), which hosts collections such as those for brain tumors, enabling collaborative validation of radiomic signatures for tumor classification and prognosis. By pooling data from multiple institutions, such databases mitigate single-site biases and accelerate the development of robust, generalizable models.^[50] In clinical settings, radiomics databases integrate with electronic health records (EHRs) to support personalized medicine through real-time data querying and decision support. This linkage allows clinicians to retrieve patient-specific radiomic features alongside clinical metadata, facilitating tailored treatment planning, such as predicting response to therapy in oncology. Such integrations are particularly valuable in the 2020s for training AI models on large-scale datasets, where radiomics features from databases like TCIA enhance machine learning algorithms for automated tumor segmentation and outcome prediction.^[50] To overcome interoperability issues, radiomics databases employ data harmonization tools like PyRadiomics, which standardizes feature extraction according to guidelines from the Image Biomarker Standardization Initiative (IBSI). This ensures cross-database compatibility by normalizing features affected by scanner variations or preprocessing differences, thereby supporting seamless multi-site analyses. For brain tumor applications, databases such as the Brain Images of Tumors for Evaluation (BITE) provide harmonized MRI and ultrasound data, aiding in the validation of radiomic models for surgical planning and recurrence assessment.^[51] These tools are essential for translating research findings into clinical workflows while adhering to creation standards that emphasize data privacy and FAIR principles.

Clinical and Research Applications

Oncological Applications

Radiomics has emerged as a powerful tool in oncology, enabling the quantitative analysis of medical images to support cancer diagnosis, staging, prognosis, treatment planning, and monitoring. By extracting high-dimensional features from tumors and surrounding tissues, radiomics models can reveal subtle patterns not visible to the naked eye, facilitating personalized medicine approaches. In cancer care, these models integrate with clinical data to enhance decision-making, often outperforming traditional imaging assessments alone.^[52] In diagnosis and staging, radiomics aids tumor characterization by predicting molecular subtypes and genetic mutations non-invasively. For instance, in non-small cell lung cancer (NSCLC), texture-based radiomic features from CT scans have been used to predict EGFR mutations, achieving areas under the curve (AUC) greater than 0.8 in multiple studies, which helps guide targeted therapies without immediate biopsy.^[53] Similar applications in breast and colorectal cancers have demonstrated radiomics' utility in distinguishing benign from malignant lesions and refining TNM staging, improving early detection rates.^[54] For prognosis, radiomics-derived risk scores predict patient survival outcomes across various cancers. In glioblastoma, multiregional radiomic features from MRI, combined with machine learning, have stratified patients into risk groups with significant differences in overall survival, such as median survival extensions of several months in low-risk cohorts.^[55] These models often incorporate habitat imaging to capture intratumor heterogeneity, providing more robust predictions than standard clinical factors like age or tumor volume.^[56] Radiomics also supports treatment response assessment by detecting early recurrence and metastasis risk. In liver cancers, such as hepatocellular carcinoma, preoperative CT radiomics models have identified patients at high risk for extrahepatic metastasis post-resection, with predictive accuracies enabling tailored follow-up strategies.^[57] For recurrence detection, dynamic radiomic signatures from preoperative CT in pancreatic cancers have predicted early liver metastasis within six months post-resection, aiding timely interventions.^[58] In genetics assessment, radiomics serves as a non-invasive surrogate for biopsies through radiogenomics, correlating imaging features with genomic profiles. Studies in gliomas have shown strong predictive performance (e.g., AUC up to 0.98) between radiomic features and IDH mutations, while similar associations have been reported for KRAS mutations in NSCLC, allowing indirect inference of tumor biology for treatment selection.^[59] This approach reduces the need for invasive procedures, particularly in inoperable cases.^[60] For radiotherapy applications, radiomics informs dose optimization by identifying radioresistant subregions within tumors. Genomic-guided radiomics models have been tested in phase 2 trials for dose escalation to hypoxic habitats in soft tissue sarcomas, potentially improving local control while minimizing toxicity.^[61] Additionally, radiomics distinguishes true progression from pseudoprogression post-radiotherapy, using multiparametric MRI features to achieve diagnostic accuracies of around 80% in validation cohorts, which is critical for avoiding unnecessary treatment changes in glioblastoma patients.^[62]

Non-Oncological Applications

Radiomics has extended beyond oncology to non-cancerous conditions, leveraging quantitative image features from modalities like MRI and CT to enhance diagnostic and prognostic capabilities in neurology, cardiology, and other fields. In neurological applications, radiomics analyzes infarct texture and other microstructural patterns to predict stroke outcomes, while in cardiology, it assesses plaque vulnerability through texture heterogeneity. Additional uses include evaluating COVID-19 lung involvement and musculoskeletal injuries, with validation through prospective studies demonstrating improved clinical decision-making, such as faster stroke triage. In neurology, radiomics facilitates stroke outcome prediction by extracting features from infarct regions on MRI, capturing texture variations that correlate with functional recovery. For instance, machine learning models incorporating radiomic signatures from diffusion-weighted MRI have achieved high accuracy (AUC > 0.85) in forecasting 90-day modified Rankin Scale scores in acute ischemic stroke patients. Similarly, prospective multicenter studies have shown that radiomics-based triage using non-contrast CT features improves early identification of salvageable tissue compared to standard assessments. For Alzheimer's disease, radiomics enables plaque quantification via MRI, where nano-radiomic analysis of amyloid-beta deposits in enhanced scans detects low-burden plaques with 100% sensitivity and specificity in preclinical models, aiding early diagnosis. These approaches integrate shape, intensity, and texture features to differentiate pathological from normal brain tissue, supporting progression monitoring. Cardiological applications focus on atherosclerosis, where CT texture features quantify plaque vulnerability by identifying high-risk compositions like thin-cap fibroatheromas. Coronary CT angiography-based radiomics models extract over 1,000 features, including gray-level co-occurrence matrices, to predict major adverse cardiac events with AUC values of 0.82-0.90, outperforming traditional plaque metrics. In carotid arteries, similar texture analysis on CT scans stratifies symptomatic plaques, enhancing risk assessment for stroke prevention. These non-invasive tools complement invasive imaging, providing reproducible vulnerability scores across patient cohorts. Beyond neurology and cardiology, radiomics assesses COVID-19 severity through lung CT patterns, particularly in 2020 studies that quantified ground-glass opacities and consolidation volumes. Deep learning-enhanced radiomics from chest CT predicted intensive care needs with 92% accuracy, using features like entropy and uniformity to score disease extent. In musculoskeletal conditions, MRI-based radiomics models evaluate injury severity, such as anterior talofibular ligament tears, achieving 88% diagnostic accuracy via ligament texture and shape features. These applications in non-oncologic injury assessment extend to arthritis and fractures, where radiomics improves lesion characterization over qualitative reads.

Emerging Multiparametric Approaches

Emerging multiparametric approaches in radiomics involve the integration of imaging-derived features with complementary data modalities, such as genomic profiles and clinical variables, to enhance predictive capabilities beyond single-modality analysis. Radiogenomics, a key subset, correlates radiomic features extracted from medical images with genomic data to uncover non-invasive biomarkers of molecular phenotypes. For instance, this fusion enables the prediction of genetic mutations without biopsy, as demonstrated in studies combining quantitative imaging data with gene expression profiles to model tumor behavior. Similarly, incorporation of clinical data—such as patient demographics, laboratory results, and histopathological findings—into radiomic models via nomograms supports personalized decision-making by quantifying risk probabilities in a unified framework. These integrations leverage standardized pipelines to align heterogeneous data types, addressing limitations in isolated radiomics applications.^[63]^[64]^[65]^[66] In oncological contexts, multiparametric radiomics has shown promise in breast cancer management through multi-omics integration. For example, combining radiomic features from multiparametric MRI (including T1-weighted, T2-weighted, and dynamic contrast-enhanced sequences) with PET imaging has enabled accurate prediction of HER2 receptor status, a critical biomarker for targeted therapies. One study developed a radiomic signature from these modalities that achieved an area under the curve (AUC) of 0.80 for distinguishing HER2-low/positive from HER2-zero tumors, outperforming single-modality models by incorporating texture and shape features reflective of tumor heterogeneity. Extending to non-oncological applications, multi-modal radiomics in acute ischemic stroke utilizes CT angiography and perfusion imaging to predict infarct core and penumbra volumes. By fusing radiomic features from non-contrast CT with perfusion maps, models have improved outcome forecasting, with ensemble approaches yielding AUC values up to 0.82 for thrombolysis response assessment, compared to 0.70 for perfusion alone. These examples highlight how cross-modality fusion captures complementary physiological insights, such as vascular dynamics in stroke or molecular signatures in cancer.^[67]^[68]^[69]^[70] Advanced techniques underpin these fusions, including the creation of joint feature spaces where radiomic descriptors are concatenated with genomic or clinical vectors prior to machine learning input. This dimensionality reduction via principal component analysis or autoencoders mitigates multicollinearity while preserving inter-modality relationships. In the 2020s, deep learning ensembles have gained traction, combining convolutional neural networks for end-to-end feature learning with traditional radiomics to handle multi-parametric inputs. For instance, hybrid models that ensemble radiomic signatures from MRI and PET with genomic data via random forests or gradient boosting have demonstrated robustness in heterogeneous tumors, where single-modality radiomics often falters due to variability in imaging protocols. Such ensembles facilitate transfer learning across datasets, enhancing generalizability.^[71]^[72]^[73] The primary benefits of these approaches include substantial gains in diagnostic and prognostic accuracy, particularly for heterogeneous tumors where intra-lesional variability obscures single-modality signals. Multiparametric models have reported AUC improvements of 10-20% over unimodal baselines; for example, fusing radiomics with clinical nomograms in breast cancer yielded an AUC of 0.90 versus 0.75 for imaging alone, enabling better stratification of treatment responses. In stroke, multi-modal integration reduced prediction errors for functional outcomes by 15%, aiding timely interventions. These enhancements stem from the synergistic capture of macroscopic (radiomic) and microscopic (genomic/clinical) tumor traits, ultimately supporting precision medicine by minimizing invasive procedures and improving patient stratification. However, standardization of fusion protocols remains essential to realize widespread clinical adoption.^[74]^[75]^[76]^[77]

Challenges and Future Directions

Current Limitations

One major technical limitation in radiomics is the instability of extracted features, which exhibit significant variability across different imaging scanners and acquisition protocols. For example, reproducibility rates for radiomic features have been reported as low as 14% between scanners from different manufacturers.^[78] This instability stems from differences in scanner hardware, reconstruction algorithms, and imaging parameters such as slice thickness and contrast settings.^[79] Compounding this issue is the lack of standardization in feature extraction pipelines and software; despite tools like PyRadiomics that promote reproducibility through standardized features, custom pipelines and variations in implementation can lead to inconsistent results across studies.^[80] Clinically, radiomics suffers from challenges related to dataset size and validation rigor. Small sample sizes, frequently under 100 cases in both preclinical and clinical investigations, promote overfitting in high-dimensional models that incorporate hundreds of features, resulting in inflated performance metrics that do not hold in real-world applications.^[80] Overfitting is particularly acute in radiomics due to the curse of dimensionality, where the number of features vastly outnumbers available data points, limiting model generalizability.^[3] Moreover, prospective validation remains scarce; as of 2024, few radiomics studies incorporate data from clinical trials, with none prospectively implementing radiomics as a clinical decision support tool, and the majority relying on retrospective cohorts that fail to address temporal and population shifts.^[81] Regulatory obstacles further constrain radiomics adoption, particularly for biomarkers intended as diagnostic or prognostic tools. In the United States, radiomics software qualifies as a medical device under FDA oversight, necessitating 510(k) premarket notification to establish substantial equivalence to existing predicates, a process complicated by the need for robust analytical and clinical validation data.^[82] The opaque nature of black-box machine learning models integrated into radiomics pipelines exacerbates these hurdles, as regulatory bodies and clinicians demand interpretable outputs to ensure safety and efficacy without unexplained decision pathways.^[83] Bias from underrepresentation in diverse populations represents a critical ethical and performance limitation in radiomics. Datasets often derive from homogeneous cohorts, predominantly featuring certain racial, ethnic, or socioeconomic groups, which leads to algorithmic biases where models underperform or misclassify individuals from underrepresented demographics.^[84] This underrepresentation, evident in many imaging archives with limited inclusion of non-Western or minority populations, perpetuates health disparities by reducing model fairness and external validity across global patient bases.^[85]

Prospects and Innovations

Advancements in artificial intelligence are poised to drive automation in radiomics workflows, particularly through end-to-end pipelines that integrate image acquisition, feature extraction, and predictive modeling. Recent developments in cloud-based infrastructure enable reproducible AI pipelines for radiology, facilitating automated radiomics analysis with minimal human intervention and supporting scalable deployment across institutions.^[86] Multi-agent frameworks, leveraging agentic systems, are emerging to automate complex radiomics tasks, such as feature selection and validation.^[87] Applications of AI-enhanced radiomics have been demonstrated in thoracic imaging for pulmonary nodule subtype differentiation as of 2025.^[88] Quantum computing holds promise for addressing the high-dimensional challenges in radiomics, where traditional methods struggle with the computational demands of vast feature sets. Integrated strategies combining radiomics feature extraction with quantum machine learning models have shown improved precision in prognostic predictions, such as for pulmonary ground-glass nodule classification, by efficiently handling entangled data representations in high-dimensional spaces.^[89] Quantum neural networks applied to MRI-radiomic data enable explainable classifications in complex scenarios, mapping features to quantum Hilbert spaces for enhanced separability without excessive classical computation.^[90] Applications of quantum principal component analysis are anticipated to accelerate dimensionality reduction in radiomics datasets, potentially transforming biomarker discovery by 2030.^[91] Prospects for radiomics include its integration into routine clinical practice, with projections indicating widespread adoption in guidelines by 2030 through standardized image-derived biomarkers. Market analyses forecast the radiomics sector to double in value to over USD 30 billion by 2030, driven by regulatory approvals and interoperability standards that support everyday use in oncology workflows.^[92]^[93] Global consortia are fostering mega-databases to enable large-scale validation; for instance, the RadioVal consortium promotes harmonized radiomics data sharing across Europe, while the Radiogenomics Consortium (RGC) facilitates collaborative analyses of imaging-genetic datasets for toxicity prediction.^[94] Emerging areas encompass real-time radiomics applications during surgery, enhancing intraoperative decision-making. In colorectal cancer procedures, habitat-based radiomics from intraoperative CT enables evaluation of radiofrequency ablation response for lung metastases.^[95] Integration with wearable devices is another frontier, allowing continuous monitoring of patient responses post-imaging. Multimodal frameworks combining radiomics with other data sources could personalize follow-up care in chronic conditions.^[96] Research directions emphasize longitudinal studies to capture dynamic changes in radiomic features over time, improving prognostic models for treatment response. Multimodal longitudinal radiomics has demonstrated superior prediction of durable benefits in immunotherapy, with AUC improvements up to 0.85 when incorporating serial imaging data.^[97] Ethical AI frameworks are critical to guide radiomics implementation, addressing biases in feature extraction and ensuring equitable access; guidelines stress transparency in model governance and patient autonomy in data use to mitigate risks in clinical translation.^[98]^[99]

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