Introduction

Radiomics and imaging biomarkers are critical concepts within medical imaging, serving distinct yet complementary roles. This article explores their differences, relationships, and clinical implications, accompanied by case studies demonstrating practical applications.

What is Radiomics?

Radiomics refers to the extraction of numerous quantitative features from medical images (including MRI, CT, PET/CT and PET/MRI), typically utilizing predefined, handcrafted methods. These features include shape, texture, and intensity-based characteristics systematically derived from regions or volumes of interest within images. The primary goal of radiomics is to quantitatively characterize tissue patterns—particularly their heterogeneity and spatial distribution—to support clinical decision-making, particularly in precision medicine.

What are Imaging Biomarkers?

Imaging biomarkers are specific, quantitative imaging characteristics with established clinical relevance. They are rigorously validated to reliably indicate biological processes, disease states, treatment responses, or prognosis. Unlike radiomics, imaging biomarkers offer explicit clinical validation and biological interpretability, serving targeted diagnostic, predictive, or prognostic roles.

Distinguishing Radiomics from Imaging Biomarkers

Radiomics generates numerous imaging-derived features, but not all qualify as imaging biomarkers. Radiomic features become imaging biomarkers only after rigorous validation confirms their consistent correlation with clinical outcomes or biological phenomena. Hence, radiomics broadly encompasses data mining, whereas imaging biomarkers constitute clinically validated subsets.

AI vs. Radiomics
Artificial Intelligence (AI) in Medical Imaging

AI, encompassing machine learning (ML) and deep learning (DL), involves computational algorithms that autonomously recognize patterns within imaging data. Unlike handcrafted methods, AI automatically extracts relevant features from raw data, often outperforming traditional methods in tasks such as image classification, lesion detection, and outcome prediction.

Is Radiomics Equivalent to AI?

A common misconception equates radiomics with AI. Traditionally, radiomics uses manually defined (handcrafted) features, not necessarily involving AI. However, AI methods, particularly ML algorithms, can augment radiomics by refining feature selection and predictive modeling. Radiomics is inherently structured and analytical, while AI refers to automated, data-driven computational methods.

Clarifying AI-driven vs. Handcrafted Radiomics

• Handcrafted Radiomics: Manual extraction of predefined features; transparent and interpretable; suitable for moderate-sized datasets.
• AI-driven Feature Extraction: Automated extraction of complex patterns; typically requires larger datasets; less interpretable without explainable AI methods.

Clinical Implications and Considerations

Selecting between radiomics, imaging biomarkers, and AI-driven methods depends significantly on clinical context, research objectives, data availability, and interpretability needs. Radiomics suits small datasets needing high interpretability, whereas AI-driven methods excel in data-rich environments prioritizing predictive accuracy. Ethical considerations, patient privacy, and regulatory compliance also guide method selection.

Case Studies

Case Study 1: Tumor Size and Analytical Validation in Cancer Management

Tumor size assessment via MRI, CT, and PET imaging is fundamental in oncology as a crucial imaging biomarker. Accurate and reproducible measurements enable clinicians to evaluate disease progression and therapeutic efficacy. Rigorous analytical validation through standardized phantom studies and protocols ensures reliability. Institutions like the University of Iowa report systematic uncertainties (up to 4%) in PET imaging, emphasizing rigorous quality assurance.

Case Study 2: PET Imaging in Tumor Response Assessment

Targeted therapies necessitate predictive biomarkers, such as tumor perfusion and metabolic activity assessed via PET imaging. Fluorodeoxyglucose (FDG)-PET significantly impacts therapeutic decisions by quantifying metabolic activity. Research from the University of Iowa illustrates PET’s critical role in adapting radiation treatment plans, notably in Hodgkin lymphoma.

Case Study 3: MRI and Magnetic Resonance Spectroscopic Imaging (MRSI)

Advanced MRI techniques like MRSI offer insights beyond anatomical imaging, evaluating metabolites such as choline and N-acetyl aspartate (NAA). Elevated choline-to-NAA ratios indicate aggressive tumor regions, and MRSI-defined metabolic volumes frequently surpass MRI-defined boundaries, significantly influencing radiation therapy planning and targeted treatment.

Case Study 4: Functional MRI Techniques

Functional MRI biomarkers such as Dynamic Contrast-Enhanced MRI (DCE-MRI) and Diffusion Weighted Imaging (DWI) assess tumor physiology and predict therapeutic outcomes. DCE-MRI evaluates vascularity and permeability, distinguishing aggressive breast lesions. DWI measures cellular density, effectively differentiating malignant from benign lesions in prostate, parotid gland, rectal, and cervical cancers, providing early predictive markers to optimize therapy.

Case Study 5: Post-treatment PET Imaging in Head and Neck Cancer

Post-treatment FDG-PET scans provide prognostic information predicting patient survival. Negative PET findings post-radiotherapy reliably indicates better outcomes, potentially reducing unnecessary interventions. However, positive PET findings require cautious interpretation due to false positives, suggesting integration with other biomarkers or imaging methods to enhance specificity.

Infrastructure Needs

Effective integration of imaging biomarkers into clinical practice requires robust infrastructure:

• Quality Assurance (QA): Ensuring consistent biomarker measurements through rigorous programs.
• Education and Training: Enhancing clinician expertise in advanced imaging techniques.
• Interoperability and Data Standardization: Developing standardized platforms to promote collaboration.
• Bioinformatics Integration: Connecting imaging data with clinical outcomes for comprehensive validation and prognostic modeling.

Radiomics, imaging biomarkers, and AI-driven approaches provide distinct yet complementary tools for clinical decision-making in medical imaging. Ongoing analytical validation, attention to ethical considerations, and infrastructure improvements remain essential. Future research trends indicate growing integration of AI and traditional radiomics, enhancing diagnostic accuracy and patient care outcomes.