Academy

Radiomics

Radiomics is a computational approach that extracts large numbers of quantitative features from standard medical images, such as CT or MRI scans. By translating visual information into measurable data—covering aspects like shape, texture, intensity distribution, and wavelet-derived statistics—radiomics can unveil subtle tissue characteristics not detectable by the human eye.

In the context of longevity science, radiomic analysis offers a noninvasive way to track microstructural changes in aging tissues. As we age, organs and cells may exhibit heterogeneous patterns—such as variations in bone microarchitecture, brain white-matter texture, or liver tissue density—that reflect the biological aging process and disease risk.

• Image acquisition and standardization: Ensuring consistent imaging protocols and resolution.
• Region of interest (ROI) delineation: Manual or automated segmentation of the anatomical area under study.
• Feature extraction: Computing first-order (histogram), shape, and higher-order (texture) features, often after image filtering (e.g., wavelet, LoG).
• Feature selection: Statistical tests, correlation analysis, and machine learning algorithms reduce feature redundancy and identify the most predictive biomarkers.
• Model building and validation: Using machine learning classifiers (e.g., random forest) or regression models to predict clinical outcomes, such as biological age, disease progression, or response to interventions.

For longevity enthusiasts, radiomics can be applied to:

1. Brain Aging: Detect early microstructural changes in white and gray matter that correlate with cognitive decline.
2. Bone Health: Quantify trabecular bone texture to assess osteoporosis risk and fracture susceptibility.
3. Liver Fibrosis: Characterize tissue stiffness and heterogeneity as indicators of metabolic aging.

By integrating radiomics with longitudinal imaging data, researchers can develop personalized aging biomarkers, monitor the efficacy of anti-aging therapies, and ultimately inform interventions that promote healthy lifespan.

Machine Learning in Medical Imaging

Machine learning (ML) involves training algorithms on labeled data to make predictions or classifications. In medical imaging, ML can learn complex patterns from radiomic features or raw pixel data, enabling tasks like disease detection, segmentation, and outcome prediction.

Key ML concepts for longevity research:

• Supervised learning: Algorithms learn from labeled examples, such as scans annotated with presence or absence of age-related pathology.
• Unsupervised learning: Techniques like clustering reveal natural groupings in radiomic data, potentially identifying subtypes of aging phenotypes.
• Feature importance: Methods such as random forest provide insight into which imaging biomarkers most influence predictions.
• Cross-validation: Splitting data into training and test sets ensures that models generalize to new populations.

Combining radiomics and ML allows longevity scientists to:

1. Develop predictive models for biological age and frailty.
2. Identify imaging biomarkers that correlate with healthy versus pathological aging.
3. Support noninvasive monitoring in clinical trials of anti-aging interventions.

Through these advances, radiomics and ML are empowering a data-driven understanding of human aging and guiding strategies to extend healthspan.