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Session 20: Radiomics and Precision Medicine
Sub-Tracks: Radiomics, Precision Medicine, Artificial Intelligence in Imaging, Machine Learning, Deep Learning Models, Quantitative Imaging, Image Biomarkers, Computational Radiology, Imaging Genomics (Radiogenomics), Tumor Characterization, Oncology Radiomics, Predictive Modeling, Imaging Biomarker Validation, Personalized Cancer Therapy, Big Data in Radiology, Multi-Omics Integration, Prognostic Imaging, Treatment Response Assessment, Clinical Decision Support, Future of AI in Radiology.
Overview
Radiomics and precision medicine represent the future of radiology by harnessing advanced imaging analytics and data-driven approaches to improve diagnostics and tailor treatments to individual patients. Radiomics extracts high-dimensional quantitative features from medical images, which, when combined with clinical, pathological, and genomic data, enable predictive and personalized healthcare strategies.
Key Areas of Radiomics & Precision Medicine:
Radiomics in Oncology
Extracting imaging biomarkers for tumor detection, characterization, and grading.
Predicting treatment response and patient outcomes.
Enabling non-invasive tumor heterogeneity assessment.
Integration with Artificial Intelligence
Machine learning and deep learning models enhance feature extraction and predictive modeling.
AI-driven radiomics supports real-time clinical decision-making.
Radiogenomics (Imaging Genomics)
Linking imaging phenotypes with genomic and molecular profiles.
Facilitating precision oncology by correlating imaging features with tumor biology.
Clinical Applications
Oncology: Tumor detection, staging, therapy response prediction.
Neurology: Radiomics in Alzheimer’s disease, multiple sclerosis, and brain tumors.
Cardiology: Predicting risk of cardiovascular events using quantitative imaging.
Diagnostic Techniques:
Automated image analysis and feature extraction.
Integration of radiomics data with molecular and clinical datasets.
Machine learning models for prognostic and predictive analytics.
Clinical Relevance:
Radiomics provides a non-invasive method to quantify disease characteristics, while precision medicine ensures treatment strategies are tailored to individual patient profiles. Together, they transform patient care by moving from generalized treatment approaches to personalized, evidence-based strategies.
Advantages:
Enhances predictive accuracy and diagnostic precision.
Non-invasive insights into disease biology.
Facilitates personalized treatment and monitoring.
Expands research opportunities in multi-omics integration.
Limitations:
Requires large, high-quality datasets for validation.
Standardization of imaging protocols is essential.
Computational complexity and need for specialized expertise.
Summary
Radiomics and precision medicine are redefining the role of radiology in the era of data-driven healthcare. By integrating imaging biomarkers, AI, and genomics, this session will highlight how radiology is leading the way toward predictive, preventive, and personalized medicine.