Topics in AI for Radiation Oncology

This is a heavily borrowed (from ChatGPT and other online sources) structure for a semester long course on AI in Radiation Oncology, aiming to provide participants with a robust understanding of AI applications in radiation oncology imaging, blending theoretical knowledge with practical insights to prepare them for the evolving landscape of medical imaging and therapy.​

Week 1: Introduction to AI in Radiation Oncology

• Overview of AI: Definition, history, and evolution of AI in healthcare.​
• Relevance to Radiation Oncology: The role and potential of AI in enhancing radiation therapy workflows.​
• Current Landscape: Review of existing AI applications in medical imaging and radiation therapy.​

Further Reading:

• “Artificial intelligence in radiation oncology”: This article provides a comprehensive overview of AI methods and their implications in the radiation therapy process. ​
• “Revolutionizing radiation therapy: the role of AI in clinical practice”: This article discusses how AI has optimized tumor and organ segmentation, saving time for radiation oncologists. ​
• “Artificial intelligence in radiation oncology: A review of its current applications and future outlook”: This review explores the potential of AI in toxicity prediction, automated treatment planning, and clinical trial patient selection.
• “Artificial intelligence and machine learning in cancer imaging”: This article discusses the challenges and opportunities of AI and ML in cancer imaging, considerations for the development of algorithms into tools that can be widely used and disseminated, and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

Week 2: Fundamentals of Machine Learning and Deep Learning

• Machine Learning Basics: Supervised vs. unsupervised learning, key algorithms, and model evaluation metrics.​
• Deep Learning Introduction: Neural networks, convolutional neural networks (CNNs), and their relevance to image analysis.​
• Tools and Frameworks: Introduction to popular AI development tools and platforms.​

Further Reading:

• Hello World: Deep Learning in Medical Imaging is a dated but useful starting point for medical image classification using a standard off-the-shelf deep neural network architecture.

Week 3: Medical Imaging in Radiation Oncology

• Imaging Modalities: CT, MRI, PET, and their roles in radiation therapy planning.​
• Image Acquisition and Reconstruction: Understanding the technical aspects and challenges.​
• Image Preprocessing Techniques: Noise reduction, normalization, and enhancement methods.​

Further Reading:

• “How tomographic reconstruction works?”: This video inspired by 3Blue1Brown shows how CT images are reconstructed in 3D using a series of single plane projections.
• “Radiology Modalities Explained: Understanding Medical Imaging Techniques”: This article includes an overview of various radiology modalities, including X-rays, CT scans, MRI, ultrasound, and nuclear medicine. It explains how each modality works, their diagnostic applications, and considerations regarding radiation exposure.

A summary of which is presented here: | Modality | Typical Uses | Duration | Pros | Cons | |————–|——————————————————————————-|———————-|———————————————————————-|————————————————————————| | X-ray | Fracture diagnosis; Lung infection detection; Dental evaluation | 10–15 minutes | Quick and accessible; Relatively low cost; Effective for detecting fractures and lung conditions | Limited soft tissue detail; Exposure to ionizing radiation | | Fluoroscopy | Barium enema procedures; Cardiac catheterization; Joint injections | 30 minutes – 2 hours | Real-time imaging; Guidance during procedures | Exposure to ionizing radiation; Potential for contrast dye reactions | | CT Scan | Tumor detection and staging; Vascular disease evaluation; Internal injury assessment | 20–25 minutes | High-resolution images; Fast scanning times; Excellent for bone and vascular evaluation | Higher radiation dose than X-rays; Contrast dye may be required | | MRI | Brain and spinal cord imaging; Soft tissue evaluation; Multiple sclerosis diagnosis | 45 minutes – 1 hour | Detailed soft tissue images; No ionizing radiation; Multiplanar imaging capabilities | Longer scanning times; Claustrophobic for some patients; Limited availability for certain conditions | | Ultrasound | Prenatal imaging and monitoring; Abdominal and pelvic evaluation; Cardiac and vascular imaging | 30 minutes – 1 hour | Real-time imaging; No ionizing radiation; Safe for pregnant women | Operator-dependent; Limited penetration for deep structures | | PET Scan | Cancer diagnosis and staging; Brain function evaluation; Heart disease assessment | 1.5 – 2 hours | Functional and metabolic information; Detection of small lesions; Accurate staging of cancers | High cost; Limited availability; Requires radiotracer administration | | Mammography | Breast cancer screening; Detection of breast abnormalities | 30 minutes | Early detection of breast cancer; High-resolution images | Slight discomfort during the procedure |

1. X-Ray How it works: Uses ionizing radiation to produce 2D images of the body. Use cases: Bone fractures, lung infections (like pneumonia), chest imaging, dental exams. Speed: Very fast (few minutes). Cost: Low. Radiation: Low dose. Limitations: Poor soft tissue contrast; overlapping structures can obscure details.

2. CT (Computed Tomography) How it works: Combines X-ray images taken from different angles into cross-sectional views. Use cases: Trauma, cancer, stroke, internal bleeding. Speed: Fast (minutes). Cost: Moderate to high. Radiation: Higher than X-ray (can be 100–1,000 times more). Strengths: Great for bone, chest, and detecting bleeding. Limitations: High radiation; contrast dye may affect kidneys.

3. MRI (Magnetic Resonance Imaging) How it works: Uses magnets and radio waves to produce detailed images of soft tissues. Use cases: Brain, spinal cord, muscles, joints, tumors. Speed: Slower (30–90 minutes). Cost: High. Radiation: None. Strengths: Best soft tissue contrast; no radiation. Limitations: Claustrophobia, loud, not suitable with metal implants.

4. Ultrasound How it works: High-frequency sound waves create real-time images. Use cases: Pregnancy, abdomen, heart (echocardiogram), blood flow. Speed: Real-time (immediate). Cost: Low to moderate. Radiation: None. Strengths: Safe in pregnancy, portable, real-time imaging. Limitations: Operator-dependent; limited by gas or obesity.

5. Nuclear Medicine (e.g., PET, SPECT) How it works: Injects small amounts of radioactive material to assess function (not just structure). Use cases: Cancer detection, heart disease, brain disorders. Speed: Variable (can take hours). Cost: High. Radiation: Moderate to high (depends on tracer). Strengths: Functional imaging; early disease detection. Limitations: Radiation exposure; long prep and scan times.

Week 4: AI for Image Segmentation

• Segmentation Techniques: Traditional methods vs. AI-driven approaches.​
• Deep Learning for Segmentation: Application of CNNs and U-Net architectures.​
• Case Studies: Automated tumor and organ-at-risk delineation.​

Week 5: AI in Image Registration and Fusion

• Concepts of Image Registration: Aligning images from different modalities or time points.​
• AI-enhanced Registration Methods: Learning-based approaches for improved accuracy.​
• Clinical Applications: Integrating multimodal imaging data for comprehensive analysis.​

Week 6: Radiomics and Feature Extraction

• Introduction to Radiomics: Quantitative analysis of imaging features.​
• Feature Extraction and Selection: Techniques for identifying relevant imaging biomarkers.​
• Predictive Modeling: Using radiomic features for outcome prediction and personalized therapy.​

Week 7: AI in Treatment Planning

• Automated Treatment Planning: Leveraging AI for plan optimization and dose calculation.​
• Knowledge-based Planning: Utilizing historical data to inform new treatment plans.​
• Case Studies: Examples of AI-driven treatment planning systems.​

Week 8: AI for Quality Assurance and Safety

• Quality Assurance Processes: Ensuring accuracy and safety in radiation therapy.​
• AI Applications in QA: Automated error detection and workflow optimization.​
• Regulatory Considerations: Compliance with standards and guidelines for AI tools.​

Week 9: AI in Adaptive Radiation Therapy

• Concept of Adaptive Therapy: Modifying treatment plans based on patient-specific changes.​
• Real-time Monitoring: Using AI to assess and respond to anatomical and physiological variations.​
• Implementation Challenges: Technical and logistical considerations for clinical adoption.​

Week 10: Ethical and Legal Considerations in AI

• Ethical Principles: Patient privacy, consent, and data security.​
• Bias and Fairness: Addressing potential biases in AI models and ensuring equitable care.
• ​Legal Frameworks: Intellectual property, liability, and regulatory approvals for AI applications.​

Week 11: Validation and Evaluation of AI Models

• Performance Metrics: Assessing accuracy, sensitivity, specificity, and robustness.​
• Clinical Validation: Translating AI models from research to practice.​
• Continuous Monitoring: Post-deployment evaluation and model updating.​

Week 12: Integration of AI into Clinical Workflow

• Workflow Analysis: Identifying points of integration for AI tools.​
• User Training and Acceptance: Strategies for effective implementation and adoption by clinical staff.​
• Change Management: Overcoming barriers to integrating AI into existing systems.​

Week 13: Future Directions and Emerging Trends

• Innovations on the Horizon: Explainable AI, federated learning, and real-time AI applications.​
• Interdisciplinary Collaboration: The role of data scientists, clinicians, and engineers in advancing AI.​
• Preparing for the Future: Skills and knowledge areas for professionals in AI-driven radiation oncology.​

Week 14: Course Review

• Peer Feedback and Discussion: Collaborative evaluation of projects and shared learning.​
• Course Recap: Review of key concepts, discussions on lessons learned, and exploration of potential career paths in AI for radiation oncology.​

Further reading

Michener course on AI in Radiation Therapy

Stanmore course on AI in Radiation Oncology

ESTRO Advanced Imaging in RT course

Symposium on Practical AI in Radiation Oncology

National Cancer Institute workshop on AI in Radiation Oncology