Radiomics & AI in Oncologic Imaging

Training Overview

Radiomics & AI in Oncologic Imaging is an advanced, simulation-enhanced training program designed for radiologists, oncologists, medical physicists, imaging scientists, residents, and clinical researchers seeking deeper expertise in radiomics, quantitative imaging, and artificial intelligence applications in cancer evaluation. This two-day course integrates structured theoretical modules with hands-on radiomic feature extraction, AI-driven image analysis simulations, and multidisciplinary case discussions, emphasizing data-driven decision-making, imaging standardization, and precision oncology workflow integration.

The program begins with foundational modules on radiomic feature categories, data acquisition standards, segmentation principles, imaging biomarkers, AI model development, and validation frameworks. Participants explore key concepts including feature reproducibility, harmonization methods, machine learning architectures, outcome prediction modeling, and integration of radiomics with clinical, genomic, and pathology datasets.

Hands-on simulations enable participants to work with real oncologic imaging datasets, performing segmentation, feature extraction, and AI-based classification under expert guidance. Attendees practice model interpretation, quality checks, radiomic profiling, and integration of quantitative metrics into clinical reports and oncology decision pathways.

Through interactive video cases, scenario-based discussions, and tumor board–style sessions, participants analyze real-world oncologic imaging cases using radiomics and AI tools to improve lesion characterization, treatment response assessment, risk stratification, and follow-up planning. Structured feedback reinforces analytical precision, communication clarity, and cross-disciplinary collaboration.

By the end of the program, participants will have developed advanced competency in applying radiomics and AI methodologies to oncologic imaging, enhancing diagnostic value, predictive accuracy, and personalized treatment planning across diverse cancer care settings.

Target Audience

• Radiologists

• Oncologists

• Nuclear Medicine Physicians

• Medical Physicists

• Imaging Scientists & AI Researchers

• Residents and Fellows in Radiology, Oncology, and Biomedical Engineering

• Data Scientists working in medical imaging

• Radiographers involved in imaging data acquisition

• Clinical Researchers in precision medicine

• Academic Educators and Imaging Program Leads

Training Specifics

Training Name: Radiomics & AI in Oncologic Imaging

Duration: 2 full days

Language: English

Simulation Lab: Radiomics workstations, AI imaging analysis platforms, segmentation tools, quantitative imaging datasets

Assessment Type: Practical Evaluation & Quiz

Certificate: Certificate of Completion & CME Credits

Venue: ADN CoE Training, Research Center & Core Lab

Additional Benefits

• Hands-On Radiomics Simulation: Practice segmentation, feature extraction, harmonization, and quantitative imaging workflows.

• AI Model Interpretation: Learn model validation, classification performance assessment, and prediction usage in clinical decisions.

• Precision Oncology Integration: Understand how imaging biomarkers complement pathology, genomics, and clinical risk models.

• Workflow Enhancement: Build competencies in integrating AI tools into daily radiology and oncology practice.

• Expert Guidance: Receive mentorship and real-time feedback from leaders in radiomics and AI imaging research.

• Case-Based Learning: Analyze complex oncologic images enhanced with quantitative metrics for improved decision-making.

• Quality & Reproducibility Skills: Strengthen understanding of data standardization, feature stability, and model reliability.

• Professional Advancement: Earn certification validating advanced capability in AI-assisted oncologic imaging.

Key Benefits

• Enhanced understanding of radiomic features, AI architectures, and quantitative oncology imaging.

• Improved lesion characterization and treatment response evaluation using data-driven techniques.

• Stronger skills in segmentation, model interpretation, and radiomic analysis workflows.

• Greater alignment between radiology, oncology, physics, and data science teams.

• Increased readiness to integrate AI and radiomics tools into clinical and research settings.

Ethical and Educational Standards

All ADN CoE training programs are designed in compliance with ethical, animal-free, and environmentally responsible education policies, ensuring clinical excellence and sustainability. For inquiries regarding accreditation or ethical compliance, contact trainings@adncoe.com.

Logistics and Additional Information

Transfer Services

Participants using ADN CoE accommodation and transfer options should adhere to the scheduled pick-up and drop-off times, which were shared before the training.

International Participants

Ensure your passport and visa are valid before travel. For invitation letters or official documentation, contact trainings@adncoe.com at least 4 weeks before the course start date. Information on nearby hotels, transport, and COVID-19 regulations will be provided before the event.

Attire

Business casual attire is recommended. Surgical scrubs and personal protective equipment will be provided for cadaver lab sessions. Please note: ADN CoE is a non-smoking, green-certified facility.

Health & Safety

Our venues comply with the highest safety and hygiene standards. Participants will receive orientation on fire, emergency, and biosafety protocols.

Transfer of Registration

Participants may transfer their registration to another individual from the same institution with prior written approval from ADN CoE, provided the request is made at least 10 days before the course start date.

Course Postponement or Cancellation

ADN CoE reserves the right to postpone or cancel the course due to unforeseen circumstances.

In case of cancellation by ADN CoE, participants will receive a full refund or the option to transfer to a future course date.

Non-Transferable Fees

Payment made for the course is non-transferable to other courses or services unless otherwise approved by ADN CoE.

Faculty Disclosure and Conflict of Interest 

All faculty members and proctors involved in this training disclose any financial or commercial interests. We maintain a strict policy of no conflict of interest with device companies, sponsors, or participants, ensuring unbiased and independent education.

Content Independence 

The educational content is developed based solely on the latest scientific evidence and clinical best practices. No product promotion or commercial advertising is included in our training materials.

Evaluation and Feedback 

We value your input. At the end of the course, you will be invited to complete a feedback survey and a knowledge assessment quiz. Your responses help us continually improve the training experience.

Quality Assurance 

Our training materials and curriculum are regularly reviewed and updated to reflect the most current research and technological advances in the field.

Sponsorship Transparency 

Any sponsorship or funding received for this course is disclosed upfront. We ensure that sponsors do not influence the content or delivery of the training.

Data Protection and Patient Confidentiality 

We strictly protect the privacy of all participants and uphold the highest standards of confidentiality regarding patient information shared during the training, in accordance with applicable data protection laws.

Accessibility and Inclusiveness 

We strive to make our training accessible to all participants. Language support and accommodations for disabilities are available upon request to ensure an inclusive learning environment.

If you have any questions about these policies or require assistance, please contact us at trainings@adncoe.com.

Please note that our trainings are specialized procedural trainings provided by ADN CoE. They are independent of any other training offered by relevant device companies.