Recent Developments in the Prediction of Clinical Outcomes Data in Radiation Oncology - 2021 Annual Meeting
The prediction of treatment outcomes for individual patients or patient populations, including tumor control and normal tissue toxicity, has always been of tremendous importance for driving clinical practice in Radiation Oncology and at the same time has been an active area within the research community for developing new algorithms, models and analysis methods. Predicting patient outcomes and developing new evidence-based therapeutic regimes are by no means a trivial task as the complexity of the problem goes beyond simple empirical observations and requires a multidisciplinary approach that relies heavily on mathematical modelling, statistical analysis and, more recently, artificial intelligence. In this session, speakers will address the diversity of challenges encountered when attempting to predict the outcome of radiotherapy.
The first presentation focus is on the radiobiological modelling of the tumor control probability (TCP) for early-stage non-small cell lung cancer. The second presentation explores the validation of a model for normal tissue complication probability (NTCP) for acute esophageal toxicity. The third presentation demonstrates how a deep learning approach can be used to predict local regional control, the occurrence of distant metastases, and overall survival in head and neck cancer patients undergoing radiotherapy. Each of these presentations introduce novel methods and unique results with high potential for impacting methods for the analysis of clinical outcome data and ultimately improving treatment outcome predictions.
This activity is available from January 24, 2022, through 11:59 p.m. Eastern time on January 23, 2025.
This activity was originally recorded at ASTRO’s 2021 Annual Meeting, October 24-27th.
The activity is designed to meet the interests of radiation oncologists, radiation oncology residents, radiation physicists, radiation therapists and radiation biologists.
Upon completion of this activity, participants should be able to:
- Explain the role of radiobiological modelling and deep learning approaches for treatment outcome predictions in radiation oncology.
- Identify the advantages and the potential limitations of the predictive approaches based on radiobiological modelling and radiomics analysis.
- Discuss the potential implementation of the predictive models in the clinical practice with respect to treatment optimization and individualization.
- David J. Carlson, PhD, Department of Radiation Oncology, University of Pennsylvania has no financial relationships with a commercial interest.
- Iuliana Toma-Dasu, PhD, Department of Oncology-Pathoolgy, Karolinska Institutet and Department of Physics, Medical Radiation Physics, Stockholm University has no financial relationships with a commercial interest.
- Joseph O. Deasy, PhD, Department of Medical Physics, Memorial Sloan Kettering Cancer Center has investment interest from Varian Corp and Paige.Al.
- Esther Troost, PhD, OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf receives compensation from Sachsische Aufbaubank.
- Jan Seuntjens, McGill University Health Centre receives compensation from Gray Oncology Solutions.
The person(s) above served as the developer(s) of this activity. Additionally, the Education Committee had control over the content of this activity. All relevant relationships have been mitigated.
The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council of Continuing Medical Education to provide continuing education to physicians.
ASTRO is awarded Deemed Status by the American Board of Radiology to provide SA-CME as part of Part II Maintenance of Certification.
- 1.25 Certificate of AttendanceThis activity was designated for 1.25 AMA PRA Category 1 Credit™.
- 1.25 SA-CME
The American Society for Radiation Oncology (ASTRO) is accredited by the Accreditation Council of Continuing Medical Education to provide continuing medical education for physicians.
The American Society for Radiation Oncology (ASTRO) designates this for a maximum of 1.25 AMA PRA Category 1 Credit™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
This activity meets the American Board of Radiology's criteria for a self-assessment activity in the ABR's Maintenance of Certification program. Participation in this course in combination with the successful completion of the corresponding assessment and course evaluation adheres to the guidelines established by the ABR for 1.25 self-assessment credits.
ASTRO members must log in to the ASTRO website to view and receive the discounted member rate.
No refunds, extensions, or substitutions will be made for those participants who, for any reason, have not completed the course by the end of the qualification date. The qualification date for each course is listed in the course catalog on the ASTRO website under availability.
Participants using ASTRO's online courses to satisfy the requirement of a Maintenance of Certification (MOC) program should verify the number, type and availability dates of any course before making a purchase. No refunds, extensions, or substitutions will be made for participants who have purchased courses that do not align with their MOC requirement.
The course and its materials will only be available on the ASTRO website for that 3 year period regardless of purchase date. At the expiration of the qualification, participants will no longer have access to the course or its materials. ASTRO reserves the right to remove a course before the end of its qualification period.
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