Course Director

• Søren M. Bentzen, Biostatistician & Radiation Biologist, University of Maryland School of Medicine, Maryland (USA)

Teachers

• Ane Appelt, Medical Physicist, University of Copenhagen-Rigshospitalet, Copenhagen (DK)
• Clifton D. Fuller, Radiation Oncologist, MD Anderson Cancer Center, Houston, TX (USA)
• Johannes Langendijk, Radiation Oncologist, University Medical Centre Groningen, Groningen (NL)
• Ivan R. Vogelius, Medical Physicist, University of Copenhagen-Rigshospitalet, Copenhagen (DK)

   

This course introduces the attendees to a range of quantitative methods frequently used in radiation oncology research and, in some cases, clinical decision-support tools. Radiation oncology probably has the most solid quantitative foundation among medical specialties. As in other specialties, results of randomized controlled trials form the basis for evidence-based clinical practice guidelines; but in addition, prognostic and predictive models provide clinical decision support for individualized management of cases.

Radiation bioeffect models of Normal Tissue Complication Probability (NTCP) and Tumor Control Probability (TCP) have become much more refined since the publication of the QUANTEC overviews 15 years ago and are increasingly being used in treatment plan comparisons or selecting cases likely to benefit from proton therapy. New generations of such models are emerging with artificial intelligence and machine learning (AI/ML) entering the scene for data aggregation, analysis and modeling.

While integration of quantitative estimates of various treatment outcomes is likely to improve patient care, it is also important to understand the limitations of these model estimates. Uncritical reliance on model results may compromise patient safety or treatment outcome or may take research down the wrong track. Thus, the course will also focus on critical appraisal of the validity or quality of a statistical data analysis, a mathematical model, or a clinical trial.

Learning outcomes

By the end of this course participants should be able to:

• Broadly describe the most common quantitative methods in radiation oncology and radiation biology and the assumptions behind these;
• Identify appropriate quantitative methods of analysis for a given data set;
• Recognize the potential of artificial intelligence, deep learning and machine learning in radiation oncology data science;
• Critically evaluate modelling results especially with respect to proper validation and estimates of uncertainties. 

Course Content

• Models and modelling, hypothesis testing and parameter estimation, type I and II uncertainties
• Clinical trials and evidence-based medicine, Phase 0, I, II, III, and IV trial designs, meta-analysis, clinical endpoints, survival statistics and the Cox Proportional Hazards Model
• Statistical modelling and exploratory data analysis, external and internal validity of models, bootstrap and Monte Carlo methods, goodness of fit
• Dose-response models, normal tissue complication probability (NTCP) and tumor control probability (TCP) models, modelling combined modality therapy, patient-level risk factors, the linear-quadratic model and beyond, use of models in treatment planning
• Artificial Intelligence and Machine Learning applications. Deep learning and Convolutional Neural Networks in image analysis.
• Big data analytics and Data Science, wide and tall data sets, dimensionality reduction, data mining, over-fitting, training and validation sets, sample splitting.
• Predictive assays, ROC curves and AUC, sensitivity, specificity, positive and negative predictive value

Prerequisites

No specific requirements are needed for attending this course although a broad familiarity with the principles of cancer medicine and radiation oncology is expected.

All lectures and discussions are in English.

Teaching Methods

The four-day course consists of 27 didactic 45-minute lectures, 4 half-hour interactive discussion sessions, a practical exercise (1.25 h), an interactive data analysis session (1.25 h) and a Meet-the-professor session where you can bring-your-own data analysis project and discuss one-on-one with faculty members (10-minute time slots, 1.25 h total time).

Methods of assessment

• Course evaluation form
• Self-assessment tools are integrated in some of the discussion sessions.

Preliminary draft programme for 2026 will follow soon.

As a reference, you can find the 2024 programme here.

Key words

Data analysis, quantitative methods, bioeffect models, critical appraisal, evidence-based medicine, predictive oncology, clinical trials methodology, outcomes research, machine learning.

Accreditation

Application for CME recognition will be submitted to the European Accreditation Council for Continuing Medical Education (EACCME), an institution of the European Union of Medical Specialists (UEMS). EACCME credits are recognized by the American Medical Association towards the Physician’s Recognition Award (PRA). Information on the status of the applications can be obtained from the ESTRO office.

TBC

Membership

ESTRO members can order products at substantially reduced prices. To benefit from the member registration rate, you must subscribe for the ESTRO membership 2026 BEFORE registering to the course. To become an ESTRO member, benefit from the member registration rate and discover the many other member advantages, please visit the membership page.

Fees

* Members with specialty RadiationTherapist (RTT) may register at the In-Training fee

The fee includes the course material, coffees, lunches, and the social event.

Advance registration & payment are required.

Access to homework and/or course material will become available upon receipt of full payment.

Insurance and cancellation

Participants are strongly advised to take out their own personal insurance policy. ESTRO does not accept liability for individual medical, travel or personal accidents or incidents. Participants are strongly advised to take insurance policies to cover flight and accommodation cancellation penalties. ESTRO will not refund any travel or accommodation expenses.

For any cancellation made by the course participant, ESTRO School Events Cancellation Policy will be followed and all stated penalty fees will be applied.

Reduced fees

Members from emerging countries may register at a preferential rate of 350 Euro. Emerging country fee applies to individuals from low-income and lower-middle-income economies according to the World Bank listing here.

Additionally, all specialties from the following countries can benefit from this preferential rate: Albania, Belarus, Bosnia and Herzegovina, Bulgaria, Hungary, Macedonia, Moldova, Montenegro, Romania, Russian Federation, Serbia, Turkey, Ukraine.  In addition, medical physicists from Cyprus can email education@estro.org to apply for this fee.

The preferential rate of 350 Euro is granted automatically when you click on the  BOOK NOW  button and the three conditions below are met:

1. Only ESTRO members for 2026 are eligible (please make sure your 2026 membership is in order before you click on the BOOK NOW  button)
2. Only one course per person per year can be subsidized by ESTRO
3. Sponsored candidates are not entitled to reduced fees (the invoicing address has to be the one of the participant)  

 Please note:

• We can only guarantee a certain number of reduced fees per course
• Application deadlines are the same as early registration fees