Course Director

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

Teachers

• Ane Appelt, Medical Physicist, University of Leeds, Leeds (UK)
• Marleen de Bruijne, Computer Scientist, Erasmus Medical Center, Rotterdam (NL)
• 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)

The aim of this course is to introduce the attendees to a range of quantitative methods that are frequently used in radiation oncology research and, in some cases, clinical decision support tool. 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 treatment 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 10 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 model estimates and to be able to assess the validity or quality of a statistical data analysis or a mathematical model. Uncritical reliance on model results may compromise patient safety or treatment outcome or may take research down the wrong track.

Learning outcomes

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

• Broadly describe the most commonly used 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;
• 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 discussion 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.

Scientific Programme

To follow

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 2024 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

Early rates are applied up to three months before the starting date of the course.

Late rates are applied three months before the starting date of the course.

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

ESTRO does not accept liability for individual medical, travel or personal accidents or incidents. Participants are strongly advised to take out their own personal insurance policies.   

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

In the unlikely event that ESTRO would need to cancel the event, ESTRO will reimburse the participants in full for the registration fee. ESTRO will not refund any travel and accommodation expenses.

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, for this teaching course, 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

conditions below are met:

1. Only ESTRO members for 2024 are eligible (please make sure your 2024 membership is in order before you click on the BOOK NOW button)
2. The application is submitted 3 months before the course start date.
3. Only one course per person per year can be subsidized by ESTRO
4. Sponsored candidates are not entitled to reduced fees (the invoicing address has to be the one of the participant)  
5. The are limited spaces available for each course in the price category. Places are granted on “first come first serve” basis.