MODELLING
Copenhagen • Denmark
2-5 December 2012
Early rate deadline: 3 September 2012
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Course Director
Søren M. Bentzen, University of Wisconsin Medical School, Clinical Science Center, Department of Human Oncology, Madison (USA)
Teachers
Francesca Buffa, Physicist, University of Oxford (UK)
Philippe Lambin, Radiation Oncologist, MAASTRO clinic (NL)
Dag Rune Olsen, Physicist, Faculty of Mathematics & Natural Sciences, University of Bergen (NO)
Randall Ten Haken, Physicist, University of Michigan (USA)
Ivan Storgaard Vogelius, Physicist, The Finsen Center-Rigshospitalet, Copenhagen (DK)
Local organisers
Ivan Storgaard Vogelius, Physicist, The Finsen Center-Rigshospitalet, Copenhagen (DK)
Per Munck af Rosenschöld, Physicist, The Finsen Center – Rigshospitalet, Copenhagen (DK)
Course Aims
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be aware of the most commonly used quantitative methods in radiation oncology and radiation biology and the assumptions behind these
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be able to identify appropriate quantitative methods of analysis for a given data set
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be able to critically evaluate modeling results especially with respect to proper validation and estimates of uncertainties
Target Group
The course is aimed at Physicians, medical physicists, therapists.
Educational Programme
Radiation oncology is unique among the medical specialties due to a strong quantitative overhead. Bioeffect models of normal tissue complication probability (NTCP) and tumor control probability (TCP) support clinical decision making and are increasingly used in mathematical optimization of radiation therapy plans. While most of the models are fairly simple – in some cases over-simplified – the problem is that their predictive value is limited by uncertainties in the model parameters. Uncritical reliance on model results may compromise patient safety or treatment outcome. The aim of this course is to make the attendees critical users of models and better at making model-supported decisions.
Topics covered:
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Models and modelling, hypothesis testing and parameter estimation, type I and II uncertainties,
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Clinical trials and evidence-based medicine, Phase 0-IV trial designs, meta-analysis, clinical endpoints, survival statistics and the Cox proportional hazards model,
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Statistical modelling and exploratory data analysis, simple mechanistic models, external and internal validity of models, bootstrap and Monte Carlo methods, goodness of fit,
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Dose-response models, normal tissue complication probability (NTCP) and tumor control probability (TCP) models, modeling combined modality therapy, patient-to-patient variability in response, the linear-quadratic model and beyond, generalized equivalent uniform dose, use of models in treatment planning
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Predictive assays, ROC and AUC, sensitivity, specificity, positive and negative predictive value
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Hierarchical clustering, principal component analysis, neural network, support vector machines, data mining
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High dimensionality data sets, over-fitting, training and validation sets, sample splitting, K-fold validation