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ESTRO SCHOOL
TARGET GROUP
The course is aimed at physicians, medical physicists,
biologists and radiation therapists (RTTs).
COURSE AIM
The aimof this course is tomake the attendees better at
makingmodel-supported decisions. Radiation oncology
probably has the most solid quantitative foundation
amongmedical specialties. As in other specialties, results
of randomised controlled trials form evidence-based
treatment guidelines; but in addition, prognostic and
predictive models provide clinical decision support for
individualisedmanagement of cases. Radiation bioeffect
models of Normal Tissue Complication Probability
(NTCP) and Tumour Control Probability (TCP) have
become much more refined and are increasingly being
validated in independent datasets. 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.
LEARNINGOUTCOMES
By the end of this course participants should be able to:
• Broadly describe themost 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
• Critically evaluate modelling results especially with
respect to proper validation and estimates of uncer-
tainties.
COURSE CONTENT
• Models and modelling, hypothesis testing and pa-
rameter estimation, type I and II uncertainties
• Clinical trials and evidence-basedmedicine, 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,
simple mechanistic models, external and internal va-
lidity of models, bootstrap andMonte Carlomethods,
goodness of fit
• Dose-response models, normal tissue complication
probability (NTCP) and tumour control probability
(TCP) models, modelling combinedmodality therapy,
patient-to-patient variability in response, the line-
ar-quadraticmodel and beyond, generalised equivalent
uniform dose, use of models in treatment planning
• Predictive assays, ROC curves and AUC, sensitivity,
specificity, positive and negative predictive value
• High dimensionality data sets, machine learning,
data mining, over-fitting, training and validation
sets, sample splitting, K-fold validation.
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.
TEACHINGMETHODS
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)
• 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.
Quantitative Methods in Radiation Oncology:
Models, Trials and Clinical Outcomes
8-11 October 2017
Maastricht, The Netherlands