ESTRO 36 Abstract Book

S102

ESTRO 36 2017

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Making accurate prediction for these outcomes requires

data on many patients, preferably from many institutions

using different treatment protocols. Also these data needs

to be of high quality, rich in its content and will come from

many data domains (clinical, imaging, omics, society,

quantified self). Integration of data across these domains

and across hospitals worldwide and analysis of such

complex datasets is the topic of this seminar.

With lung cancer as the running example, known

prognostic and predictive factors for outcomes after

radiotherapy will be summarized to show the domains

from which data needs to be integrated and the challenges

associated with each domain. The problem of

dimensionality reduction, risk of over-fitting and need for

robust validation will be detailed. Then the need and

barriers for integration data across vertical data partitions

(e.g. departments in a hospital having a ‘slice’ of data for

a give patient) and across horizontal partitions (e.g. each

hospital has a patient cohort with data) will be discussed.

Finally, some example implementations and first results

of efforts to integrate and analyse these complex data will

be shown.

SP-0203 Innovative clinical trial designs for

Personalised Radiation Oncology

S. Brown

University of Leeds, Institute of Clinical Trials

Research, Leeds, United Kingdom

Radiation oncology research requires use of appropriate

novel trial designs and robust statistical strategies to

ensure reliable decision making. Phase I/II drug trials

typically focus on identifying the maximum tolerated dose

of a novel therapy. In the context of radiation oncology,

this concept is not so clear cut. Various aims of phase I/II

trials investigating radiotherapy-novel agent combinations

include identifying optimum scheduling, determining the

optimum biologic dose, and obtaining preliminary

estimates of efficacy. Difficulties in attributing toxicities

and evaluating longer-term toxicity also pose problems in

designing early phase radiation oncology trials.

Opportunities exist to apply novel clinical trial designs to

the setting of phase I and II trials in radiation oncology, to

overcome some of these difficulties, and to also enable

investigation of a number of novel agent-radiotherapy

combinations together. Several practical designs

addressing this will be discussed in this session, as well as

those taking into account known biomarkers in the

evaluation of efficacy.

SP-0204 Decision support systems and shared decision

making

M.A. Gambacorta

, G. Chiloiro

, C. Masciocchi

, N.

Dinapoli

, F. Cellini

, A. Re

, V. Valentini

Università Cattolica del Sacro Cuore -Policlinico A.

Gemelli, Radiation Oncology, Rome, Italy

The personalized approach to cancer patient requires a

shared decision which derives from the contribution of

every single specialist involved in the cure of any single

person affected by cancer. The tumor behaviour and

patient outcomes are related to several factors. These

factors and their interactions are sometimes poorly known

by the doctors who will take the therapeutic decision for

the patients. Different branches of medicine have

developed their own lines of research which are

sometimes difficult to be interpreted, difficult to be

integrated with classical clinical factors and for these

reasons, difficult to be applied in clinical practice. In the

clinical prediction and decision making process, results

provided by these researches are rarely included, whereas

clinicians usually use few clinical and imaging data for

understanding tumor behaviour, predicting patients'

outcomes and thus for choosing the the most

suitable treatment. In the last years, in the literature,

several studies based on the analysis of large data-base

started to appear. These type of studies uses an advanced

statistic aiming to find the connections between different

covariates in predicting outcomes. The clinical decision

is usually based on general guidelines which extrapolate

information from randomized clinical trial (RCT).

Moreover independent factors, derived from several RCT,

are used by the Oncologists to make his prevision on tumor

behaviour and consequently to choose the „right

treatment“ for a specific patient. RCT enclose patients

with characteristics chosen beforehand and usually the

innovative information are never or rarely included. This

lead to a potential miss of several information that could

refine prediction and thus promote personalized

treatments and to an erroneous outcomes prediction that

can lead to un-appropriate treatment decision for a

specific patient. Integrative data analysis has the

potential to correlate data of different origins (genetic,

radiology, clinic,...) with patient’s outcomes and to

create a consistent dataset useful to obtain a trustful

analysis for the Decision Support System (DSS). The DSS

can easily be applied in clinical practice helping the

Oncologist to merge several information that otherwise

would be excluded in the process of decision making. The

possibility to predict the outcome for a certain patient in

combination with a specific treatment with more

accuracy, will lead to better identification of risk groups

and thus better treatment decisions in individual patients,

but it will also stimulate research focused on specific risk

groups which try to find new treatment options or other

combinations of treatment options for these subgroups.

These treatments will be more personalized, which will

not only save patients from unnecessary toxicity and

inconvenience, but will also facilitate the choice of the

most appropriate treatment . The resulting predictive

models, based on patient features, enable a more patient

specific selection from the treatment options menu and a

possibility to share decisions with patients based on an

objective evaluation of risks and benefits. Finally,

considering the important role that predictive models

could play in the clinical practice, clinicians must be

aware of the limits of these prediction models. They need

to be internally validated taking into account the quality

of the collected data. An external validation of models is

also essential to support general applicability of the

prediction model. Therefore structural collaboration

between different groups is crucial to generate enough

anonymized large databases from patients included or not

in clinical trials.

Symposium: Safety and clinical and cost effectiveness of

multi-modality IGRT and ART

SP-0205 What evidence is needed to assess cost-

effectiveness of new technology and how can we get it

(easily)?

M. Johannesma

Health Insurance Company CZ, Department Innovation &

Advice, Tilburg, The Netherlands

The incidence of cancer is increasing worldwide. By this,

but also due to the rapid diffusion of new technologies,

there is a continues rise in health care costs. For decision-

making, primary studies of costs of health technology are

gaining importance and cost-effectiveness analysis are

increasingly used to estimate the incremental health gain

for an incremental use of resources. Economic evaluation

in health care, the comparative analysis of alternative

interventions in terms of both their costs and

consequences, are performed with the aim to determine

the value for money of new treatments compared to the

prevailing standard. In their aim to support decision

making in health care, they represent one aspect within