ESTRO 36 Abstract Book

S309

ESTRO 36 2017

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Fig. 1

Example of 3D Doppler image for a cervical cancer

patient. Grey scale represents the morphology, while

color scale is related to the presence of flow in the

corresponding area.

SP-0596 Machine learning and bioinformatics

approaches to combine imaging with non-imaging data

for outcome prediction

O. Gevaert

Stanford University School of Medicine, Biomedical

Informatics Research, Stanford, USA

Radiomics and radiogenomics are burgeoning fields of

science that put quantitative analysis of medical images

(CT, MRI, etc.) central in the analysis towards the goal of

precision medicine. The idea is to extract quantitative

information from images that can be used for tailoring

treatment decisions to the individual patient. More

specifically, radiomics is defined as the quantitative

analysis of medical images by semi-automatically, and

increasingly more automatically, extracting image

features from images . Radiogenomics (also known as

imaging genomics) is concerned with the mapping of high

dimensional molecular data (e.g. transcriptomics,

genomics) with quantitative image features that result

from radiomics pipelines. Recent developments in both

areas of radiomics and radiogenomics are changing the

paradigm of precision medicine. While previous work has

focused mainly on molecular analysis of cancer, radiomics

and radiogenomics propose to harness the power of

quantitative medical imaging. This has several

advantages, medical images are part of the diagnostic

routine, are increasingly more available digitally, and are

non-invasive. Especially the latter, the non-invasive

characteristic, provides translational opportunities for

diagnosis and also in vivo therapy follow-up. For example,

radiomics signatures that predict prognosis (e.g.

recurrence) can be more easily translated without

incurring extra costs. Additionally, radiogenomics allows

mapping molecular pathway activities to image signatures

for non-invasive assessment of pathway activities, and

subsequently hypothesis for targeted treatment. Within

the field of machine learning, deep learning and

convolution neural networks (CNN) have recently

revolutionized analysis of images with many far-reaching

applications outside of medicine. More recently, deep

learning has entered the medical domain, especially in

medical imaging. While most applications have focused on

pathological/histological images, applications on medical

images are emerging. Currently being used mostly by

radiologists to interpret disease and quantitative analysis

is still limited. In the meantime quantitative analysis as

done in radiomics and radiogenomics research has already

shown that additional information is present that is not

necessarily observable by the human eye. Even more,

computer vision algorithms have already shown promise in

predicting crucial clinical or biological characteristics

based on medical images. I will present an update on the

latest work in radiomics and radiogenomics for clinical

outcome prediction. In addition, I will provide a gentle

introduction to deep learning and its potential to rapidly

influence quantitative imaging analysis and eventually

treatment prediction.

SP-0597 Tissue classification models for prostate

based on imaging and non-imaging data

U. Van der Heide

Netherlands Cancer Institute Antoni van Leeuwenhoek

Hospital, Radiation Oncology, Amsterdam, The

Netherlands

Imaging data form the basis for target definition in

radiotherapy. To attain the best possible understanding of

the tissue, increasingly a combination of CT, PET and

multiple MRI sequences are used. For dose differentiation

treatments, such as dose painting, it is necessary to use

the same images to characterize the heterogeneity within

the target volume. With quantitative feature extraction,

this process reaches a new level of sophistication.

Intensity, shape and texture features provide a

characterization of the images that can be used to build a

classifier that characterizes the disease on a voxel-by-

voxel basis.

For prostate cancer, multi-parametric MRI is used

routinely for detection of tumors inside the gland. Using

feature extraction techniques, we constructed a classifier

predicting the presence of cancer. This classifier can be

used either to facilitate target delineation or to apply

directly for dose painting by numbers. While the

performance of image-based classifiers is quite good for

the peripheral zone of the prostate gland, it can be

challenging to classify tissue between cancer and non-

cancerous voxels in the transition zone. Confounders, such

as benign prostate hyperplasia, exhibit similar imaging

features as cancer and are thus hard to distinguish.

In clinical practice, a radiation oncologist has more

information about the patient’s disease to be used to

improve the quality of target delineation. The a-priori

prevalence of the distribution of prostate cancer in the

gland is well known. For example, prostate cancers mostly

occur in the peripheral zone of the gland and less in the

transition zone. As part of their diagnostic work-up,

patients have received biopsies in the gland, proving

cancer presence. The distribution of positive and negative

biopsies of a particular patient is available for a radiation

oncologist and is considered when defining a GTV

delineation. In a study of two independent cohorts of

patients, we show that a classifier that combines a-priori

prevalence and biopsy data with features derived from

multi-parametric MRI, performs significantly better than a

classifier based on imaging data alone.

Imaging features are not only used for tissue classification,

but also to construct prognostic and predictive models for

outcome after treatment. For prostate cancer, similar

models are constructed based on T2-weighted MRI, or

even multi-parametric MRI. Again, addition of non-imaging

data may improve the performance of such models.

Debate: Debate: Precision in radiotherapy: mission

complete!

SP-0598 Precision in RT: mission completed!

A. Duffton

, C. Dickie

Inst. of Cancer Sciences-Univ. Glasgow The Beatson

West of Scotland Cancer Center, Research &

Development Radiographer, Glasgow, United Kingdom

Princess Margaret Cancer Centre, Radiation Medicine

Department, Toronto, Canada

Over the past two decades, radiation therapy treatment

has rapidly changed. Since 2000, Computed tomography

(CT) scans have been used for the definition and

delineation of target volumes by radiation oncologists.

Treatment planning platforms have advanced from 2D to

3D, as well as radiotherapy techniques from 2D

conventional approaches to 3D conformal radiotherapy

(3D-CRT). Moreover, in just one decade Intensity

Modulated Radiotherapy (IMRT) and rotational therapy

(Volumetric

Modulated

Arc

Therapy

(VMAT),

TomoTherapy) have made their appearance. CT

simulation is often accompanied by MR simulation or MR

fusion for radiotherapy planning purposes and over the

next few years, the use of Magnetic Resonance Imaging

(MRI) accelerators will become more widespread providing

new insights and opportunities. Online megavoltage

and/or kilovoltage Imaging before and during radiation

treatment is allowing for localization of the target volume

to deliver the prescribed dose as accurately as possible.