ESTRO 36 Abstract Book

S228

ESTRO 36

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by TME surgery and optional postoperative

chemotherapy and an experimental arm B:

short course 5

x 5 Gy radiation followed by six cycles of full-dose CAPOX

or nine cycles of FOLFOX and TME surgery.

Results

A total of 920 patients were included between June 2011

and June 2016. At randomisation, 302 were cT4 and 828

were cN+, of whom 621 were considered cN2 disease and

137 as extramesorectal pelvic lymphnodes. Based on MRI,

extramural vascular invasion was diagnosed in 275

patients, whereas the mesorectal fascia was threatened in

564 patients.

Preliminary data show that median time between

randomization and surgery was 15,9 weeks for arm A and

25,3 weeks for arm B. In arm B, 100% of the patients who

started, completed the radiotherapy and 72% of patients

completed all scheduled cycles of neoadjuvant

chemotherapy after 5x5 Gy. Another 9% of patients

completed the last course(s) without oxaliplatin. In arm A,

96% received all scheduled radiotherapy fractions and 94%

of the patients received 5 weeks of preoperative

capecitabine

combined

with

radiotherapy.

Open surgery was performed in 59% of the patients and

35% underwent an APR. In total, 19% of patients had a

ypT0N0. For 4% of all patients a wait & watch strategy was

applied. Of the operated patients, 89% had a negative

circumferential resection margin (> 1 mm).

Conclusion

Compliance for neoadjuvant treatment was good in both

treatment arms. Given the locally advanced state of most

tumors, the ypT0N0 rate can be considered satisfactory.

Final data and details concerning differences in pre-

treatment characteristics and treatments between the

two arms will be presented.

Joint Symposium: ESTRO-ESR: Radiomics and imaging

databases for precision radiation oncology

SP-0430 Radiomics in radiology, what are the

parameters of interest for different imaging

modalities?

H. Ahlström

Uppsala University, Dept of Radiology, Uppsala, Sweden

CT, MRI, PET, PET-CT and PET-MRI datasets contain huge

amounts of spatially detailed morphological, functional

and metabolic information. Today, when analysed, these

detailed datasets are typically heavily reduced to a few

measurements of a priori specified measurements of

interest

(e.g.

volumes,

areas,

diameters,

average/maximum tracer concentrations etc.) and/or

visually – and therefore inevitably subjectively – assessed

by a human operator. As a result, normality/non-normality

can only be assessed on these measurements and not on

the entire data collected, and statistical interaction with

non-imaging parameters can also be assessed only on

these a priori specified measurements. In order to utilise

the full potential of these image datasets, new analysis

tools included in the concept Radiomics, that allow

objective or quantitative assessment of all imaging data

(including e.g. previously discarded information about

texture), are needed. Radiomics can be divided into

distinct processes: (a) image acquisition and

reconstruction, (b) image segmentation and rendering, (c)

feature extraction and feature qualification and (d)

databases and data sharing with non-imaging data (e.g.

different “omics” and clinical data) for (e) informatics

analyses. Statistical knowledge of the normal range of

Radiomics features are needed for the analyses. These

analyses are anticipated to bring out new associations and

understandings that traditional approaches could not

achieve. Radiomics features can, together with non-

imaging data, be included in models that have shown to

provide valuable diagnostic, prognostic or predictive

information for oncological diseases. This information

aims at improving individual patients’ outcomes by a

better treatment selection.

SP-0431 Radiomics in radiotherapy. How is it used to

personalise treatment and to predict toxicity and/or

tumour control

C. Gani

University Hospital Tübingen Eberhard Karls University

Tübingen, Radiation Oncology Department, Tübingen,

Germany

Radiomics is defined as the automated or semi-automated

extraction of a large number of features from imaging

datasets resulting an individual “imaging phenotype”.

These features and the imaging phenotype can then be

correlated with a variety of other parameters: from

genetic phenotypes to oncological outcome data.

Radiomics as a non-invasive procedure is of particular

interest for the radiation oncologist in times of precision

radiation oncology: The radiomics phenotype might help

to identify patients at high risk for treatment failure and

therefore candidates for more aggressive treatment.

Furthermore radiomics can also be a helpful tool to

predict the risk for radiation-induced toxicities and guide

the dose distribution within normal tissues. This lecture

will give an overview about the existing data on radiomics

in the field of radiation oncology.

SP-0432 Uncertainties in imaging -how they should be

reported and propagated in prediction models using

radiomics

L. Muren

Aarhus University Hospital - Aarhus University, Medical

Physics, Aarhus, Denmark

Abstract not received

SP-0433 Imaging biobanks: challenges and

opportunities

A. Van der Lugt

Erasmus MC University Medical Center Rotterdam,

Department of Radiology, Rotterdam, The Netherlands

An imaging biobank can be defined as an organised

database of medical images and associated imaging

biomarkers (radiology and beyond) shared among multiple

researchers, and linked to other biorepositories. An

imaging biobank is designed for scientific use. Image data

are systematically analysed visually, manual, or (semi)-

automated with the main aim to extract imaging

biomarkers than can be related to patient characteristics

like medical history, genomic data, and outcome or

disease characteristics like genomic data, biomaterials or

response to treatment. The data storage is structured in a

way that the database can be queried and retrieved based

on available metadata. In order to exploit the available

information interactions with other databases are a

perquisite. General requirements with respect to the data

collection are therefore a database facilitating storage of

image data and metadata, storage of derived image-based

measurements, and storage of associated non-imaging

data, taking into account the need to deal with

longitudinal data, and to cope with multiple file formats.

Finally, automated retrieval is needed for image analysis

pipelines that extract image features for radiomics

signatures or for hypothesis free deep learning algorithms.