ESTRO 36 Abstract Book

S301

ESTRO 36 2017

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Tumour samples were available from the BCON phase III

trial that randomised bladder cancer patients to

radiotherapy alone or with carbogen and nicotinamide

(CON). Gene expression was analysed in 152 BCON tumours

using Affymetrix Human 1.0 Exon arrays and used for

independent validation.

Results

Published signatures performed inconsistently and tended

to do worse in bladder than prostate and sarcoma. A

bladder-specific signature was derived, which was

prognostic in an independent surgical cohort (n=408,

p=0.00274) and in BCON patients receiving radiotherapy

alone (n=75, hazard ratio [HR] for overall survival 2.80,

95% CI 1.48-5.32, p=0.0016). The signature also predicted

benefit from CON (n=76, HR 0.47, 95% CI 0.26-0.84,

p=0.011). Prognostic and predictive significance remained

after adjusting for clinicopathological variables. A

sarcoma signature was derived that was prognostic in

independent Affymetrix Plus2 (n=127; HR=3.44, 95% CI

1.73-6.84; p<0.001) and The Cancer Genome Atlas (TCGA)

RNAseq (n=246; HR=2.63, 95% CI 1.67-4.14; p<0.001)

validation cohorts. A prostate signature was prognostic in

TCGA (n=491; p<0.001) and an FFPE RNAseq (n=102;

p=0.014) validation cohorts. The prostate signature was

prognostic in the BCON radiotherapy arm (n=75; p=0.049)

and predicted benefit from addition of CON to

radiotherapy (p=0.019).

Conclusion

Tumour-type specific signatures outperform those derived

in other tumour types. Biomarker driven trials are now

required to test these signatures in a prospective setting.

Symposium: 4D imaging and tracked delivery

SP-0574 MLC tracking: from bench to bedside

M. Fast

The Institute of Cancer Research and The Royal Marsden

NHS Foundation Trust, Joint Department of Physics,

London, United Kingdom

Dynamic multi-leaf collimator tracking is an emerging

form of real-time adaptive radiotherapy in which the

treatment beam is continuously reshaped according to the

target motion in beam’s-eye-view. By following the

regular or erratic motion of the treatment target, MLC

tracking ensures its dosimetric coverage. Prominent

examples for intra-fractional anatomy variations are

respiration and gastrointestinal motion. Through better

beam-target alignment and subsequent margin

reductions, MLC tracking is utilized to reduced the size of

the target volume. The reduction of dose to adjacent

healthy tissue in combination with complete target

coverage is especially relevant for highly conformal, hypo-

fractionated treatment protocols.

MLC tracking was first suggested in 2001 [1]. Since then,

MLC tracking has been demonstrated on linac platforms of

all major radiotherapy vendors: Elekta, Siemens and

Varian. Initially, the feasibility of MLC tracking was

established in phantom experiments and treatment

planning studies. Recently, first clinical implementation

trials have started in Sydney, Australia, designed to

demonstrate the safety and efficacy of tracked treatment

deliveries for prostate and lung cancer patients [2]. This

presentation will give a brief summary of past research

activities and derive conclusions with regards to clinical

implementation.

One crucial consideration when translating MLC tracking

from the laboratory `bench’ to the clinical `bedside’ is

quality assurance. Traditional delivery QA approximates

the patient as a static object and relies on the planned

sequence of delivery control points. For MLC tracking,

these approximations are too simplistic. Crucially, the

interplay of patient and MLC motion is not known a-priori.

A task group (TG 264) assembled by the American

Association of Physicists in Medicine (AAPM) is currently

developing guidelines on the “Safe clinical

implementation of MLC tracking in radiotherapy”. At the

same time, novel online QA tools are being developed in

order to accumulate dose in real-time during each

treatment fraction with the same dosimetric precision

achieved in clinical treatment planning systems [3]. This

presentation will summarise tracking QA requirements and

give preliminary recommendations for clinical

implementation.

The efficacy of MLC tracking is limited by the spatial and

temporal resolution of the target localisation modality,

system lag times, and the mechanical performance of the

MLC. Current target localisation strategies rely on x-ray

imaging, often in combination with implanted radiopaque

markers, or implanted resonant circuits and their

detection with an electromagnetic antenna array. Less

invasive and non-ionizing target localisation relying on soft

tissue contrast, namely ultrasound imaging and MRI, are

increasingly being investigated as drivers for MLC tracking

deliveries. For tracked deliveries in the presence of a

strong magnetic field, the electron-return effect could

potentially introduce an additional challenge for the safe

delivery of treatment. A recent treatment planning study,

however, has confirmed the efficacy of MLC tracking on

the Elekta MR-linac prototype [4]. The suitability of the

treatment plan for MLC tracking is another important area

of investigation. As an example, this presentation will

provide a simple template for creating a treatment plan

suitable for a tracked lung SBRT delivery.

References

[1] Keall et al. Motion adaptive x-ray therapy: a feasibility

study. Phys Med Biol 2001;46:1-10.

[2] Keall et al. The first clinical implementation of

electromagnetic transponder-guided MLC tracking. Med

Phys 2014;41: 020702.

[3] Fast et al. Assessment of MLC tracking performance

during hypofractionated prostate radiotherapy using real-

time dose reconstruction. Phys Med Biol 2016; 61: 1546–

1562.

[4] Menten et al. Lung stereotactic body radiotherapy with

an MR-linac–Quantifying the impact of the magnetic field

and real-time tumor tracking. Radiother Oncol

2016;119:461-466.

SP-0575 Motions models and tracking using MR

R. Tijssen

UMC Utrecht, Department of Radiation Oncology,

Utrecht, The Netherlands

Image guidance plays an important role in modern

radiotherapy. Current x-ray based onboard imaging,

however, has poor soft-tissue contrast, which challenges

image guidance of mobile tumors. Particularly in

abdominal regions, tumor visualization is virtually

impossible

clinical

CBCT

images.

By utilizing Magnetic Resonance Imaging (MRI) for

radiation guidance, tumor and organs-at-risks (OAR) can

be directly visualized and targeting can be improved. This

has been the philosophy behind the development of the

MRI-Linac (MRL). An MRI based workflow has the potential

to shape the radiotherapy paradigm of the future: an

online adaptive treatment in which positional

uncertainties are mitigated and the deposited dose can be

tracked in real-time making dose escalation possible while

sparing healthy tissue.

In order to facilitate this new paradigm, fast MRI

acquisition and processing methods are needed that

accurately map the motion within the entire irradiated

volume with sufficient temporal and spatial resolution.

Respiratory motion models compliment real-time imaging

in two ways: 1) motion prediction models overcome the

inherent latency in tracked delivery associated with the

real-time feedback loop, and 2) motion estimation models