Group leader: Stephanie Tanadini-Lang
Group members: Marta Nesteruk, Alex Vils, Xaver Würms
Cancer is a heterogeneous disease in regard to etiology, pathogenesis, therapy response and prognosis. Tumor response to therapy varies not only among patients but also within the tumor itself. For optimizing treatment strategies, identification of biomarkers may be essential. Imaging biomarkers are of special interest as they provide spatial information on tumor biology and are acquired non-invasively.
In recent years, radiomics has become increasingly important for medical image characterization, both in terms of volume segmentation and prediction of treatment response. Using our in-house developed Software we can extract about 700 radiomic features describing tumor shape, tumor intensity, tumor texture from medical images (figure below).
Based on mathematical definitions we investigate tumor
morphology as well as the prominent perceptual texture characteristics such as regularity
(or periodicity), directionality and complexity. Altogether, texture features
provide much more information about a region of interest than the mean or
maximum intensity values, generally used in clinical medicine.
These radiomic features can be used for
outcome prognosis or for correlation to
the tumor biology.
Recently we have developed a prognostic
model based on 3 radiomic features to assess local tumor control in squamous
cell carcinoma of the head and neck treated with definitive radio-chemotherapy.
We were able to separate the patients into two groups with excellent and
- Nesteruk, M., Lang, S., Veit-Haibach, P., Studer, G., Stieb, S., Glatz, S., ... & Guckenberger, M. (2015). Tumor stage, tumor site and HPV dependent correlation of perfusion CT parameters and [18F]-FDG uptake in head and neck squamous cell carcinoma. Radiotherapy and Oncology, 117(1), 125-131.
- Bogowicz, M., Riesterer, O., Bundschuh, R., Veit-Haibach, P., Huellner, M., Studer, G., Stieb, S., Glatz, S., Pruschy, M., Guckenberger, M., Tanadini-Lang, S. Stability of radiomic features in CT perfusion maps. Physics in medicine and biology, accepted for publication
Automated CTV delineation
Group Leader: Prof. Dr. Jan Unkelbach
Group Members: Bertrand Pouymayou
Many tumors infiltrate the adjacent normal tissue beyond the mascroscopic tumor mass (GTV) that is visible on today's imaging modalities such as CT, MR, and PET. This represents a challange for defining the clinical target volume (CTV) in radiotherapy, ie the volume that contains microscopic disease and therefore is to be irradiated. While GTV delineation amounts to defining a visible tumor mass on CT, MR, und PET imaging, CTV delineation is not based on visualizing tumor cells directly. It is rather based on antatomical imaging in order to localize the anatomically defined routes of microscopic tumor progression. We work on automated methods for CTV delineation - based on imaging, image processing, and computational models of tumor progression - to consistenty account for complex patient anatomy.
CTV delineation for glioblastoma
Glioblastoma is the most common primary brain tumor. Glioblastoma are known to infiltrate the healthy appearing brain tissue far beyond the GTV that is visible on MRI. In current clinical practice, many practitioners account for the infiltrative growth by expanding the GTV with a 1-3 centimeter margin to form the CTV which is irradiated to a homogeneous dose of 60 Gy. Target delineation can potentially be improved by accounting the anisotropic spatial growth patterns of gliomas, which are observed in histopathology and MR imaging:
- Anatomical boundaries: The dura, including its extensions falx cerebri and tentorium cerebelli, represents a boundary for migrating tumor cells. Also, except for rare cases of CSF seeding, gliomas do not infiltrate the ventricles.
- Tumor cells infiltrate gray matter much less than white matter.
- Tumor cells seem to migrate primarily along white matter fiber tracts.
Accounting for these growth characteristics requires an interdisciplinary effort involving mathematical modeling techniques, image processing, and analysis of clinical data. We investigate the use of a phenomenological tumor growth model for treatment planning, which replicates these growth patterns. The model is based on the Fisher-Kolmogorov equation, a partial differential equation of reaction-diffusion type. The model predicts the spatial distribution of tumor cells in regions of the brain that appear normal using current imaging techniques. It is personalized for a given patient using MRI data obtained routinely for glioma patients. More specifically, a segmentation of the brain into white matter, gray matter, cerebrospinal fluid, and tumor. The brain tissue segmentation allows us the solve the model equations on the patient specific geometry. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density.
In preliminary studies we have identified situations in which the use of the tumor growth model for radiotherapy target definition leads to differences compared to the clinical plan that was actually delivered. This is illustrated in the figures below. Figure 1 shows the T1 post contrast image of a GBM located in the left parietal lobe, close to the falx and the corpus callosum. Figure 4 shows the FLAIR image of the same patient, revealing peritumoral edema surrounding the central tumor mass. Figure 2 shows the segmentation of the brain into white matter, gray matter and CSF as well as the segmentation of the tumor into enhancing core (blue) and peritumoral edema (red).
Figure 3 shows the simulated tumor cell
density using the growth model. The model reproduces important spatial growth
patterns of glioblastomas: The falx is modeled as an anatomical boundary, which
prevents tumor cells from migrating into the contralateral hemisphere. At the
same time, the corpus callosum, white matter fiber tracts connecting the cerebral
hemispheres, is modeled as a route for contralateral spread of tumor cells.
Furthermore, the glioma growth model allows us to describe reduced infiltration
of gray matter surrounding major sulci, which can be seen primarily in the
region of the lateral sulcus.
Figure 4 illustrates the use of the growth
model for target delineation. It compares the manually delineated target
(yellow) used in the clinical treatment plan to the target contour derived from
the model (red). The red contour corresponds to an isoline of the tumor cell
density that encloses the same total volume as the manually defined target. For
this patient, the model suggests a further extension of the target into the
- J. Unkelbach,
B. H. Menze, E. Konukoglu, F. Dittmann, M. Le, N. Ayache, and H. Shih.
Radiotherapy planning for glioblastoma based on a tumor growth model: improving
target volume delineation. Phys. Med. Biol., 2014; 59(3):747-770
Helen Shih; Massachusetts General Hospital,
Ender Konukoglu; ETH, Zürich, Switzerland
Group of Nicholas Ayache, INRIA, Sophia
Bjoern Menze, TU München, Germany
Treatment plan optimization for intensity-modulated radiotherapy (IMRT/IMPT)
Group Leader: Prof. Dr. Jan Unkelbach
Treatment planning for radiotherapy is based on two main components: Dose calculation algorithms and mathematical optimization algorithms. Dose calculation algorithms use physical models to describe the interaction of radiation in tissue to calculate the distribution of absorbed radiation dose in the patient. Mathematical optimization methods are used to optimize intensities and incident directions of external radiation fields in order to irradiate the tumor while minimizing the radiation dose to surrounding normal tissues. Our group has worked on many problems related to the further development of optimization algorithms for treatment planning. This includes direct aperture optimization (DAO) [3,4], volumetric modulated arc therapy (VMAT) , multi-criteria optimization (MCO) , beam angle optimization and non-coplanar VMAT , and robust optimization for handling uncertainties in intensity modulated proton therapy .
- D. Papp, T. Bortfeld, J. Unkelbach. A modular approach to intensity-modulated arc therapy optimization with noncoplanar trajectories. Phys. Med. Biol., 2015; 60(13):5179-5198
- D. Papp and J. Unkelbach. Direct leaf trajectory optimization for volumetric modulated arc therapy with sliding window delivery. Medical Physics, 2014; 41:011701
- Cassioli A and Unkelbach J. Aperture shape optimization for IMRT treatment planning. Phys. Med. Biol. 2013; 58(2):301-18
- Salari E and Unkelbach J. A column-generation based technique for multi-criteria direct aperture optimization. Phys. Med. Biol. 2013; 58:621-39
- J. Unkelbach, B. Martin, M. Soukup, and T. Bortfeld. Reducing the sensitivity of IMPT treatment plans to setup errors and range uncertainties via probabilistic treatment planning. Medical Physics. 2009; 36:149-163
David Craft, Thomas Bortfeld; Massachusetts General Hospital, Boston, MA
David Papp; North Carolina State University, USA
Mark Bangert; German Cancer Research Center (DKFZ), Heidelberg, Germany
Optimal fractionation and spatiotemporal fractionation schemes
Group Leader: Prof. Dr. Jan Unkelbach
In current clinical practice, most radiotherapy treatments are fractionated. This is motivated by the observation that most healthy tissues can tolerate a much higher total dose if the radiation is split into small fractions. On the other hand, fractionation typically requires that a higher total dose is delivered to the tumor in order to achieve the same level of response. Fractionation decisions therefore face the tradeoff between increasing the number of fractions to protect normal tissues and increasing the total dose to maintain the same level of tumor control.
In that regard, the ideal treatment would fractionate in normal tissues, and at the same time hypofractionate in the tumor. This appears to be impossible at first glance because the dose to normal tissues is an unavoidable consequence of delivering dose to the tumor. Generally, increasing the dose to the tumor in a given fraction will increase the dose to healthy tissues in that fraction. However, interestingly it is possible to achieve some degree of hypofractionation in parts of the tumor while exploiting the fractionation effect in normal tissues. The latter can be achieved by delivering distinct dose distributions in different fractions, a concept which is referred to a spatiotemporal fractionation.
Figure 1 illustrates the concept of spatiotemporal fractionation for treating a large cerebral arteriovenous malformation (AVM). The treatment consists of 4 fractions delivered with rotation therapy (VMAT or Tomotherapy). Each fraction delivers a high single fraction dose to a distinct part of the target volume.
At the same time, each fraction creates a similar dose bath in the surrounding normal brain and thereby exploits the fractionation effect. Hence, partial hypofractionation in the target volume is achieved with more uniform fractionation in normal tissues, which yields a net improvement of the therapeutic ratio. This demonstrates that there may be a benefit of delivering different dose distributions in different fractions, purely motivated by fractionation effects rather than geometric changes of the patient.
Figure 1: Spatiotemporal treatment plan for a large cerebral AVM.
- J. Unkelbach, C. Zeng, and M. Engelsman.Simultaneous optimization of dose distributions and fractionation schemes in particle radiotherapy. Med. Phys. 2013; 40(9):091702
- J. Unkelbach, D. Papp. The emergence of nonuniform spatiotemporal fractionation schemes within the standard BED model. Med. Phys., 2015;42:2234-2241
- J. Unkelbach, M. Bussière, P. Chapman, J. Loeffler, H. Shih. Spatiotemporal Fractionation Schemes for Irradiating Large Cerebral Arteriovenous Malformations. Int. J. Rad. Onc. Biol. Phys., 2016 (in press)
International clinical collaborators:
Helen Shih, Jay Loeffler, Paul Chapman, Ted Hong; Massachusetts General Hospital, Boston, MA
International mathematical collaborators:
David Papp; North Carolina State University, USA
Ehsan Salari; University of Kansas, Wichita, USA
LET in proton therapy planning
Group Leader: Prof. Dr. Jan Unkelbach
In-vitro cell survival experiments suggest
an increase in proton relative biological effectiveness (RBE) towards the end
of range. Although the data from in-vitro experiments varies substantially, it
suggests that the RBE might increase from values between 1.0 and 1.1 in the
entrance region to values around 1.3 at the Bragg peak and 1.6 in the falloff
region . It is typically assumed that this RBE increase is explained by an
increase of linear energy transfer (LET) towards the end of range. On the other
hand, proton treatment planning and dose reporting has been based on physical
dose and a constant RBE of 1.1.
This creates a dilemma for proton therapy
planning, especially for IMPT. Underestimation of RBE may lead to
underestimation of normal tissue complication probabilities. IMPT treatments
with highly modulated fields may deliver highly inhomogeneous LET
distributions. This may result in LET hot spots in critical structures within
or near the target volume. On the other hand, large uncertainties in RBE, and
the fact that dose reporting has historically been based on physical dose, discourage
RBE-based IMPT planning approaches that lead to drastic changes compared to
A possible hybrid approach to address this
dilemma consists in LET-guided IMPT planning as recently suggested by our
group. In contrast to previous works, our method does not assume knowledge of
RBE to perform biological IMPT planning. Instead, it is designed to facilitate
IMPT planning in the absence of reliable normal tissue RBE values. We first
determine an IMPT plan based on physical dose objectives, as is current
clinical practice. In a second step, we modify the LET distribution to avoid
high LET in critical structures. This is done using a prioritized optimization
scheme, in which LET-based objectives are optimized while limiting the
degradation of the physical dose distribution.
In that sense, IMPT treatment plans become safer, while allowing the
planning process to be consistent with current dose reporting.
Figure 1a illustrates this approach for a
atypical meningioma patient, in whom the target volume (red) overlaps the
brainstem (green), the optic nerve, the chiasm, and the pituitary gland (orange).
Traditional IMPT planning based on physical dose provides highly conformal dose
distributions (1c). Figure 1e shows the spatial distribution of the product of
LET and physical dose, which serves as a first order approximation of the
additional biological dose that is caused by high LET. In this example, high
LET is observed in critical structures in the target volume. After the LET
reoptimization step, such LET hot spots in critical structures can be avoided
(1f) while minimally compromising the physical dose distribution (1d).
Figure 1: LET based IMPT reoptimization for
a meningioma patient.
It is clear that, in order to modify the
LET distribution in critical structures, the dose to these regions has to be
delivered by different pencil beams. This is illustrated in Figure 1b, which
shows the difference between the physical dose distributions (the reoptimized
plan is subtracted from the reference plan). The fluence of pencil beams
incident from the patient's left (right side of the image) that stop in the
OARs is reduced. Instead, more dose is delivered by pencil beams incident from
the patient's right (left side of the image).
- J. Unkelbach,
P. Botas, D. Giantsoudi, B. Gorissen, H. Paganetti. Reoptimization of
intersity-modulated proton therapy plans based on linear energy transfer. Int.
J. Rad. Onc. Biol. Phys., 2016 (in press)
Group of Harald Paganetti; Massachusetts
General Hospital, Boston, MA
Applied Medical Physics
Group Leader: Dr. Stephan Klöck
Group members: Frederique Cavelaars, Mattia Di Martino, Stefanie Ehrbar, Dr. Jérôme Krayenbühl, Izabela Pytko, Dr. Sabrina Stark, Dr. Tino Streller, Anja Stüssi, Dr. Stephanie Tanadini-Lang, Alessandra Tini
The scientific work of the group for applied medical physics is dealing with scientific evaluation of technical innovations in collaboration with various vendors, and the implementation of new treatment techniques in close collaboration with clinicians.
In the first one pilot studies are performed on technical innovations, which are implemented and evaluated in close collaboration with the vendors. Often these innovations are not yet available on the market:
Together with Philips (USA) and Varian Medical Systems (USA) we have investigated different approaches of automated plan creation for modulated treatments (Dr. Jérôme Krayenbühl, Dr. Mariangela Zamburlini) to increase the consistency of treatment plans. Parts of the results have been awarded with the Q-Award of the UniversityHospital Zurich.
In the second category new clinical treatment techniques are introduced in close collaboration with clinicians and evaluated with methodology of radiation physics:
One of the possible motion mitigation techniques for stereotactic body radiation therapy of lung tumors is the mid-ventilation technique, where the treated volume can be reduced on basis of a probabilistic approach to determine the path excursion of the tumor compared to the whole envelope of motion (Dr. Stephanie Tanadini-Lang, Stefanie Ehrbar). Single session radio surgery treatments of single and multiple brain metastasis have been introduced using a new cranial fixation device and an electromagnetic surface transponder system for patient monitoring (Dr. Jérôme Krayenbühl, Izabela Pytko, Dr. Stephanie Tanadini-Lang).
Many patients with recurrent cancer or metastatic disease need a re-irradiation. As the organs at risk of the environment have certain dose limits, the previous treatments have to be precisely reconstructed on the new CT. For this purpose deformable image and dose registration algorithms have been evaluated (Izabela Pytko).
Treatments of head and neck cancer with simultaneous integrated boost and of lung cancer receive a plan adaptation dealing with the morphological changes in the target region within the first three weeks of treatment (Dr. Tino Streller).
For the motion management of pancreatic cancer treatment three patients received a clip at the duodenum carrying two electromagnetic transponders, which allow for localizing the position of the pancreas/ duodenum during treatment in real time (Dr. Stephanie Tanadini-Lang).
- Ehrbar, S., S. Lang, S. Stieb, O. Riesterer, L. S. Stark, M. Guckenberger and S. Klöck (2016). "Three-dimensional versus four-dimensional dose calculation for volumetric modulated arc therapy of hypofractionated treatments." Z Med Phys 26(1): 45-53.
- Krayenbühl, J., M. Zamburlini, I. Norton, S. Graydon, G. Studer, S. Klöck and M. Guckenberger (2016). "Automated treatment plan generation - the Zurich experience. Abstract (Nr. SP-0311) ESTRO 35, Symposium Automated treatment plan generation in the clinical routine, IT-Turin, 29.04.-02.05.2016." Radiother and Oncol Vol. 119, Supplement 1: 144-145.
- Tini, A., I. Pytko, S. Lang, C. Winter, M. Guckenberger and C. Linsenmeier (2016). "Clinical implementation of an optical surface monitoring system(OSMS®, Varian) in breast irradiation. Abstract (Nr. EP-2113) ESTRO 35, IT-Turin, 29.04.-02.05.2016." Radiother and Oncol Vol. 119, Supplement 1: 993.
- Lang, S., C. Linsenmeier, M. L. Brown, F. Cavelaars, A. Tini, C. Winter and J. Krayenbuehl (2015). "Implementation and validation of a new fixation system for stereotactic radiation therapy: An analysis of patient immobilization." Pract Radiat Oncol Vol. 5, Nr. 6: e689-695.
- Pytko, I., A. Stüssi, S. Lang, S. Klöck and M. Guckenberger (2015). "Feasibility study of using a radiofrequency tracking system for intra-fractional monitoring during radiosurgery. Electronic Poster (Nr. EP-1486) 3rd ESTRO Forum, Sp-Barcelona, 24.-28.04.15." Radiother Oncol Vol. 115, Supplement 1: 808.
- Krayenbühl, J., I. Norton, G. Studer and M. Guckenberger (2015). "Evaluation of an automated knowledge based treatment planning system for head and neck." Radiat Oncol (online Journal) Vol. 10, Nr. 1 (published 10.11.15): 226.
Motion compensation through couch tracking
Group leader: Stephanie Tanadini-Lang
Group members: Stefanie Ehrbar, Alexander Jöhl, Konstantina Karavas
Modern linear accelerators achieve sub-millimeter accuracy in dose delivery. However, not all tumors are stable during the treatment session. Tumors in the lung can move up to 16 mm, in the liver up to 34 mm and the thoracic wall moves up to 14 mm due to respiratory motion. In these cases, tumor motion management is needed to accurately irradiate the tumor while sparing healthy tissue. This becomes important for large treatment volumes, where dose to the surrounding tissue is a limiting factor, and for hypo-fractionated treatments, where high doses are applied in a small number of treatment fractions.
The three motion management techniques currently used in the clinical setting are motion-encompassing treatment, gating and tracking. In comparison to motion-encompassing treatment, gating allows treatment to a smaller volume; however, at the cost of substantially increased treatment time. The most sophisticated treatment technique appears to be tracking because it confines the high dose to the tumor (small volume) and is time efficient.
In Zurich, we have developed and evaluated a couch tracking system to counter-steer the tumor motion during radiotherapy treatments (compare figures below).
In several studies,
we have evaluated the performance of this tracking system and the potential
clinical benefit of couch tracking for lung, prostate and pancreatic cancer.
Currently we are working on a study to assess the tolerance of volunteers to the motion of the treatment couch and the integration of different image acquisition methods into the tracking system as motion feedback systems.
- Lang, S., Zeimetz, J., Ochsner, G., Daners, M. S., Riesterer, O., & Klöck, S. (2014). Development and evaluation of a prototype tracking system using the treatment couch. Medical physics, 41(2), 021720.
- Jöhl, A., Lang, S., Ehrbar, S., Guckenberger, M., Klöck, S., Meboldt, M., & Schmid Daners, M. (2016). Modeling and performance evaluation of a robotic treatment couch for tumor tracking. Biomedical Engineering/Biomedizinische Technik.
- Ehrbar, S., Perrin, R., Peroni, M., Bernatowicz, K., Parkel, T., Pytko, I., Klöck, S., Weber, D., Guckenberger, M., Tanadini-Lang, S., Lomax, A. (2016). Respiratory motion-management in stereotactic body radiation therapy for lung cancer–A dosimetric comparison in an anthropomorphic lung phantom (LuCa). Radiotherapy and Oncology.
- Ehrbar, S., Schmid, S., Jöhl, A., Klöck, S., Guckenberger, M., Riesterer, O., & Tanadini-Lang, S. (2017). Validation of Dynamic Treatment-Couch Tracking for Prostate SBRT. Medical Physics.
Collaborations: ETH Zurich, Chair of Product Dev.& Eng. Design