Biological, robust, and 4D planning for hadron therapy

Another emphasis of our group lies in the enhancement of treatment planning solutions for particle therapy. Particle therapy which usually refers to irradiation with protons or carbon ions is a relatively new field. Charged particles have very promising physical and biological properties that patients can benefit from: the dose they deposit can be shaped very precisely and moreover the patient may benefit from enhanced radiobiological porperties of these beams. But the high precision of particle therapy also bears risks. A small error in patient setup or the proton range calculation may lead to both critical over- and underdosage of irradiated tissues. We are trying to incorporate both the advantages and risks connected with particle therapy into the treatment planning software to ensure that a patient can be safely treated with high quality.

Biological treatment planning

Our main tool is the in-house developed treatment planning system KonRad. In the past decade this tool has been extended to incorporate biological optimization for both protons and carbon ions. Where classical treatment planning optimizes the deposited dose we are optimizing the biological effect instead. The relative biological effectiveness of particles varies considerably with the distance particles have traveled through tissue. The treatment planning system especially accounts for these changes aiming to ensure a homogeneous biological effect across the tumor. KonRad was the first treatment planning system providing methods to optimize the biological effect for both protons [1] and carbon ions [2] and as such it has been used to directly compare protons and carbon ions for the first time [3]. In a clinically more relevant setting we used the biological optimization for protons to investigate the risks that are currently taken by simply assuming that protons have a constant relative biological effectiveness [4].

Figure 1: Absorbed dose (blue) and RBE-weighted dose (green) of a proton (left panel) and a carbon ion (right panel) spread out Bragg peak. RBE-weighted dose is a photon dose that would lead to an equivalent cell kill rate as the respective particle’s absorbed dose. Clearly, the overall increase in cell kill is significantly higher for carbon ions than for protons. Yet, if the ratio of the RBE-weighted dose in the target to the RBE-weighted dose in normal tissue is better for carbon ions or protons is still part of an ongoing discussion.
© dkfz.de

Functional imaging in treatment planning

Recently we have been working on the integration of information obtained by functional imaging into the treatment planning process. In a first step we focus on the influence of cellular oxygen pressure. Low oxygen supply, which can be measured by positron emission tomography (PET), has been linked to a higher radioresistance in tumors. We are developing methods to include this information into our treatment planning system so that it automatically increases the dose in radioresistant regions of the tumor volume to ensure sufficiently high cell kill rates.

Figure 2: The hypoxia reduction factor (HRF) is a measure of the additional dose that is required to keep the rate of cell kill constant in tissues with poor oxygen supply (low partial oxygen pressure pO2). The higher the linear energy transfer (LET) the lower the influence of the oxygen concentration. The white lines indicate the region of LET values observed in the patient.
© dkfz.de

Robust treatment planning

Particles have finite ranges in matter. This characteristic implies great potential for radiation therapy as particle beams can be used to deliver very high doses to the tumor volume while sparing healthy tissues. At the same time, however, this characteristic also implies great challenges: Errors during patient setup or range calculations may result in significant under dosage within the target volume and/or over dosage in normal tissues.

At DKFZ, we were the first to include these sources of uncertainty into the treatment planning process and to quantify their impact on the quality of the resulting treatment plans [5, 6, 7]. We have introduced two techniques to account for potential uncertainties during inverse treatment planning: Probabilistic treatment planning interprets the dose distribution as a random variable [5, 6, 7] and worst case optimization incorporates a physically impossible worst case dose scenario into the conventional optimization [8]. In its original implementation, our robust treatment planning system for particle therapy was only designed for the optimization of physical dose but it has lately been extended to a robust biological optimization tool. We have shown that – using these techniques – it is possible to establish treatment plans which are robust regarding potential uncertainties guaranteing both successful treatment and patient safety.

In our opinion, robust treatment planning is essential to close the gap between the computer-aided treatment planning process and the actual delivered treatment. In the future we want to promote its role as a cornerstone of modern treatment planning by

  • identifying the indications which would benefit most from robust planning in the clinic,
  • investigating current margin concepts for intensity-modulated radiation therapy,
  • developing strategies for robust beam angle selection.

Recently, we have presented a robust method for 4D treatment planning which accounts for uncertainties in respiratory motion for photon therapy [9].

4D Treatment planning

Advanced particle therapy facilities such as HIT, PSI [10], and GSI [11] apply active treatment beam delivery techniques, i.e. online energy variation and lateral scanning of the hadron beam. For static targets such a 3D spot-scanning technique [10,12] provides highly conformal dose deposition to the tumor. However, for targets under the influence of organ motion, interference of the beam and target motion can lead to distortions of the planned dose distribution, also known as interplay effects.

We are studying those interplay effects for scanned proton beam delivery to moving targets. We have developed a computer based tool to simulate the delivery of scanning proton beam delivery to a moving tumor. Motion mitigation techniques, such as gating, i.e. the tumor is only irradiated in a pre-defined motion window, and rescanning, i.e. scanning the tumor volume several times within one fraction of dose delivery, are investigated to improve the treatment of moving targets in particle therapy.

Figure 3: Schematic view of active scanning. The particle beam is laterally and vertically deflected by two magnets. In depth, the target is divided into isoenergy slices. As the tumor moves up and down while the beam is deflected the tumor motion interferes with the beam motion which leads to a distorted dose distribution. In the figure hot and cold spots are indicated by black and white dots.
© dkfz.de

Treatment planning for fast-cycling hadron linacs

Another possibility to reduce the motion caused distortions in the dose distribution in cancer treatment of moving tumors is provided by the development of innovative hadron accelerators and beam delivery techniques. Hadron beams accelerated by fast-cycling linacs are superior to those accelerated by synchrotrons because the energy can be adjusted electronically with every pulse of the linear accelerator. In collaboration with the TERA foundation at CERN we are studying the design of a new single room facility consisting of a high-gradient linac mounted on a gantry which rotates around the patient.

People involved

  • Mark Bangert, Postdoc
  • Kim Kraus, PhD student

Collaborations

Selected publications

[1] Wilkens JJ; Oelfke U: A phenomenological model for the relative biological effectiveness in therapeutic proton beams. Phys. Med. Biol., 49(13) (2004) 2811-2825

[2] Wilkens JJ; Oelfke U: Fast multifield optimization of the biological effect in ion therapy. Phys. Med. Biol., 51 (2006) 3127-3140.

[3] Wilkens JJ; Oelfke U: Direct comparison of biologically optimized spread-out Bragg peaks for protons and carbon ions. Int. J. Rad. Onc. Biol. Phys., 70(1) (2008) 262-266.

[4] Frese M; Wilkens JJ; Huber PE; Jensen AD; Oelfke U; Taheri-Kadkhoda Z: Application of Constant vs. Variable Relative Biological Effectiveness in Treatment Planning of Intensity-Modulated Proton Therapy. Int J Radiat Oncol Biol Phys. In press

[5] J. Unkelbach & U. Oelfke: Inclusion of organ movements in IMRT treatment planning via inverse planning based on probability distributions. Phys. Med. Biol., 49 (2004).

[6] J. Unkelbach & U. Oelfke: Incorporating organ movements in inverse planning: assessing dose uncertainties by Bayesian inference. Phys. Med. Biol., 50 (2005).

[7] J. Unkelbach & U. Oelfke: Incorporating organ movements in IMRT treatment planning for prostate cancer: Minimizing uncertainties in the inverse planning process. Med. Phys., 32 (2005).

[8] D. Pflugfelder, J. J. Wilkens & U. Oelfke: Worst Case optimization: a method to account for uncertainties in the optimization of intensity modulated proton therapy. Phys. Med. Biol., 53 (2008).

[9] E. Heath, J. Unkelbach & U. Oelfke: Incorporating uncertainties in respiratory motion into 4D treatment plan optimization. Med. Phys., 36 (2009).

[10] E. Pedroni et al.: The 200-MeV proton therapy project at the Paul Scherrer Institute: conceptual design and practical realization. Med. Phys., 22(1) (1995) 37-53.

[11] Th. Haberer et al.: Magnetic scanning system for heavy ion therapy. Nucl. Instrum. Meth. A, Vol. 330 (1-2) (1993) 296-305.

[12] A. Lomax: Intensity modulation methods for proton radiotherapy. Phys. Med. Biol., 44(1) (1999) 185-205.

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