Cristian Cotrutz

A Study of the Accuracy of CyberKnife Spinal Radiosurgery Using Skeletal Structure Tracking

OBJECTIVE New technology has enabled the increasing use of radiosurgery to ablate spinal lesions. The first generation of the CyberKnife (Accuray, Inc., Sunnyvale, CA) image-guided radiosurgery system required implanted radiopaque markers (fiducials) to localize spinal targets. A recently developed and now commercially available spine tracking technology called Xsight (Accuray, Inc.) tracks skeletal structures and eliminates the need for implanted fiducials. The Xsight system localizes spinal targets by direct reference to the adjacent vertebral elements. This study sought to measure the accuracy of Xsight spine tracking and provide a qualitative assessment of overall system performance. METHODS Total system error, which is defined as the distance between the centroids of the planned and delivered dose distributions and represents all possible treatment planning and delivery errors, was measured using a realistic, anthropomorphic head-and-neck phantom. The Xsight tracking system error component of total system error was also computed by retrospectively analyzing image data obtained from eleven patients with a total of 44 implanted fiducials who underwent CyberKnife spinal radiosurgery. RESULTS The total system error of the Xsight targeting technology was measured to be 0.61 mm. The tracking system error component was found to be 0.49 mm. CONCLUSION The Xsight spine tracking system is practically important because it is accurate and eliminates the use of implanted fiducials. Experience has shown this technology to be robust under a wide range of clinical circumstances.

Segment-based dose optimization using a genetic algorithm

Intensity modulated radiation therapy (IMRT) inverse planning is conventionally done in two steps. Firstly, the intensity maps of the treatment beams are optimized using a dose optimization algorithm. Each of them is then decomposed into a number of segments using a leaf-sequencing algorithm for delivery. An alternative approach is to pre-assign a fixed number of field apertures and optimize directly the shapes and weights of the apertures. While the latter approach has the advantage of eliminating the leaf-sequencing step, the optimization of aperture shapes is less straightforward than that of beamlet-based optimization because of the complex dependence of the dose on the field shapes, and their weights. In this work we report a genetic algorithm for segment-based optimization. Different from a gradient iterative approach or simulated annealing, the algorithm finds the optimum solution from a population of candidate plans. In this technique, each solution is encoded using three chromosomes: one for the position of the left-bank leaves of each segment, the second for the position of the right-bank and the third for the weights of the segments defined by the first two chromosomes. The convergence towards the optimum is realized by crossover and mutation operators that ensure proper exchange of information between the three chromosomes of all the solutions in the population. The algorithm is applied to a phantom and a prostate case and the results are compared with those obtained using beamlet-based optimization. The main conclusion drawn from this study is that the genetic optimization of segment shapes and weights can produce highly conformal dose distribution. In addition, our study also confirms previous findings that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization. Thus the technique may have useful applications in facilitating IMRT treatment planning.

Inverse planning for functional image-guided intensity-modulated radiation therapy

Radiation therapy is an image-guided process whose success critically depends on the imaging modality used for treatment planning and the level of integration of the available imaging information. In this work, we establish a dose optimization framework for incorporating metabolic information from functional imaging modalities into the intensity-modulated radiation therapy (IMRT) inverse planning process and to demonstrate the technical feasibility of planning deliberately non-uniform dose distributions in accordance with functional imaging data. For this purpose, a metabolic map from functional images is discretized into a number of abnormality levels (ALs) and then fused with CT images. To escalate dose to the metabolically abnormal regions, we assume, for a given spatial point, a linear relation between the AL and the prescribed dose. But the formalism developed here is independent of the assumption and any other relation between AL and prescription is applicable. For a given AL and prescription relation, it is only necessary to prescribe the dose to the lowest AL in the target and the desired doses to other regions with higher AL values are scaled accordingly. To accomplish differential sparing of a sensitive structure when its functional importance (FI) distribution is known, we individualize the tolerance doses of the voxels within the structure according to their Fl levels. An iterative inverse planning algorithm in voxel domain is used to optimize the system with in homogeneous dose prescription. To model intra-structural trade-off, a mechanism is introduced through the use of voxel-dependent weighting factors, in addition to the conventional structure specific weighting factors which model the inter-structural trade-off. The system is used to plan a phantom case with a few hypothetical functional distributions and a brain tumour treatment with incorporation of magnetic resonance spectroscopic imaging data. The results indicated that it is technically feasible to produce deliberately non-uniform dose distributions according to the functional imaging requirements. Integration of functional imaging information into radiation therapy dose optimization allows for consideration of patient-specific biologic information and provides a significant opportunity to truly individualize radiation treatment. This should enhance our capability to safely and intelligently escalate dose and lays the technical foundation for future clinical studies of the efficacy of functional imaging-guided IMRT.

Using voxel-dependent importance factors for interactive DVH-based dose optimization

Intensity modulated radiation therapy (IMRT) inverse planning is usually performed by pre-selecting parameters such as beam modality, beam configuration and importance factors and then optimizing the fluence profiles or beamlet weights. In reality, the IMRT dose optimization problem may be ill-conditioned and there may not be a physical solution to account for the chosen parameters and constraints. Planner intervention is often required to conduct a multiple trial-and-error process where several parameters are sequentially varied until an acceptable compromise is achieved. The resulting solution reflects a balance between the conflicting requirements of the target and the sensitive structures. A major problem of the conventional inverse planning formalism is that there exists no effective mechanism for a planner to fine-tune the dose distribution on a local level or to differentially modify the dose-volume histograms (DVHs) of the involved structures. In this paper we introduce a new inverse planning scheme with voxel-dependent importance factors and demonstrate that it provides us with an effective link between the system parameters and the dosimetric behaviour at a local level. The planning proceeds in two steps. After a conventional trial-and-error inverse planning procedure is completed, we identify the dose interval at which the fractional volume on the DVH curve needs to be changed. The voxels that receive dose in the selected range are then located and their voxel-dependent importance factors are adjusted accordingly. The fine-tuning of the DVHs is iterative in nature and, using widely available computer graphic software tools, the process can be made graphically interactive. The new IMRT planning scheme is applied to two test cases and the results indicate that our control over the differential shapes of the DVHs of the involved structures is,greatly enhanced. Thus the technique may have significant practical implications in facilitating the IMRT treatment planning process.

Prostate Cancer Therapy with Stereotactic Body Radiation Therapy

The purpose of this work is to provide background and current directions of image guidance for localized prostate cancer treatments. We will describe the external beam hypofractionation protocol for localized prostate cancer currently in progress at Stanford University and the biological bases for large fractions in an abbreviated treatment course for prostate cancer. The need for image guidance in external beam prostate cancer treatments will be discussed. Our experience with two imageguided implementations of hypofractionated radiotherapy for localized prostate cancer will be presented. These are the Cyberknife System (Accuray, Inc.) and the Trilogy System (Varian Medical Systems, Inc.).

Quantitation of the a priori dosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution space

In inverse planning, the likelihood for the points in a target or sensitive structure to meet their dosimetric goals is generally heterogeneous and represents the a priori knowledge of the system once the patient and beam configuration are chosen. Because of this intrinsic heterogeneity, in some extreme cases, a region in a target may never meet the prescribed dose without seriously deteriorating the doses in other areas. Conversely, the prescription in a region may be easily met without violating the tolerance of any sensitive structure. In this work, we introduce the concept of dosimetric capability to quantify the a priori information and develop a strategy to integrate the data into the inverse planning process. An iterative algorithm is implemented to numerically compute the capability distribution on a case specific basis. A method of incorporating the capability data into inverse planning is developed by heuristically modulating the importance of the individual voxels according to the a priori capability distribution. The formalism is applied to a few specific examples to illustrate the technical details of the new inverse planning technique. Our study indicates that the dosimetric capability is a useful concept to better understand the complex inverse planning problem and an effective use of the information allows us to construct a clinically more meaningful objective function to improve IMRT dose optimization techniques.

Therapeutic treatment plan optimization with probability density-based dose prescription

The dose optimization in inverse planning is realized under the guidance of an objective function. The prescription doses in a conventional approach are usually rigid values, defining in most instances an ill-conditioned optimization problem. In this work, we propose a more general dose optimization scheme based on a statistical formalism [Xing et al., Med. Phys. 21, 2348-2358 (1999)]. Instead of a rigid dose, the prescription to a structure is specified by a preference function, which describes the users preference over other doses in case the most desired dose is not attainable. The variation range of the prescription dose and the shape of the preference function are predesigned by the user based on prior clinical experience. Consequently, during the iterative optimization process, the prescription dose is allowed to deviate, with a certain preference level, from the most desired dose. By not restricting the prescription dose to a fixed value, the optimization problem becomes less ill-defined. The conventional inverse planning algorithm represents a special case of the new formalism. An iterative dose optimization algorithm is used to optimize the system. The performance of the proposed technique is systematically studied using a hypothetical C-shaped tumor with an abutting circular critical structure and a prostate case. It is shown that the final dose distribution can be manipulated flexibly by tuning the shape of the preference function and that using a preference function can lead to optimized dose distributions in accordance with the planners specification. The proposed framework offers an effective mechanism to formalize the planners priorities over different possible clinical scenarios and incorporate them into dose optimization. The enhanced control over the final plan may greatly facilitate the IMRT treatment planning process.

Single fraction stereotactic radiosurgery (SFSR) for lung tumors - A phase I dose escalation trial

7231 Background: Although stereotactic radiosurgery is well established for the treatment of intracranial neoplasms, its use for lung tumors is novel. We report preliminary results of SFSR in patients with inoperable lung tumors in a phase I study. METHODS Eligible patients included those with inoperable early stage (T1-2N0) non-small cell lung cancers (NSCLC) or solitary lung metastases. Treatments were delivered via a linear accelerator mounted on a computer-control robotic arm (Cyberknife, Accuray, CA). Before treatment, all patients underwent CT-guided metallic fiducial placement in the tumor for image guided targeting, which when combined with breath-holding ensures accurate tumor targeting. All radiation treatments were delivered in a single fraction. 9 to 10 patients were treated per dose cohort starting at 15 Gy/fraction followed by dose escalation of 5-10 Gy/fraction. A minimal 3 month period is required between each dose level for monitoring toxicity. RESULTS Nineteen patients (11 NSCLC and 8 metastatic tumors) were enrolled to date. The median age was 74 (range: 22-83). Tumor size ranged from 1.5 to 5 cm in maximal dimension. Nine patients received 15 Gy and 10 received 25 Gy. There were 2 complications related to fiducial placement - 1 pneumothorax requiring chest tube insertion and 1 emphysema exacerbation. One patient with prior thoracic irradiation for a separate esophageal cancer developed grade 3 radiation pneumonitis at 3 months post treatment. She subsequently died at 6 months post treatment with an enlarging pleural effusion in the face of both radiation-pneumonitis and tumor progression. For the remaining patients, there was no obvious clinical decline in pulmonary function. 17 patients were evaluable for radiographic response, which was scored as stable in 1, minor in 6, partial in 6 and complete in 4. 3 complete responders received 25 Gy. At a median follow up of 8 months (range 1-32), 4 have relapsed locally, all in the 15 Gy dose group. CONCLUSIONS SFSR is feasible and well tolerated for the treatment of selected lung tumors. Additional dose escalation is underway to determine the optimal radiation dose and overall treatment efficacy. No significant financial relationships to disclose.

Quality Assurance of the Cyberknife Fiducial and Skull Tracking Systems

Routine quality assurance (QA) should be performed for any radiosurgery system. Given the complexity of the Cyberknife, QA plays an even more important function. Below we review the suggested daily