Largely reduced OAR doses, and planning and delivery times for challenging robotic SBRT cases, obtained with a novel optimizer.

Recently, VOLO™ was introduced as a new optimizer for CyberKnife® planning. In this study, we investigated possibilities to improve treatment plans for MLC-based prostate SBRT with enhanced peripheral zone dose while sparing the urethra, and central lung tumors, compared to existing Sequential Optimization (SO). The primary focus was on reducing OAR doses. For 25 prostate and 25 lung patients treated with SO plans, replanning with VOLO™ was performed with the same planning constraints. For equal PTV coverage, almost all OAR plan parameters were improved with VOLO™. For prostate patients, mean rectum and bladder doses were reduced by 34.2% (P < 0.001) and 23.5% (P < 0.001), with reductions in D0.03cc of 3.9%, 11.0% and 3.1% for rectum, mucosa and bladder (all P ≤ 0.01). Urethra D5% and D10% were 3.8% and 3.0% lower (P ≤ 0.002). For lung patients, esophagus, main bronchus, trachea, and spinal cord D0.03cc was reduced by 18.9%, 11.1%, 16.1%, and 13.2%, respectively (all P ≤ 0.01). Apart from the dosimetric advantages of VOLO™ planning, average reductions in MU, numbers of beams and nodes for prostate/lung were 48.7/32.8%, 26.5/7.9% and 13.4/7.9%, respectively (P ≤ 0.003). VOLO™ also resulted in reduced delivery times with mean/max reductions of: 27/43% (prostate) and 15/41% (lung), P  < 0.001. Planning times reduced from 6 h to 1.1 h and from 3 h to 1.7 h for prostate and lung, respectively. The new VOLO™ planning was highly superior to SO planning in terms of dosimetric plan quality, and planning and delivery times.

Implementation of a dose gradient method into optimization of dose distribution in prostate cancer 3D-CRT plans.

AIM: The aim of this work is to present a method of beam weight and wedge angle optimization for patients with prostate cancer. BACKGROUND: 3D-CRT is usually realized with forward planning based on a trial and error method. Several authors have published a few methods of beam weight optimization applicable to the 3D-CRT. Still, none on these methods is in common use. MATERIALS AND METHODS: Optimization is based on the assumption that the best plan is achieved if dose gradient at ICRU point is equal to zero. Our optimization algorithm requires beam quality index, depth of maximum dose, profiles of wedged fields and maximum dose to femoral heads. The method was tested for 10 patients with prostate cancer, treated with the 3-field technique. Optimized plans were compared with plans prepared by 12 experienced planners. Dose standard deviation in target volume, and minimum and maximum doses were analyzed. RESULTS: The quality of plans obtained with the proposed optimization algorithms was comparable to that prepared by experienced planners. Mean difference in target dose standard deviation was 0.1% in favor of the plans prepared by planners for optimization of beam weights and wedge angles. Introducing a correction factor for patient body outline for dose gradient at ICRU point improved dose distribution homogeneity. On average, a 0.1% lower standard deviation was achieved with the optimization algorithm. No significant difference in mean dose-volume histogram for the rectum was observed. CONCLUSIONS: Optimization shortens very much time planning. The average planning time was 5 min and less than a minute for forward and computer optimization, respectively.

Tumor tracking with non-linear internal/external correlation models in the presence of respiratory motion baseline drifts and phase shifts.

PURPOSE: In CyberKnife® respiratory tracking, tumor positions are predicted from external marker positions using correlation models. With available models, prediction accuracy may deteriorate when respiratory motion baseline drifts occur. Previous investigations have demonstrated that for linear models this can be mitigated by adding a time-dependent term. In this study, we have focused on added value of time-dependent terms for the available non-linear correlation models, and on phase shifts between internal and external motion tracks. METHODS: Treatment simulations for tracking with and without time-dependent terms were performed using computer generated respiratory motion tracks for 60.000 patients with variable baseline drifts and phase shifts. The protocol for acquisition of X-ray images was always the same. Tumor position prediction accuracies in simulated treatments were largely based on cumulative error-time histograms and quantified with R95: in 95% of time the prediction error is < R95 mm. RESULTS: For all available correlation models, prediction accuracy improved by adding a time-dependent term in case of occurring baseline drifts, with and without phase shifts present. For the most accurate model and 150 s between model updates, adding time dependency reduced R95 from 3.9 to 3.1 mm and from 5.4 to 3.3 mm for 0.25 and 0.50 mm/min drift, respectively. Tumor position prediction accuracy improvements with time-dependent models were obtained without increases in X-ray imaging. CONCLUSIONS: Using available correlation models with an added time-dependent term could largely mitigate negative impact of respiratory motion baseline drifts on tumor position prediction accuracy, also in case of large phase shifts.

A novel external/internal tumor tracking approach to compensate for respiratory motion baseline drifts.

Objective.Real-time respiratory tumor tracking as implemented in a robotic treatment unit is based on continuous optical measurement of the position of external markers and a correlation model between them and internal target positions, which are established with X-ray imaging of the tumor, or fiducials placed in or around the tumor. Correlation models are created with fifteen simultaneously measured external/internal marker position pairs divided over the respiratory cycle. Every 45-150 s, the correlation model is updated by replacing the three first acquired data pairs with three new pairs. Tracking simulations for >120.000 computer-generated respiratory tracks demonstrated that this tracking approach resulted in relevant inaccuracies in internal target position predictions, especially in case of presence of respiratory motion baseline drifts.Approach.To better cope with drifts, we introduced a novel correlation model with an explicit time dependence, and we proposed to replace the currently applied linear-motion tracking (LMT) by mixed-model tracking (MMT). In MMT, the linear correlation model is extended with an explicit time dependence in case of a detected baseline drift. MMT prediction accuracies were then established for the same >120.000 computer-generated patients as used for LMT.Main results.For 150 s update intervals, MMT outperformed LMT in internal target position prediction accuracy for 93.7 ∣ 97.2% of patients with 0.25 ∣ 0.5 mm min-1linear respiratory motion baseline drifts with similar numbers of X-ray images and similar treatment times. For the upper 25% of patients, mean 3D internal target position prediction errors reduced by 0.7 ∣ 1.8 mm, while near maximum reductions (upper 10% of patients) were 0.9 ∣ 2.0 mm.Significance.For equal numbers of acquired X-ray images, MMT greatly improved tracking accuracy compared to LMT, especially in the presence of baseline drifts. Even with almost 50% less acquired X-ray images, MMT still outperformed LMT in internal target position prediction accuracy.

Fully automated treatment planning for MLC-based robotic radiotherapy.

PURPOSE: To propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCiseTM 2 multileaf collimator. METHODS: The AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil-beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in-house. AUTO MCO generates for each patient a single, high-quality Pareto-optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil-beams, segment dose depositions, and final dose calculations are performed with a stand-alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. RESULTS: AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (-49%)), segments (59.5/98.9 (-40%)), and delivery times (17.1/22.3 min. (-23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (-21%)) and had on average shorter delivery times (18.6/22.3 min. (-17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. CONCLUSIONS: A new, vendor-independent optimization workflow for fully automated generation of deliverable high-quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (-23%) due to large reductions in beam and segment numbers.

Coping with interfraction time trends in tumor setup.

PURPOSE: Interfraction tumor setup variations in radiotherapy are often reduced with image guidance procedures. Clinical target volume (CTV)-planning target volume (PTV) margins are then used to deal with residual errors. We have investigated characterization of setup errors in patient populations with explicit modelling of occurring interfraction time trends. METHODS: The core of a "trendline characterization" of observed setup errors in a population is a distribution of trendlines, each obtained by fitting a straight line through a patient's daily setup errors. Random errors are defined as daily deviations from the trendline. Monte Carlo simulations were performed to predict the impact of offline setup correction protocols on residual setup errors in patient populations with time trends. A novel CTV-PTV margin recipe was derived that assumes that systematic underdosing of tumor edges in multiple consecutive fractions, as caused by trend motion, should preferentially be avoided. Similar to the well-known approach by van Herk et al. for conventional error characterization (no explicit modelling of trends), only a predefined percentage of patients (generally 10%) was allowed to have nonrandom (systematic + trend) setup errors outside the margin. Additionally, a method was proposed to avoid erroneous results in Monte Carlo simulations with setup errors, related to decoupling of error sources in characterizations. The investigations were based on a database of daily measured setup errors in 835 prostate cancer patients that were treated with 39 fractions, and on Monte Carlo-generated patient populations with time trends. RESULTS: With conventional characterization of setup errors in patient populations with time trends, predicted standard deviations of residual systematic errors ( Σ res ) after application of an offline correction protocol could be underestimated by more than 50%, potentially resulting in application of too small margins. With the new trendline characterization this was avoided. With the novel CTV-PTV margin recipe with an allowed 10% of patients having nonrandom errors outside the margin, the observed percentage was 10.0% ± 0.2%. When using conventional characterization of errors and the van Herk margin recipe, on average 58.0% ± 24.3% of patients had errors outside the margin, while 10% was prescribed. For populations with no time trends, the novel recipe simplifies to the generally applied M = 2.5 Σ + 0.7 σ formula proposed by van Herk et al. CONCLUSIONS: In populations with time trends in setup errors, the use of trendline characterizations in Monte Carlo simulations for establishment of residual errors after a setup correction protocol can avoid application of erroneous margins. The novel margin recipe can be used to accurately control the percentage of patients with nonrandom errors outside the margin. In case of daily image guidance of patients with multiple targets with differential motion, the recipe can be used to establish margins for the targets that are not the primary target for the image guidance (e.g., nodal regions). Probabilistic planning might be improved by using trendline characterization for modelling of setup errors. Population analyses of interfraction setup errors need to take into account potential time trends.