Evaluation of clinical margins via simulation of patient setup errors in prostate IMRT treatment plans

Assessment of robustness of institutional applied clinical target volume (CTV) to planning target volume (PTV) margin in cervical cancer using biological models

The aim of this study is to investigate the robustness of our institutionally applied clinical target volume (CTV)-to-planning target volume (PTV) margins in cervical cancer patients in terms of an equivalent uniform dose (EUD) based on tumor control probability (TCP). We simulated target motion using 25 IMRT cervical cancer plans to demonstrate the effect of geometrical uncertainties on the EUD and TCP. The different components of the total geometrical uncertainties budget were estimated. The biological effects were compared by calculating the EUDs from the trial DVHs. The impact of geometric uncertainties was calculated as a percentage of the difference between 〖EUD〗_static and 〖EUD〗_motion, where the 〖EUD〗_static is the EUD calculated from the target DVHs and 〖EUD〗_motion is averaged, over a 1000 calculated EUDs for each of the analyzed IMRT treatment plans. The multivariate nonlinear regression was used to find the predicted difference between the static and motion EUD. The estimate of the systematic and random motion errors were Σ_(total(SI,LR,AP)) (mm)=(2.6; 2.5; 1.8) and σ_(total(SI,LR,AP)) (mm)=(3.4; 1.4; 3.4). For average 〈EUD〉_motion=44.3 Gy (over 25 patients) we have found a TCP decrease of about 1%, %(ΔTCP)≈1% for predefined PTV margin. According to the calculated EUD motion-distributions, for particular patients, the CTV does receive the prescribed EUD of 45 Gy. The predicted difference in EUD showed that our isotropic margin of 10 mm is large enough to absorb geometric uncertainties and ensure dose coverage of the moving CTV in the cervical cancer patients.

A study of nonuniform CTV to PTV margin expansion incorporating both rotational and translational uncertainties

PURPOSE

In this work, we implemented a method to obtain a nonuniform clinical target volume (CTV) to planning target volume (PTV) margin caused by both rotational and translational uncertainties and evaluated it in the treatment planning system (TPS).

MATERIALS AND METHOD

Based on a previously published statistical model, the relationship between a target margin and the distance d (from isocenter to target point), setup uncertainties, and significance level was established. For a single CTV, it can be thought as a combination of many small volume elements or target points. The margin of each point could be obtained using the suggested statistical model. The whole nonuniform CTV-PTV margin was determined by the union of all possible margins of the CTV boundary points. This method was implemented in the Pinnacle³ treatment planning system and compared with uniform margin algorithm. Ten vertebral metastases targets and multiple brain metastases targets were chosen for evaluation.

RESULTS

The combined CTV-PTV margin as a function of d for various initial translational margin and rotational uncertainties was calculated. The combined margin increases as d, rotational uncertainties and translational margin increase. For the same rotational uncertainty, a smaller initial translational margin requires a larger rotational margin to compensate for the rotational error. Compared with the uniform margin algorithm, the advantage of this method is that it could minimize the PTVs volume for given CTVs to obtain same significance level. Using vertebral metastases targets and multiple brain metastases targets, a series of volume difference was obtained for various translational margins and rotational uncertainties. The volume difference of PTV could be more than 17% when translational margin is 2 mm and rotational uncertainty is 1.4°.

CONCLUSION

Nonuniform margin algorithm could avoid excessive compensation for the CTV boundary points near isocenter. This method could be used for clinical margin determination and might be useful for the protection of risk organs.

Calculation of delivered composite dose from Calypso tracking data for prostate cancer: And subsequent evaluation of reasonable treatment interruption tolerance limits

PURPOSE

In this study we calculate composite dose delivered to the prostate by using the Calypso tracking -data- stream acquired during patient treatment in our clinic. We evaluate the composite distributions under multiple simulated Calypso tolerance level schemes and then recommend a tolerance level.

MATERIALS AND METHODS

Seven Calypso-localized prostate cancer patients treated in our clinic were selected for retrospective analysis. Two different IMRT treatment plans, with prostate PTV margins of 5 and 3 mm respectively, were computed for each patient. A delivered composite dose distribution was computed from Calypso tracking data for each plan. Additionally, we explored the dosimetric implications for "worst case" scenarios by assuming that the prostate position was located at one of the eight extreme corners of a 3 or 5 mm "box." To characterize plan quality under each of the studied scenarios, we recorded the maximum, mean, and minimum doses and volumetric coverage for prostate, PTV, bladder, and rectum.

RESULTS AND DISCUSSIONS

Calculated composite dose distributions were very similar to the original plan for all patients. The difference in maximum, mean, and minimum doses as well as volumetric coverage for the prostate, PTV, bladder, and rectum were all < 4.0% of prescription dose. Even for worst scenario cases, the results show acceptable isodose distribution, with the exception for the combination of a 3 mm PTV margin with a 5 mm position tolerance scheme.

CONCLUSIONS

Calculated composite dose distributions show that the vast majority of dosimetric metrics agreed well with the planned dose (within 2%). With significant/detrimental deviations from the planned dose only occurring with the combination of a 3 mm PTV margin and 5 mm position tolerance, the 3 mm position tolerance strategy appears reasonable, confirming that further reducing prostate PTV margins to 3 mm is possible when using Calypso with a position tolerance of 3 mm.

Clinical adequacy assessment of autocontours for prostate IMRT with meaningful endpoints

PURPOSE

To determine if radiation treatment plans created based on autosegmented (AS) regions-of-interest (ROI)s are clinically equivalent to plans created based on manually segmented ROIs, where equivalence is evaluated using probabilistic dosimetric metrics and probabilistic biological endpoints for prostate IMRT.

METHOD AND MATERIALS

Manually drawn contours and autosegmented ROIs were created for 167 CT image sets acquired from 19 prostate patients. Autosegmentation was performed utilizing Pinnacle's Smart Probabilistic Image Contouring Engine. For each CT set, 78 Gy/39 fraction 7-beam IMRT treatment plans with 1 cm CTV-to-PTV margins were created for each of the three contour scenarios; PMD using manually delineated (MD) ROIs, PAS using autosegmented ROIs, and PAM using autosegmented organ-at-risks (OAR)s and the manually drawn target. For each plan, 1000 virtual treatment simulations with different systematic errors for each simulation and a different random error for each fraction were performed. The statistical probability of achieving dose-volume metrics (coverage probability (CP)), expectation values for normal tissue complication probability (NTCP), and tumor control probability (TCP) metrics for all possible cross-evaluation pairs of ROI types and planning scenarios were reported. In evaluation scenarios, the root mean square loss (RMSL) and maximum absolute loss (MAL) of coverage probability of dose-volume objectives, E[TCP], and E[NTCP] were compared with respect to the base plan created and evaluated with manually drawn contours.

RESULTS

Femoral head dose objectives were satisfied in all situations, as well as the maximum dose objectives for all ROIs. Bladder metrics were within the clinical coverage tolerances except D35Gy for the autosegmented plan evaluated with the manual contours. Dosimetric indices for CTV and rectum could be highly compromised when the definition of the ROIs switched from manually delineated to autosegmented. Seventy-two percent of CT image sets satisfied the worst-case CP thresholds for all dosimetric objectives in all scenarios, the percentage dropped to 50% if biological indices were taken into account. Among evaluation scenarios, (MD,PAM ) bore the highest resemblance to (MD,PMD ) where 99% and 88% of cases met all CP thresholds for bladder and rectum, respectively.

CONCLUSIONS

When including daily setup variations in prostate IMRT, the dose-volume metric CP, and biological indices of ROIs were approximately equivalent for the plans created based on manually drawn targets and autosegmented OARs in 88% of cases. The accuracy of autosegmented prostates and rectums are impediment to attain statistically equivalent plans created based on manually drawn ROIs.

Head and Neck Margin Reduction With Adaptive Radiation Therapy: Robustness of Treatment Plans Against Anatomy Changes

PURPOSE

We set out to investigate loss of target coverage from anatomy changes in head and neck cancer patients as a function of applied safety margins and to verify a cone beam computed tomography (CBCT)-based adaptive strategy with an average patient anatomy to overcome possible target underdosage.

METHODS AND MATERIALS

For 19 oropharyngeal cancer patients, volumetric modulated arc therapy treatment plans (2 arcs; simultaneous integrated boost, 70 and 54.25 Gy; 35 fractions) were automatically optimized with uniform clinical target volume (CTV)-to-planning target volume margins of 5, 3, and 0 mm. We applied b-spline CBCT-to-computed tomography (CT) deformable registration to allow recalculation of the dose on modified CT scans (planning CT deformed to daily CBCT following online positioning) and dose accumulation in the planning CT scan. Patients with deviations in primary or elective CTV coverage >2 Gy were identified as candidates for adaptive replanning. For these patients, a single adaptive intervention was simulated with an average anatomy from the first 10 fractions.

RESULTS

Margin reduction from 5 mm to 3 mm to 0 mm generally led to an organ-at-risk (OAR) mean dose (Dmean) sparing of approximately 1 Gy/mm. CTV shrinkage was mainly seen in the elective volumes (up to 10%), likely related to weight loss. Despite online repositioning, substantial systematic errors were present (>3 mm) in lymph node CTV, the parotid glands, and the larynx. Nevertheless, the average increase in OAR dose was small: maximum of 1.2 Gy (parotid glands, Dmean) for all applied margins. Loss of CTV coverage >2 Gy was found in 1, 3, and 7 of 73 CTVs, respectively. Adaptive intervention in 0-mm plans substantially improved coverage: in 5 of 7 CTVs (in 6 patients) to <2 Gy of initially planned.

CONCLUSIONS

Volumetric modulated arc therapy head and neck cancer treatment plans with 5-mm margins are robust for anatomy changes and show a modest increase in OAR dose. Margin reduction improves OAR sparing with approximately 1 Gy/mm at the expense of target coverage in a subgroup of patients. Patients at risk of CTV underdosage >2 Gy in 0-mm plans may be identified early in treatment using dose accumulation. A single intervention with an average anatomy derived from CBCT effectively mitigates discrepancies.

Coverage-based treatment planning to accommodate delineation uncertainties in prostate cancer treatment

PURPOSE

To compare two coverage-based planning (CP) techniques with fixed margin-based (FM) planning for high-risk prostate cancer treatments, with the exclusive consideration of the dosimetric impact of delineation uncertainties of target structures and normal tissues.

METHODS

In this work, 19-patient data sets were involved. To estimate structure dose for each delineated contour under the influence of interobserver contour variability and CT image quality limitations, 1000 alternative structures were simulated by an average-surface-of-standard-deviation model, which utilized the patient-specific information of delineated structure and CT image contrast. An IMRT plan with zero planning-target-volume (PTV) margin on the delineated prostate and seminal vesicles [clinical-target-volume (CTV prostate) and CTVSV] was created and dose degradation due to contour variability was quantified by the dosimetric consequences of 1000 alternative structures. When D98 failed to achieve a 95% coverage probability objective D98,95 ≥ 78 Gy (CTV prostate) or D98,95 ≥ 66 Gy (CTVSV), replanning was performed using three planning techniques: (1) FM (PTV prostate margin = 4,5,6 mm and PTVSV margin = 4,5,7 mm for RL, PA, and SI directions, respectively), (2) CPOM which optimized uniform PTV margins for CTV prostate and CTVSV to meet the D98,95 objectives, and (3) CPCOP which directly optimized coverage-based objectives for all the structures. These plans were intercompared by computing percentile dose-volume histograms and tumor-control probability/normal tissue complication probability (TCP/NTCP) distributions.

RESULTS

Inherent contour variability resulted in unacceptable CTV coverage for the zero-PTV-margin plans for all patients. For plans designed to accommodate contour variability, 18/19 CP plans were most favored by achieving desirable D98,95 and TCP/NTCP values. The average improvement of probability of complication free control was 9.3% for CPCOP plans and 3.4% for CPOM plans.

CONCLUSIONS

When the delineation uncertainties need to be considered for prostate patients, CP techniques can produce more desirable plans than FM plans for most patients. The relative advantages between CPCOP and CPOM techniques are patient specific.

A method of surface marker location optimization for tumor motion estimation in lung stereotactic body radiation therapy

PURPOSE

Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient's body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparse optimization approach (SOA) model.

METHODS

The principle-component-analysis (PCA) method was employed as the framework to build the authors' statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the "best represented" columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4 DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution.

RESULTS

The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4 DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4 DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms.

CONCLUSIONS

The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.

Image Guidance-Based Target Volume Margin Expansion in IMRT of Head and Neck Cancer

This study quantifies the setup uncertainties to optimize the planning target volume (PTV) margin based on daily image guidance, its dosimetric impact, and radiobiological implication for intensity-modulated radiation therapy (IMRT) in head and neck cancer. Ten patients were retrospectively chosen who had been treated with IMRT and with daily image-guided radiation therapy (IGRT). The daily setup errors of the 10 patients from on-board imaging for the entire treatment were analyzed. Planning target volumes were generated by expanding the clinical target volumes (CTVs) with 0 to 10 mm margins. The IMRT plans with the same dose-volume constraints were created in an Eclipse treatment planning system. The effect of volume expansion was analyzed with biological indices such as tumor control probability, normal tissue complication probability (NTCP), and equivalent uniform dose. Analysis of 906 daily setup corrections using daily IGRT showed that 98% of the daily setups are within ± 5 mm. The relative increase in PTV-CTV volume from 0 to 10 mm margins provides nearly 4-fold volume increase and is linearly related to monitor unit (MU). The increase in MU is about 5%/mm margin increase. The relative increase in NTCP of parotids from 5 to 10 mm margins is 3.2 ± 1.15. Increase in PTV margin increases extra tissue volume with a corresponding increase in MU for treatment and NTCP values. Even a small margin increase (eg, 1 mm) may result in increase of more than 20% in relative extra volume and 15% in NTCP value of organs at risk (OARs). With image guidance, the setup uncertainty could be achieved within ± 5 mm for 98% of the treatments, and a margin <5 mm for PTV may seem desirable to reduce the extra tissue irradiated, but at the expense of a more demanding setup accuracy.

Patient specific respiratory motion modeling using a 3D patient's external surface

PURPOSE

Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal.

METHODS

Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient.

RESULTS

Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only.

CONCLUSIONS

The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.

Comparisons of treatment optimization directly incorporating systematic patient setup uncertainty with a margin-based approach

PURPOSE

To develop a probabilistic treatment planning (PTP) method which is robust to systematic patient setup errors and to compare PTP plans with plans generated using a planning target volume (PTV) margin optimized to give the same target coverage probability as the PTP plan.

METHODS

Plans adhering to the RTOG-0126 protocol are developed for 28 prostate patients using PTP and margin-based planning. For PTP, an objective function that simultaneously considers multiple possible patient positions is developed. PTP plans are optimized using clinical target volume (CTV) structures and organ at risk (OAR) structures. The desired CTV coverage probability is 95%. Plans that cannot achieve a 95% CTV coverage probability are re-optimized with a desired CTV coverage probability reduced by 5% until the desired CTV coverage probability is achieved. Margin-based plans are created which achieve the same CTV coverage probability as the PTP plans by iterative adjustment of the CTV-to-PTV margin. Postoptimization, probabilistic dose-volume coverage metrics are used to compare the plans.

RESULTS

For equivalent target coverage probability, PTP plans significantly reduce coverage probability for rectum objectives (-17% for D(35) < 65 Gy, p = 0.0010; -23% for D(25) < 70 Gy, p < 0.0001; and -27% for D(15) < 75 Gy, p < 0.0001). Physician assessment indicates PTP plans are entirely preferred 71% of the time while margin-based plans are entirely preferred 7% of the time.

CONCLUSIONS

For plans having the same target coverage probability, PTP has potential to reduce rectal doses while maintaining CTV coverage probability. In blind comparisons, physicians prefer PTP plans over optimized margin plans.

Sensitivity of postplanning target and OAR coverage estimates to dosimetric margin distribution sampling parameters

PURPOSE

A dosimetric margin (DM) is the margin in a specified direction between a structure and a specified isodose surface, corresponding to a prescription or tolerance dose. The dosimetric margin distribution (DMD) is the distribution of DMs over all directions. Given a geometric uncertainty model, representing inter- or intrafraction setup uncertainties or internal organ motion, the DMD can be used to calculate coverage Q, which is the probability that a realized target or organ-at-risk (OAR) dose metric D, exceeds the corresponding prescription or tolerance dose. Postplanning coverage evaluation quantifies the percentage of uncertainties for which target and OAR structures meet their intended dose constraints. The goal of the present work is to evaluate coverage probabilities for 28 prostate treatment plans to determine DMD sampling parameters that ensure adequate accuracy for postplanning coverage estimates.

METHODS

Normally distributed interfraction setup uncertainties were applied to 28 plans for localized prostate cancer, with prescribed dose of 79.2 Gy and 10 mm clinical target volume to planning target volume (CTV-to-PTV) margins. Using angular or isotropic sampling techniques, dosimetric margins were determined for the CTV, bladder and rectum, assuming shift invariance of the dose distribution. For angular sampling, DMDs were sampled at fixed angular intervals w (e.g., w = 1 degree, 2 degrees, 5 degrees, 10 degrees, 20 degrees). Isotropic samples were uniformly distributed on the unit sphere resulting in variable angular increments, but were calculated for the same number of sampling directions as angular DMDs, and accordingly characterized by the effective angular increment omega eff. In each direction, the DM was calculated by moving the structure in radial steps of size delta (=0.1, 0.2, 0.5, 1 mm) until the specified isodose was crossed. Coverage estimation accuracy deltaQ was quantified as a function of the sampling parameters omega or omega eff and delta.

RESULTS

The accuracy of coverage estimates depends on angular and radial DMD sampling parameters omega or omega eff and delta, as well as the employed sampling technique. Target deltaQ/ < l% and OAR /deltaQ/ < 3% can be achieved with sampling parameters omega or omega eef = 20 degrees, delta =1 mm. Better accuracy (target /deltaQ < 0.5% and OAR /deltaQ < approximately 1%) can be achieved with omega or omega eff = 10 degrees, delta = 0.5 mm. As the number of sampling points decreases, the isotropic sampling method maintains better accuracy than fixed angular sampling.

CONCLUSIONS

Coverage estimates for post-planning evaluation are essential since coverage values of targets and OARs often differ from the values implied by the static margin-based plans. Finer sampling of the DMD enables more accurate assessment of the effect of geometric uncertainties on coverage estimates prior to treatment. DMD sampling with omega or omega eff = 10 degrees and delta = 0.5 mm should be adequate for planning purposes.

Schedule for CT image guidance in treating prostate cancer with helical tomotherapy

The aim of this study was to determine the effect of reducing the number of image guidance sessions and patient-specific target margins on the dose distribution in the treatment of prostate cancer with helical tomotherapy. 20 patients with prostate cancer who were treated with helical tomotherapy using daily megavoltage CT (MVCT) imaging before treatment served as the study population. The average geometric shifts applied for set-up corrections, as a result of co-registration of MVCT and planning kilovoltage CT studies over an increasing number of image guidance sessions, were determined. Simulation of the consequences of various imaging scenarios on the dose distribution was performed for two patients with different patterns of interfraction changes in anatomy. Our analysis of the daily set-up correction shifts for 20 prostate cancer patients suggests that the use of four fractions would result in a population average shift that was within 1 mm of the average obtained from the data accumulated over all daily MVCT sessions. Simulation of a scenario in which imaging sessions are performed at a reduced frequency and the planning target volume margin is adapted provided significantly better sparing of organs at risk, with acceptable reproducibility of dose delivery to the clinical target volume. Our results indicate that four MVCT sessions on helical tomotherapy are sufficient to provide information for the creation of personalised target margins and the establishment of the new reference position that accounts for the systematic error. This simplified approach reduces overall treatment session time and decreases the imaging dose to the patient.

Coverage-based treatment planning: optimizing the IMRT PTV to meet a CTV coverage criterion

This work demonstrates an iterative approach-referred to as coverage-based treatment planning-designed to produce treatment plans that ensure target coverage for a specified percentage of setup errors. In this approach the clinical target volume to planning target volume (CTV-to-PTV) margin is iteratively adjusted until the specified CTV coverage is achieved. The advantage of this approach is that it automatically compensates for the dosimetric margin around the CTV, i.e., the extra margin that is created when the dose distribution extends beyond the PTV. When applied to 27 prostate plans, this approach reduced the average CTV-to-PTV margin from 5 to 2.8 mm. This reduction in PTV size produced a corresponding decrease in the volume of normal tissue receiving high dose. The total volume of tissue receiving > or =65 Gy was reduced on average by 19.3% or about 48 cc. Individual reductions varied from 8.7% to 28.6%. The volume of bladder receiving > or =60 Gy was reduced on average by 5.6% (reductions for individuals varied from 1.7% to 10.6%), and the volume of periprostatic rectum receiving > or =65 Gy was reduced on average by 4.9% (reductions for individuals varied from 0.9% to 12.3%). The iterative method proposed here represents a step toward a probabilistic treatment planning algorithm which can generate dose distributions (i.e., treated volumes) that closely approximate a specified level of coverage in the presence of geometric uncertainties. The general principles of coverage-based treatment planning are applicable to arbitrary treatment sites and delivery techniques. Importantly, observed deviations between coverage implied by specified CTV-to-PTV margins and coverage achieved by a given treatment plan imply a generic need to perform coverage probability analysis on a per-plan basis to ensure that the desired level of coverage is achieved.

A deliverable four-dimensional intensity-modulated radiation therapy-planning method for dynamic multileaf collimator tumor tracking delivery

PURPOSE

To develop a deliverable four-dimensional (4D) intensity-modulated radiation therapy (IMRT) planning method for dynamic multileaf collimator (MLC) tumor tracking delivery.

METHODS AND MATERIALS

The deliverable 4D IMRT planning method involves aligning MLC leaf motion parallel to the major axis of target motion and translating MLC leaf positions by the difference in the target centroid position between respiratory phases of the 4D CT scan. This method ignores nonlinear respiratory motion and deformation. A three-dimensional (3D) optimal method whereby an IMRT plan on each respiratory phase of the 4D CT scan was independently optimized was used for comparison. For 12 lung cancer patient 4D CT scans, individual phase plans and deformable dose-summed 4D plans using the two methods were created and compared.

RESULTS

For each of the individual phase plans, the deliverable method yielded similar isodose distributions and dose-volume histograms. The deliverable and 3D optimal methods yielded statistically equivalent dose-volume metrics for both individual phase plans and 4D plans (p > 0.05 for all metrics compared). The deliverable method was affected by 4D CT artifacts in one case. Both methods were affected by high vector field variations from deformable registration.

CONCLUSIONS

The deliverable method yielded similar dose distributions for each of the individual phase plans and statistically equivalent dosimetric values compared with the 3D optimal method, indicating that the deliverable method is dosimetrically robust to the variations of fractional time spent in respiratory phases on a given 4D CT scan. Nonlinear target motion and deformation did not cause significant dose discrepancies.

Evaluation of dosimetric margins in prostate IMRT treatment plans

This work introduces a new concept--the dosimetric margin distribution (DMD)--and uses it to explain the sensitivity of a group of prostate IMRT treatment plans to patient setup errors. Prior work simulated the effect of setup errors on 27 prostate IMRT treatment plans and found the plans could tolerate larger setup errors than predicted by the van Herk margin formula. The conjectured reason for this disagreement was a breakdown in van Herk's assumption that the planned dose distribution conforms perfectly to target structures. To resolve the disagreement, this work employed the same 27 plans to evaluate the actual margin distributions that exist between: (i) the clinical target volume (CTV) and planning target volume (PTV) and (ii) the CTV and PTV minimum dose isodose surface. These distributions were evaluated for both prostate and nodal targets. Distribution (ii) is the DMD. The dosimetric margin in a given direction determines the probability that the CTV will be underdosed due to setup errors in that direction. Averaging over 4 pi sr gives the overall probability of CTV coverage. Minimum doses for prostate and nodal PTVs were obtained from dose volume histograms. Corresponding isodose surfaces were created and converted to regions of interest (ROIs). CTV, PTV, and isodose ROIs were saved as mesh files and then imported into a computational geometry application which calculated distances between meshes (i.e., margins) in 614 discrete directions covering 4 pi sr in 10 deg increments. Measured prostate CTV-to-PTV margins were close to the nominal value of 0.5 cm specified in the treatment planning protocol. However, depending on direction, prostate dosimetric margins ranged from 0.5 to 3 cm, reflecting the imperfect conformance of the planned dose distribution to the prostate PTV. For the nodal CTV, the nominal CTV-to-PTV margin employed in treatment planning was again 0.5 cm. However, due to the planning protocol, the nodal PTV follows the surface of the nodal CTV in several places, ensuring that there is no room for rigid body motion of the nodal CTV inside the nodal PTV. Measured nodal CTV-to-PTV margins were therefore zero, while nodal dosimetric margins ranged from 0.2 to 2.8 cm. Prostate and nodal target coverage were found to be well correlated with the measured DMDs, thereby resolving the apparent disagreement with our prior results. The principal conclusion is that target coverage in the presence of setup errors should be evaluated using the DMD, rather than the CTV-to-PTV margin distribution. The DMD is a useful planning metric, which generalizes the ICRU conformity index. DMDs could vary with number of beams, beam arrangements, TPS, and treatment site.

The effect of statistical noise on IMRT plan quality and convergence for MC-based and MC-correction-based optimized treatment plans

Monte Carlo (MC) is rarely used for IMRT plan optimization outside of research centres due to the extensive computational resources or long computation times required to complete the process. Time can be reduced by degrading the statistical precision of the MC dose calculation used within the optimization loop. However, this eventually introduces optimization convergence errors (OCEs). This study determines the statistical noise levels tolerated during MC-IMRT optimization under the condition that the optimized plan has OCEs <100 cGy (1.5% of the prescription dose) for MC-optimized IMRT treatment plans.Seven-field prostate IMRT treatment plans for 10 prostate patients are used in this study. Pre-optimization is performed for deliverable beams with a pencil-beam (PB) dose algorithm. Further deliverable-based optimization proceeds using: (1) MC-based optimization, where dose is recomputed with MC after each intensity update or (2) a once-corrected (OC) MC-hybrid optimization, where a MC dose computation defines beam-by-beam dose correction matrices that are used during a PB-based optimization. Optimizations are performed with nominal per beam MC statistical precisions of 2, 5, 8, 10, 15, and 20%. Following optimizer convergence, beams are re-computed with MC using 2% per beam nominal statistical precision and the 2 PTV and 10 OAR dose indices used in the optimization objective function are tallied. For both the MC-optimization and OC-optimization methods, statistical equivalence tests found that OCEs are less than 1.5% of the prescription dose for plans optimized with nominal statistical uncertainties of up to 10% per beam. The achieved statistical uncertainty in the patient for the 10% per beam simulations from the combination of the 7 beams is ~3% with respect to maximum dose for voxels with D>0.5D(max). The MC dose computation time for the OC-optimization is only 6.2 minutes on a single 3 Ghz processor with results clinically equivalent to high precision MC computations.

Performance of a hybrid MC dose algorithm for IMRT optimization dose evaluation

This paper presents a hybrid intensity modulated radiation therapy (IMRT) optimization strategy which combines the speed of pencil beam (PB) and the accuracy of Monte Carlo (MC) dose calculations. After an initial deliverable-based optimization using a PB algorithm, doses are recomputed using the VMC++ MC code to determine dose correction factors, which are then utilized during further PB-based optimization. The hybrid method is benchmarked with respect to full MC deliverable-based optimization for ten prostate and ten head-and-neck IMRT plans. Final optimized plans are compared in terms of dose-volume indices used for the plan optimization. Dose prediction errors (DPEs) and optimization convergence errors (OCEs) at intermediate steps of the hybrid sequence are evaluated. The hybrid method is found to produce optimized plans that are clinically equivalent to full MC-based optimization, yet requires only 40% of the number of MC dose calculations. With the hybrid strategy presented here, MC-based optimization results are achieved in 35 min or less on a modest computing cluster. While the initial PB-deliverable-based optimization is found to have DPEs and OCEs of up to 3 Gy relative to the 65-73 Gy prescription doses, application of the first MC correction reduces the average DPEs to less than 0.3 Gy for the prostate plans and less than 0.06 Gy for the head and neck plans. The maximum observed DPE or OCE is 0.7 Gy after 1 MC dose correction, indicating that a single MC dose calculation correction might be sufficient for IMRT optimization.