Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments.

PURPOSE: To develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT). METHODS: Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (req_PTV ) and the equivalent radius of volume that receives a set of prescription dose (req_Vi , where Vi  = V10% , V20% … V120% ). To evaluate the model's predictability, a fourfold cross-validation was performed for dosimetric indices such as gradient measure (GM) and brain V50% . The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., ΔGM = GMpred - GMclin and fractional ΔV50%  = (V50%pred - V50%clin )/V50%clin ) and a coefficient of determination, R2 . Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency. RESULTS: Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 ± 0.06 mm for ΔGM, and 0.12 ± 0.08 for ΔV50% . The R2 for the linear fit between req_PTV and req_vi was 0.985 ± 0.019 for isodose volumes ranging from V10% to V120% ; particularly, R2  = 0.995 for V50% and R2  = 0.997 for V100% . Compared to the training plans, our KBP test plan nCI was improved from 1.31 ± 0.15 to 1.15 ± 0.08 (P < 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner's intervention. CONCLUSION: We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency.

A model for predicting the dose to the parotid glands based on their relative overlapping with planning target volumes during helical radiotherapy.

The sparing of the parotid glands in the treatment of head and neck cancers is of clinical relevance as high doses to the salivary glands may result in xerostomia. Xerostomia is a major cause of decreased quality of life for head and neck patients. This paper explores the relationship between the overlap of the target volumes and their expansions with the parotid glands for helical delivery plans and their ability to be spared. Various overlapping volumes were examined, and an overlap with a high statistical relevance was found. A model that predicts exceeding tolerance parotid mean dose based on its fractional overlapping volume with PTVs was developed. A fractional overlapping volume of 0.083 between the parotid gland and the high dose PTV plus 5 mm expansion - was determined to be the threshold value to predict parotid Dmean  > 26 Gy for parotids that overlap with the high dose PTV plus 5 mm expansion. If the parotid gland only overlaps with the intermediate dose target (and/or low dose target) and the overlapping volume of the parotid gland and the intermediate dose target is less than 25%, the parotid mean dose is likely less than 26 Gy. If the parotid overlaps with the low dose target only then the mean dose to the parotid is likely to be less than 26 Gy. This finding will prove as a very useful guide for the physicians and planners involved in the planning process to know prior whether the parotid glands will be able to be spared with the current set of target volumes or if revisions are necessary. This work will serve as a helpful guide in the planning process of head and neck target cases.

The step-and-shoot IMRT overshooting phenomenon: a novel method to mitigate patient overdosage.

The goal of this work is to evaluate the dosimetric impact of an overshooting phenomenon in step-and-shoot IMRT delivery, and to demonstrate a novel method to mitigate the issue. Five pelvis IMRT patients treated on Varian 2100C EX linacs with larger than +4.5% phantom ion chamber point-dose difference relative to planned dose were investigated. For each patient plan, 5 fractions were delivered. DynaLog files were recorded and centi-MU pulses from dose integrator board for every control point (CP) were counted using a commercial pulse counter. The counter recorded CP MU agrees with DynaLog records, both showing an ~ 0.6MU overshoot of the first segment of every beam. The 3D patient dose was recalculated from the counter records and compared to the planned dose, showing that the overshoot resulted in on average 2.05% of PTV D95 error, and 2.49%, 2.61% and 2.45% of D1cc error for rectum, bladder, and bowel, respectively. The initial plans were then modified by inserting a specially designed MLC segment to the start of every beam. The modified plans were also delivered five times. The dose from the modified delivery was calculated using counter recorded CP MU. The corresponding Dx parameters were all within 0.31% from the original plan. IMRT QA results also show a 2.2% improvement in ion chamber point-dose agreement. The results demonstrate that the proposed plan modification method effectively eliminates the overdosage from the overshooting phenomenon.

Dosimetric comparison of Acuros XB with collapsed cone convolution/superposition and anisotropic analytic algorithm for stereotactic ablative radiotherapy of thoracic spinal metastases.

The aim of this study is to compare the recent Eclipse Acuros XB (AXB) dose calculation engine with the Pinnacle collapsed cone convolution/superposition (CCC) dose calculation algorithm and the Eclipse anisotropic analytic algorithm (AAA) for stereotactic ablative radiotherapy (SAbR) treatment planning of thoracic spinal (T-spine) metastases using IMRT and VMAT delivery techniques. The three commissioned dose engines (CCC, AAA, and AXB) were validated with ion chamber and EBT2 film measurements utilizing a heterogeneous slab-geometry water phantom and an anthropomorphic phantom. Step-and-shoot IMRT and VMAT treatment plans were developed and optimized for eight patients in Pinnacle, following our institutional SAbR protocol for spinal metastases. The CCC algorithm, with heterogeneity corrections, was used for dose calculations. These plans were then exported to Eclipse and recalculated using the AAA and AXB dose calculation algorithms. Various dosimetric parameters calculated with CCC and AAA were compared to that of the AXB calculations. In regions receiving above 50% of prescription dose, the calculated CCC mean dose is 3.1%-4.1% higher than that of AXB calculations for IMRT plans and 2.8%-3.5% higher for VMAT plans, while the calculated AAA mean dose is 1.5%-2.4% lower for IMRT and 1.2%-1.6% lower for VMAT. Statistically significant differences (p < 0.05) were observed for most GTV and PTV indices between the CCC and AXB calculations for IMRT and VMAT, while differences between the AAA and AXB calculations were not statistically significant. For T-spine SAbR treatment planning, the CCC calculations give a statistically significant overestimation of target dose compared to AXB. AAA underestimates target dose with no statistical significance compared to AXB. Further study is needed to determine the clinical impact of these findings.

Per-beam, planar IMRT QA passing rates do not predict clinically relevant patient dose errors.

PURPOSE: The purpose of this work is to determine the statistical correlation between per-beam, planar IMRT QA passing rates and several clinically relevant, anatomy-based dose errors for per-patient IMRT QA. The intent is to assess the predictive power of a common conventional IMRT QA performance metric, the Gamma passing rate per beam. METHODS: Ninety-six unique data sets were created by inducing four types of dose errors in 24 clinical head and neck IMRT plans, each planned with 6 MV Varian 120-leaf MLC linear accelerators using a commercial treatment planning system and step-and-shoot delivery. The error-free beams/plans were used as "simulated measurements" (for generating the IMRT QA dose planes and the anatomy dose metrics) to compare to the corresponding data calculated by the error-induced plans. The degree of the induced errors was tuned to mimic IMRT QA passing rates that are commonly achieved using conventional methods. RESULTS: Analysis of clinical metrics (parotid mean doses, spinal cord max and D1cc, CTV D95, and larynx mean) vs. IMRT QA Gamma analysis (3%/3 mm, 2/2, 1/1) showed that in all cases, there were only weak to moderate correlations (range of Pearson's r-values: -0.295 to 0.653). Moreover, the moderate correlations actually had positive Pearson's r-values (i.e., clinically relevant metric differences increased with increasing IMRT QA passing rate), indicating that some of the largest anatomy-based dose differences occurred in the cases of high IMRT QA passing rates, which may be called "false negatives." The results also show numerous instances of false positives or cases where low IMRT QA passing rates do not imply large errors in anatomy dose metrics. In none of the cases was there correlation consistent with high predictive power of planar IMRT passing rates, i.e., in none of the cases did high IMRT QA Gamma passing rates predict low errors in anatomy dose metrics or vice versa. CONCLUSIONS: There is a lack of correlation between conventional IMRT QA performance metrics (Gamma passing rates) and dose errors in anatomic regions-of-interest. The most common acceptance criteria and published actions levels therefore have insufficient, or at least unproven, predictive power for per-patient IMRT QA.

Moving from gamma passing rates to patient DVH-based QA metrics in pretreatment dose QA.

PURPOSE: The purpose of this work is to explore the usefulness of the gamma passing rate metric for per-patient, pretreatment dose QA and to validate a novel patient-dose∕DVH-based method and its accuracy and correlation. Specifically, correlations between: (1) gamma passing rates for three 3D dosimeter detector geometries vs clinically relevant patient DVH-based metrics; (2) Gamma passing rates of whole patient dose grids vs DVH-based metrics, (3) gamma passing rates filtered by region of interest (ROI) vs DVH-based metrics, and (4) the capability of a novel software algorithm that estimates corrected patient Dose-DVH based on conventional phantom QA data are analyzed. METHODS: Ninety six unique "imperfect" step-and-shoot IMRT plans were generated by applying four different types of errors on 24 clinical Head∕Neck patients. The 3D patient doses as well as the dose to a cylindrical QA phantom were then recalculated using an error-free beam model to serve as a simulated measurement for comparison. Resulting deviations to the planned vs simulated measured DVH-based metrics were generated, as were gamma passing rates for a variety of difference∕distance criteria covering: dose-in-phantom comparisons and dose-in-patient comparisons, with the in-patient results calculated both over the whole grid and per-ROI volume. Finally, patient dose and DVH were predicted using the conventional per-beam planar data as input into a commercial "planned dose perturbation" (PDP) algorithm, and the results of these predicted DVH-based metrics were compared to the known values. RESULTS: A range of weak to moderate correlations were found between clinically relevant patient DVH metrics (CTV-D95, parotid D(mean), spinal cord D1cc, and larynx D(mean)) and both 3D detector and 3D patient gamma passing rate (3%∕3 mm, 2%∕2 mm) for dose-in-phantom along with dose-in-patient for both whole patient volume and filtered per-ROI. There was considerable scatter in the gamma passing rate vs DVH-based metric curves. However, for the same input data, the PDP estimates were in agreement with actual patient DVH results. CONCLUSIONS: Gamma passing rate, even if calculated based on patient dose grids, has generally weak correlation to critical patient DVH errors. However, the PDP algorithm was shown to accurately predict the DVH impact using conventional planar QA results. Using patient-DVH-based metrics IMRT QA allows per-patient dose QA to be based on metrics that are both sensitive and specific. Further studies are now required to analyze new processes and action levels associated with DVH-based metrics to ensure effectiveness and practicality in the clinical setting.

Dosimetric effects of saline- versus water-filled balloon applicators for IORT using the model S700 electronic brachytherapy source.

PURPOSE: The Xoft Axxent Electronic Brachytherapy System (Xoft, Inc., San Jose, CA) is a viable option for intraoperative radiation therapy (IORT) treatment of early-stage breast cancer. The low-energy (50-kVp) X-ray source simplifies shielding and increases relative biological effectiveness but increases dose distribution sensitivity to medium composition. Treatment planning systems typically assume homogenous water for brachytherapy dose calculations, including precalculated atlas plans for Xoft IORT. However, Xoft recommends saline for balloon applicator filling. This study investigates dosimetric differences due to increased effective atomic number (Zeff) for saline (Zeff = 7.56) versus water (Zeff = 7.42). METHODS: Balloon applicator diameters range from 3 to 6 cm. Monte Carlo N-Particle software is used to calculate dose at the surface (Ds) of and 1 cm away (D1cm) from the water-/saline-filled balloon applicator using a single dwell at the applicator center as a simple estimation of the dosimetry and multiple dwells simulating the clinical dose distributions for the atlas plans. RESULTS: Single-dwell plans show a 4.4-6.1% decrease in Ds for the 3- to 6-cm diameter applicators due to the saline. Multidwell plans show similar results: 4.9% and 6.4% Ds decrease, for 4-cm and 6-cm diameter applicators, respectively. For the single-dwell plans, D1cm decreases 3.6-5.2% for the 3- to 6-cm diameter applicators. For the multidwell plans, D1cm decreases 3.3% and 5.3% for the 4-cm and 6-cm applicators, respectively. CONCLUSIONS: The dosimetric effect introduced by saline versus water filling for Xoft balloon applicator-based IORT treatments is ∼5%. Users should be aware of this in the context of both treatment planning and patient outcome studies.

Automated Extraction of Dose/Volume Statistics for Radiotherapy-Treatment-Plan Evaluation in Clinical-Trial Quality Assurance.

Radiotherapy clinical-trial quality assurance is a crucial yet challenging process. This note presents a tool that automatically extracts dose/volume statistics for determining dosimetry compliance review with improved efficiency and accuracy. A major objective of this study is to develop an automated solution for clinical-trial radiotherapy dosimetry review.

On the use of biomathematical models in patient-specific IMRT dose QA.

PURPOSE: To investigate the use of biomathematical models such as tumor control probability (TCP) and normal tissue complication probability (NTCP) as new quality assurance (QA) metrics. METHODS: Five different types of error (MLC transmission, MLC penumbra, MLC tongue and groove, machine output, and MLC position) were intentionally induced to 40 clinical intensity modulated radiation therapy (IMRT) patient plans (20 H&N cases and 20 prostate cases) to simulate both treatment planning system errors and machine delivery errors in the IMRT QA process. The changes in TCP and NTCP for eight different anatomic structures (H&N: CTV, GTV, both parotids, spinal cord, larynx; prostate: CTV, rectal wall) were calculated as the new QA metrics to quantify the clinical impact on patients. The correlation between the change in TCP∕NTCP and the change in selected DVH values was also evaluated. The relation between TCP∕NTCP change and the characteristics of the TCP∕NTCP curves is discussed. RESULTS: ΔTCP and ΔNTCP were summarized for each type of induced error and each structure. The changes/degradations in TCP and NTCP caused by the errors vary widely depending on dose patterns unique to each plan, and are good indicators of each plan's "robustness" to that type of error. CONCLUSIONS: In this in silico QA study the authors have demonstrated the possibility of using biomathematical models not only as patient-specific QA metrics but also as objective indicators that quantify, pretreatment, a plan's robustness with respect to possible error types.