Morbidity and mortality in hip surgery patients due to fracture during the COVID-19 pandemic.

BACKGROUND: The COVID-19 epidemic generated major changes in general surgical management protocols. The literature has reported high mortality rates for hip fracture surgery in patients with COVID-19. This study describes the morbidity and mortality in patients undergoing surgery due to hip fractures in 12 Colombian institutions between March and September 2020. METHODOLOGY: This was a retrospective observational descriptive study. Medical records of 12 hospitals were reviewed. Consecutive patients who underwent hip fracture surgery from March 6 to September 6, 2020, were included. Data collected were sociodemographic profile, type of fracture, surgical treatment, complications, and early (1 month) or mid-term (1-6 months) mortality associated or not with COVID-19. RESULTS: Five hundred twenty patients with hip fractures requiring surgery in the 12 institutions were included. 364 (70%) were women; mean age was 77.7 years (SD: 13.8), mean BMI was 25.1, 91.73% of patients had at least one comorbidity, 60.38% were classified as ASA II and 25.77% as ASA III. There were 267 (51.34%) pertrochanteric fractures, 227 (43.65%) femoral neck fractures, and 26 (5.0%) subtrochanteric fractures. 274 (52.69%) patients were treated with osteosynthesis, 244 (46.92%) with arthroplasty, and 2 (0.38%) with girdlestone. Surgery was performed less than 24 h after the fracture for 115 (22.11%) patients, between 24 and 72 h for 208 (40.0%) patients, and more than 72 h for 197 (37.88%) patients. One hundred six patients in total suffered a medical or surgical complication throughout the different follow-up stages, amongst the most frequent were respiratory failure, coronary events, surgical site infection, cutting-out and peri­implant fracture. 25 (4.8%) patients required attention in the Intensive Care Unit (ICU). 13 patients had COVID-19 throughout the follow-up period. 27 patients died due to any cause, and 3 of them had reported a positive COVID-19 test any time during follow-up period, of which one died during the first month, and two died between 1 and 6 months. Statistically significant associations were found between age older than 75 years old, ASA classification, ICU requirement, and death. CONCLUSION: 520 patients received surgical treatment for hip fracture during the first six months of the COVID-19 pandemic in 12 medical centers in Colombia. 21.10% suffered a complication during the early stage (30 days) and 4.77% during the midterm stage (1-6 months). 4.8% were admitted in the ICU during the early stage. All-cause death was 27 patients, early death was 11 (40.74%) and midterm death was 16 (59.25%). 13 patients were positive for COVID-19, 3 died, one (1/5=20%) on the first 30 days and the other two (2/8=25%) from month 1 to 6.

Intensity modulated proton arc therapy via geometry-based energy selection for ependymoma.

PURPOSE: We developed and tested a novel method of creating intensity modulated proton arc therapy (IMPAT) plans that uses computing resources similar to those for regular intensity-modulated proton therapy (IMPT) plans and may offer a dosimetric benefit for patients with ependymoma or similar tumor geometries. METHODS: Our IMPAT planning method consists of a geometry-based energy selection step with major scanning spot contributions as inputs computed using ray-tracing and single-Gaussian approximation of lateral spot profiles. Based on the geometric relation of scanning spots and dose voxels, our energy selection module selects a minimum set of energy layers at each gantry angle such that each target voxel is covered by sufficient scanning spots as specified by the planner, with dose contributions above the specified threshold. Finally, IMPAT plans are generated by robustly optimizing scanning spots of the selected energy layers using a commercial proton treatment planning system (TPS). The IMPAT plan quality was assessed for four ependymoma patients. Reference three-field IMPT plans were created with similar planning objective functions and compared with the IMPAT plans. RESULTS: In all plans, the prescribed dose covered 95% of the clinical target volume (CTV) while maintaining similar maximum doses for the brainstem. While IMPAT and IMPT achieved comparable plan robustness, the IMPAT plans achieved better homogeneity and conformity than the IMPT plans. The IMPAT plans also exhibited higher relative biological effectiveness (RBE) enhancement than did the corresponding reference IMPT plans for the CTV in all four patients and brainstem in three of them. CONCLUSIONS: The proposed method demonstrated potential as an efficient technique for IMPAT planning and may offer a dosimetric benefit for patients with ependymoma or tumors in close proximity to critical organs. IMPAT plans created using this method had elevated RBE enhancement associated with increased linear energy transfer (LET) in both targets and abutting critical organs.

An empirical model of proton RBE based on the linear correlation between x-ray and proton radiosensitivity.

BACKGROUND: Proton relative biological effectiveness (RBE) is known to depend on physical factors of the proton beam, such as its linear energy transfer (LET), as well as on cell-line specific biological factors, such as their ability to repair DNA damage. However, in a clinical setting, proton RBE is still considered to have a fixed value of 1.1 despite the existence of several empirical models that can predict proton RBE based on how a cell's survival curve (linear-quadratic model [LQM]) parameters α and ß vary with the LET of the proton beam. Part of the hesitation to incorporate variable RBE models in the clinic is due to the great noise in the biological datasets on which these models are trained, often making it unclear which model, if any, provides sufficiently accurate RBE predictions to warrant a departure from RBE = 1.1. PURPOSE: Here, we introduce a novel model of proton RBE based on how a cell's intrinsic radiosensitivity varies with LET, rather than its LQM parameters. METHODS AND MATERIALS: We performed clonogenic cell survival assays for eight cell lines exposed to 6 MV x-rays and 1.2, 2.6, or 9.9 keV/µm protons, and combined our measurements with published survival data (n = 397 total cell line/LET combinations). We characterized how radiosensitivity metrics of the form DSF% , (the dose required to achieve survival fraction [SF], e.g., D10% ) varied with proton LET, and calculated the Bayesian information criteria associated with different LET-dependent functions to determine which functions best described the underlying trends. This allowed us to construct a six-parameter model that predicts cells' proton survival curves based on the LET dependence of their radiosensitivity, rather than the LET dependence of the LQM parameters themselves. We compared the accuracy of our model to previously established empirical proton RBE models, and implemented our model within a clinical treatment plan evaluation workflow to demonstrate its feasibility in a clinical setting. RESULTS: Our analyses of the trends in the data show that DSF% is linearly correlated between x-rays and protons, regardless of the choice of the survival level (e.g., D10% , D37% , or D50% are similarly correlated), and that the slope and intercept of these correlations vary with proton LET. The model we constructed based on these trends predicts proton RBE within 15%-30% at the 68.3% confidence level and offers a more accurate general description of the experimental data than previously published empirical models. In the context of a clinical treatment plan, our model generally predicted higher RBE-weighted doses than the other empirical models, with RBE-weighted doses in the distal portion of the field being up to 50.7% higher than the planned RBE-weighted doses (RBE = 1.1) to the tumor. CONCLUSIONS: We established a new empirical proton RBE model that is more accurate than previous empirical models, and that predicts much higher RBE values in the distal edge of clinical proton beams.

Optimization of FLASH proton beams using a track-repeating algorithm.

BACKGROUND: Radiation with high dose rate (FLASH) has shown to reduce toxicities to normal tissues around the target and maintain tumor control with the same amount of dose compared to conventional radiation. This phenomenon has been widely studied in electron therapy, which is often used for shallow tumor treatment. Proton therapy is considered a more suitable treatment modality for deep-seated tumors. The feasibility of FLASH proton therapy has recently been demonstrated by a series of pre- and clinical trials. One of the challenges is to efficiently generate wide enough dose distributions in both lateral and longitudinal directions to cover the entire tumor volume. The goal of this paper is to introduce a set of automatic FLASH proton beam optimization algorithms developed recently. PURPOSE: To develop a fast and efficient optimizer for the design of a passive scattering proton FLASH radiotherapy delivery at The University of Texas MD Anderson Proton Therapy Center, based on the fast dose calculator (FDC). METHODS: A track-repeating algorithm, FDC, was validated versus Geant4 simulations and applied to calculate dose distributions in various beamline setups. The design of the components was optimized to deliver homogeneous fields with well-defined diameters between 11.0 and 20.5 mm, as well as a spread-out Bragg peak (SOBP) with modulations between 8.5 and 39.0 mm. A ridge filter, a high-Z material scatterer, and a collimator with range compensator were inserted in the beam path, and their shapes and sizes were optimized to spread out the Bragg peak, widen the beam, and reduce the penumbra. The optimizer was developed and tested using two proton energies (87.0 and 159.5 MeV) in a variety of beamline arrangements. Dose rates of the optimized beams were estimated by scaling their doses to those of unmodified beams. RESULTS: The optimized 87.0-MeV beams, with a distance from the beam pipe window to the phantom surface (window-to-surface distance [WSD]) of 550 mm, produced an 8.5-mm-wide SOBP (proximal 90% to distal 90% of the maximum dose); 14.5, 12.0, and 11.0-mm lateral widths at the 50%, 80%, and 90% dose location, respectively; and a 2.5-mm penumbra from 80% to 20% in the lateral profile. The 159.5-MeV beam had an SOBP of 39.0 mm and lateral widths of 20.5, 15.0, and 12.5 mm at 50%, 80%, and 90% dose location, respectively, when the WSD was 550 mm. Wider lateral widths were obtained with increased WSD. The SOBP modulations changed when the ridge filters with different characteristics were inserted. Dose rates on the beam central axis for all optimized beams (other than the 87.0-MeV beam with 2000-mm WSD) were above that needed for the FLASH effect threshold (40 Gy/s) except at the very end of the depth dose profile scaling with a dose rate of 1400 Gy/s at the Bragg peak in the unmodified beams. The optimizer was able to instantly design the individual beamline components for each of the beamline setups, without the need of time intensive iterative simulations. CONCLUSION: An efficient system, consisting of an optimizer and an FDC have been developed and validated in a variety of beamline setups, comprising two proton energies, several WSDs, and SOBPs. The set of automatic optimization algorithms produces beam shaping element designs efficiently and with excellent quality.

Impact of RBE variations on risk estimates of temporal lobe necrosis in patients treated with intensity-modulated proton therapy for head and neck cancer.

BACKGROUND: Temporal lobe necrosis (TLN) is a potential late effect after radiotherapy for skull base head and neck cancer (HNC). Several photon-derived dose constraints and normal tissue complication probability (NTCP) models have been proposed, however variation in relative biological effectiveness (RBE) may challenge the applicability of these dose constraints and models in proton therapy. The purpose of this study was therefore to investigate the influence of RBE variations on risk estimates of TLN after Intensity-Modulated Proton Therapy for HNC. MATERIAL AND METHODS: Seventy-five temporal lobes from 45 previously treated patients were included in the analysis. Sixteen temporal lobes had radiation associated Magnetic Resonance image changes (TLIC) suspected to be early signs of TLN. Fixed (RWDFix) and variable RBE-weighed doses (RWDVar) were calculated using RBE = 1.1 and two RBE models, respectively. RWDFix and RWDVar for temporal lobes were compared using Friedman's test. Based on RWDFix, six NTCP models were fitted and internally validated through bootstrapping. Estimated probabilities from RWDFix and RWDVar were compared using paired Wilcoxon test. Seven dose constraints were evaluated separately for RWDFix and RWDVar by calculating the observed proportion of TLIC in temporal lobes meeting the specific dose constraints. RESULTS: RWDVar were significantly higher than RWDFix (p < 0.01). NTCP model performance was good (AUC:0.79-0.84). The median difference in estimated probability between RWDFix and RWDVar ranged between 5.3% and 20.0% points (p < 0.01), with V60GyRBE and DMax at the smallest and largest differences, respectively. The proportion of TLIC was higher for RWDFix (4.0%-13.1%) versus RWDVar (1.3%-5.3%). For V65GyRBE ≤ 0.03 cc the proportion of TLIC was less than 5% for both RWDFix and RWDVar. CONCLUSION: NTCP estimates were significantly influenced by RBE variations. Dmax as model predictor resulted in the largest deviations in risk estimates between RWDFix and RWDVar. V65GyRBE ≤ 0.03 cc was the most consistent dose constraint for RWDFix and RWDVar.

Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer.

PURPOSE: Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT. METHODS AND MATERIALS: For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve. RESULTS: An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively. CONCLUSIONS: Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration.

The Reality of Randomized Controlled Trials for Assessing the Benefit of Proton Therapy: Critically Examining the Intent-to-Treat Principle in the Presence of Insurance Denial.

PURPOSE: This study hypothesized that insurance denial would lead to bias and loss of statistical power when evaluating the results from an intent-to-treat (ITT), per-protocol, and as-treated analyses using a simulated randomized clinical trial comparing proton therapy to intensity modulated radiation therapy where patients incurred increasing rates of insurance denial. METHODS AND MATERIALS: Simulations used a binary endpoint to assess differences between treatment arms after applying ITT, per-protocol, and as-treated analyses. Two scenarios were developed: 1 with clinical success independent of age and another assuming dependence on age. Insurance denial was assumed possible for patients <65 years. All scenarios considered an age distribution with mean ± standard deviation: 55 ± 15 years, rates of insurance denial ranging from 0%-40%, and a sample of N = 300 patients (150 per arm). Clinical success rates were defined as 70% for proton therapy and 50% for intensity modulated radiation therapy. The average treatment effect, bias, and power were compared after applying 5000 simulations. RESULTS: Increasing rates of insurance denial demonstrated inherent weaknesses among all 3 analytical approaches. With clinical success independent of age, a per-protocol analysis demonstrated the least bias and loss of power. When clinical success was dependent on age, the per-protocol and ITT analyses resulted in a similar trend with respect to bias and loss of power, with both outperforming the as-treated analysis. CONCLUSIONS: Insurance denial leads to misclassification bias in the ITT analysis, a missing data problem in the per-protocol analysis, and covariate imbalance between treatment arms in the as-treated analysis. Moreover, insurance denial forces the critical appraisal of patient features (eg, age) affected by the denial and potentially influencing clinical success. In the presence of insurance denial, our study suggests cautious reporting of ITT and as-treated analyses, and placing primary emphasis on the results of the per-protocol analysis.

Radiation-Induced Hypothyroidism After Radical Intensity Modulated Radiation Therapy for Oropharyngeal Carcinoma.

PURPOSE: To evaluate 2 published normal tissue complication probability models for radiation-induced hypothyroidism (RHT) on a large cohort of oropharyngeal carcinoma (OPC) patients who were treated with intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: OPC patients treated with retrievable IMRT Digital Imaging and Communications in Medicine (DICOMs) data and available baseline and follow-up thyroid function tests were included. Mean dose (Dmean) to the thyroid gland (TG) and its volume were calculated. The study outcome was clinical HT at least 6 months after radiation therapy, which was defined as grade ≥2 HT per Common Terminology Criteria for Adverse Events grading system (symptomatic hypothyroidism that required thyroid replacement therapy). Regression analyses and Wilcoxon rank-sum test were used. Receiver operating characteristic curves and area under the curve for the fitted model were calculated. RESULTS: In the study, 360 OPC patients were included. The median age was 58 years. Most tumors (51%) originated from the base of tongue. IMRT-split field was used in 95%, and median radiation therapy dose was 69.96 Gy. In the study, 233 patients (65%) developed clinical RHT that required thyroid replacement therapy. On multivariate analysis higher Dmean and smaller TG volume maintained the statistically significant association with the risk of clinical RHT (P < .0001). Dmean was significantly higher in patients with clinical RHT versus those without (50 vs 42 Gy, P < .0001). Patients with RHT had smaller TG volume compared with those without (11.8 compared with 12.8 mL, P < .0001). AUC of 0.72 and 0.66 were identified for fitted model versus for the applied Boomsma et al and Cella et al models, respectively. CONCLUSIONS: Volume and Dmean of the TG are important predictors of clinical RHT and shall be integrated into normal tissue complication probability models for RHT. Dmean and thyroid volume should be considered during the IMRT plan optimization in OPC patients.

Automatic phase space generation for Monte Carlo calculations of intensity modulated particle therapy.

Monte Carlo (MC) is generally considered as the most accurate dose calculation tool for particle therapy. However, a proper description of the beam particle kinematics is a necessary input for a realistic simulation. Such a description can be stored in phase space (PS) files for different beam energies. A PS file contains kinetic information such as energies, positions and travelling directions for particles traversing a plane perpendicular to the beam direction. The accuracy of PS files plays a critical role in the performance of the MC method for dose calculations. A PS file can be generated with a set of parameters describing analytically the beam kinematics. However, determining such parameters can be tedious and time consuming. Thus, we have developed an algorithm to obtain those parameters automatically and efficiently. In this paper, we presented such an algorithm and compared dose calculations using PS automatically generated for the Shanghai Proton and Heavy Ion Center (SPHIC) with measurements. The gamma-index for comparing calculated depth dose distributions (DDD) with measurements are above 96.0% with criterion 0.6%/0.6 mm. For each single energy, the mean difference percentage between calculated lateral spot sizes at 5 different locations along beam direction and measurements are below 3.5%.

Transitioning from measurement-based to combined patient-specific quality assurance for intensity-modulated proton therapy.

OBJECTIVE: This study is part of ongoing efforts aiming to transit from measurement-based to combined patient-specific quality assurance (PSQA) in intensity-modulated proton therapy (IMPT). A Monte Carlo (MC) dose-calculation algorithm is used to improve the independent dose calculation and to reveal the beam modeling deficiency of the analytical pencil beam (PB) algorithm. METHODS: A set of representative clinical IMPT plans with suboptimal PSQA results were reviewed. Verification plans were recalculated using an MC algorithm developed in-house. Agreements of PB and MC calculations with measurements that quantified by the γ passing rate were compared. RESULTS: The percentage of dose planes that met the clinical criteria for PSQA (>90% γ passing rate using 3%/3 mm criteria) increased from 71.40% in the original PB calculation to 95.14% in the MC recalculation. For fields without beam modifiers, nearly 100% of the dose planes exceeded the 95% γ passing rate threshold using the MC algorithm. The model deficiencies of the PB algorithm were found in the proximal and distal regions of the SOBP, where MC recalculation improved the γ passing rate by 11.27% (p < 0.001) and 16.80% (p < 0.001), respectively. CONCLUSIONS: The MC algorithm substantially improved the γ passing rate for IMPT PSQA. Improved modeling of beam modifiers would enable the use of the MC algorithm for independent dose calculation, completely replacing additional depth measurements in IMPT PSQA program. For current users of the PB algorithm, further improving the long-tail modeling or using MC simulation to generate the dose correction factor is necessary. ADVANCES IN KNOWLEDGE: We justified a change in clinical practice to achieve efficient combined PSQA in IMPT by using the MC algorithm that was experimentally validated in almost all the clinical scenarios in our center. Deficiencies in beam modeling of the current PB algorithm were identified and solutions to improve its dose-calculation accuracy were provided.

A track repeating algorithm for intensity modulated carbon ion therapy.

The fast dose calculator (FDC), a track repeating Monte Carlo (MC) algorithm was initially developed for proton therapy. The validation for proton therapy has been demonstrated in a previous work. In this work we presented the extension of FDC to the calculation of dose distributions for ions, particularly for carbon. Moreover the code algorithm is validated by comparing 3D dose distributions and dose volume histograms (DVH) calculated by FDC with Geant4. A total of 19 patients were employed, including three patients of prostate, five of brain, three of head and neck, four of lung and four of spine. We used a gamma-index technique to analyze dose distributions and we performed a dosimetric analysis for DVHs, a more direct and informative quantity for planning system assessment. The gamma-index passing rates of all patients discussed in this paper are above 90% with the criterion 1%/1 mm, above 98% with the criterion 2%/2 mm and over 99.9% with the criterion 3%/3 mm. The root mean square (RMS) of percent difference of dosimetric indices D 02, D 05, D 50, D 95 and D 98 are 0.75%, 0.70%, 0.79%, 0.83% and 0.76%. All the differences are within clinically accepted norms.

Reply to Comment on 'Linear energy transfer incorporated intensity modulated proton therapy optimization'.

In this reply we present additional description of our linear model for LET optimization in response to the comments by Dr Gorissen. We clarify that this model cannot guarantee global optimal solutions to the sum-of-fractions problem. Based on our data, it could be used to optimize LET efficiently while dose constraints are maintained.

Fixed- versus Variable-RBE Computations for Intensity Modulated Proton Therapy.

PURPOSE: To evaluate how using models of proton therapy that incorporate variable relative biological effectiveness (RBE) versus the current practice of using a fixed RBE of 1.1 affects dosimetric indices on treatment plans for large cohorts of patients treated with intensity modulated proton therapy (IMPT). METHODS AND MATERIALS: Treatment plans for 4 groups of patients who received IMPT for brain, head-and-neck, thoracic, or prostate cancer were selected. Dose distributions were recalculated in 4 ways: 1 with a fast-dose Monte Carlo calculator with fixed RBE and 3 with RBE calculated to 3 different models-McNamara, Wedenberg, and repair-misrepair-fixation. Differences among dosimetric indices (D02, D50, D98, and mean dose) for target volumes and organs at risk (OARs) on each plan were compared between the fixed-RBE and variable-RBE calculations. RESULTS: In analyses of all target volumes, for which the main concern is underprediction or RBE less than 1.1, none of the models predicted an RBE less than 1.05 for any of the cohorts. For OARs, the 2 models based on linear energy transfer, McNamara and Wedenberg, systematically predicted RBE >1.1 for most structures. For the mean dose of 25% of the plans for 2 OARs, they predict RBE equal to or larger than 1.4, 1.3, 1.3, and 1.2 for brain, head-and-neck, thorax, and prostate, respectively. Systematically lower increases in RBE are predicted by repair-misrepair-fixation, with a few cases (eg, femur) in which the RBE is less than 1.1 for all plans. CONCLUSIONS: The variable-RBE models predict increased doses to various OARs, suggesting that strategies to reduce high-dose linear energy transfer in critical structures should be developed to minimize possible toxicity associated with IMPT.

Comparison of Monte Carlo and analytical dose computations for intensity modulated proton therapy.

To evaluate the effect of approximations in clinical analytical calculations performed by a treatment planning system (TPS) on dosimetric indices in intensity modulated proton therapy. TPS calculated dose distributions were compared with dose distributions as estimated by Monte Carlo (MC) simulations, calculated with the fast dose calculator (FDC) a system previously benchmarked to full MC. This study analyzed a total of 525 patients for four treatment sites (brain, head-and-neck, thorax and prostate). Dosimetric indices (D02, D05, D20, D50, D95, D98, EUD and Mean Dose) and a gamma-index analysis were utilized to evaluate the differences. The gamma-index passing rates for a 3%/3 mm criterion for voxels with a dose larger than 10% of the maximum dose had a median larger than 98% for all sites. The median difference for all dosimetric indices for target volumes was less than 2% for all cases. However, differences for target volumes as large as 10% were found for 2% of the thoracic patients. For organs at risk (OARs), the median absolute dose difference was smaller than 2 Gy for all indices and cohorts. However, absolute dose differences as large as 10 Gy were found for some small volume organs in brain and head-and-neck patients. This analysis concludes that for a fraction of the patients studied, TPS may overestimate the dose in the target by as much as 10%, while for some OARs the dose could be underestimated by as much as 10 Gy. Monte Carlo dose calculations may be needed to ensure more accurate dose computations to improve target coverage and sparing of OARs in proton therapy.

Linear energy transfer incorporated intensity modulated proton therapy optimization.

The purpose of this study was to investigate the feasibility of incorporating linear energy transfer (LET) into the optimization of intensity modulated proton therapy (IMPT) plans. Because increased LET correlates with increased biological effectiveness of protons, high LETs in target volumes and low LETs in critical structures and normal tissues are preferred in an IMPT plan. However, if not explicitly incorporated into the optimization criteria, different IMPT plans may yield similar physical dose distributions but greatly different LET, specifically dose-averaged LET, distributions. Conventionally, the IMPT optimization criteria (or cost function) only includes dose-based objectives in which the relative biological effectiveness (RBE) is assumed to have a constant value of 1.1. In this study, we added LET-based objectives for maximizing LET in target volumes and minimizing LET in critical structures and normal tissues. Due to the fractional programming nature of the resulting model, we used a variable reformulation approach so that the optimization process is computationally equivalent to conventional IMPT optimization. In this study, five brain tumor patients who had been treated with proton therapy at our institution were selected. Two plans were created for each patient based on the proposed LET-incorporated optimization (LETOpt) and the conventional dose-based optimization (DoseOpt). The optimized plans were compared in terms of both dose (assuming a constant RBE of 1.1 as adopted in clinical practice) and LET. Both optimization approaches were able to generate comparable dose distributions. The LET-incorporated optimization achieved not only pronounced reduction of LET values in critical organs, such as brainstem and optic chiasm, but also increased LET in target volumes, compared to the conventional dose-based optimization. However, on occasion, there was a need to tradeoff the acceptability of dose and LET distributions. Our conclusion is that the inclusion of LET-dependent criteria in the IMPT optimization could lead to similar dose distributions as the conventional optimization but superior LET distributions in target volumes and normal tissues. This may have substantial advantages in improving tumor control and reducing normal tissue toxicities.

Validation of a track repeating algorithm for intensity modulated proton therapy: clinical cases study.

Monte Carlo (MC) methods are acknowledged as the most accurate technique to calculate dose distributions. However, due its lengthy calculation times, they are difficult to utilize in the clinic or for large retrospective studies. Track-repeating algorithms, based on MC-generated particle track data in water, accelerate dose calculations substantially, while essentially preserving the accuracy of MC. In this study, we present the validation of an efficient dose calculation algorithm for intensity modulated proton therapy, the fast dose calculator (FDC), based on a track-repeating technique. We validated the FDC algorithm for 23 patients, which included 7 brain, 6 head-and-neck, 5 lung, 1 spine, 1 pelvis and 3 prostate cases. For validation, we compared FDC-generated dose distributions with those from a full-fledged Monte Carlo based on GEANT4 (G4). We compared dose-volume-histograms, 3D-gamma-indices and analyzed a series of dosimetric indices. More than 99% of the voxels in the voxelized phantoms describing the patients have a gamma-index smaller than unity for the 2%/2 mm criteria. In addition the difference relative to the prescribed dose between the dosimetric indices calculated with FDC and G4 is less than 1%. FDC reduces the calculation times from 5 ms per proton to around 5 µs.

A symmetric probabilistic γ-index for Monte Carlo dose comparisons.

The γ-index is a widely used tool to compare two dose distributions, which combines both the dose difference and distance-to-agreement criteria into a single metric. The γ-index passing rate, defined as the percentage of dose points with γ-index value less than one, is often used as an agreement metric. However, the γ-index is not symmetric with respect to the choice of the reference and evaluation distributions. Moreover, the statistical fluctuations present in the dose distributions may have non-negligible effects on γ-passing rates. Fluctuations have opposite effects on the γ-passing rates depending whether they are present in the evaluation or the reference dose distributions. Those discrepancies are analyzed in the case of realistic clinical proton dose distributions. The concept of a probabilistic and symmetric γ-index is introduced to make more robust versus statistical fluctuations.

Benchmark measurements and simulations of dose perturbations due to metallic spheres in proton beams.

Monte Carlo simulations are increasingly used for dose calculations in proton therapy due to its inherent accuracy. However, dosimetric deviations have been found using Monte Carlo code when high density materials are present in the proton beam line. The purpose of this work was to quantify the magnitude of dose perturbation caused by metal objects. We did this by comparing measurements and Monte Carlo predictions of dose perturbations caused by the presence of small metal spheres in several clinical proton therapy beams as functions of proton beam range, spread-out Bragg peak width and drift space. Monte Carlo codes MCNPX, GEANT4 and Fast Dose Calculator (FDC) were used. Generally good agreement was found between measurements and Monte Carlo predictions, with the average difference within 5% and maximum difference within 17%. The modification of multiple Coulomb scattering model in MCNPX code yielded improvement in accuracy and provided the best overall agreement with measurements. Our results confirmed that Monte Carlo codes are well suited for predicting multiple Coulomb scattering in proton therapy beams when short drift spaces are involved.

GRID-ENABLED TREATMENT PLANNING FOR PROTON THERAPY USING MONTE CARLO SIMULATIONS.

Grid computing is an emerging technology that enables computational tasks to be accomplished in a collaborative approach by using a distributed network of computers. The grid approach is especially important for computationally intensive problems that are not tractable with a single computer or even with a small cluster of computers, e.g., radiation transport calculations for cancer therapy. The objective of this work was to extend a Monte Carlo (MC) transport code used for proton radiotherapy to utilize grid computing techniques and demonstrate its promise in reducing runtime from days to minutes. As proof of concept we created the Medical Grid between Texas Tech University and Rice University. Preliminary computational experiments were carried out in the GEANT4 simulation environment for transport of 25 ×106 200 MeV protons in a prostate cancer treatment plan. The simulation speedup was approximately linear; deviations were attributed to the spectrum of parallel runtimes and communication overhead due to Medical Grid computing. The results indicate that ~3 × 105 to 5 × 105 proton events with processor core would result in 65 to 83% efficiency. Extrapolation of our results indicates that about 103 processor cores of the class used here would reduce the MC simulation runtime from 18.3 days to ~1 h.