Accuracy and stability of deep inspiration breath hold in gated breast radiotherapy - A comparison of two tracking and guidance systems.

PURPOSE: To characterize reproducibility of patient breath-hold positioning and compare tracking system performance for Deep Inspiration Breath Hold (DIBH) gated left breast radiotherapy. METHODS: 29 consecutive left breast DIBH patients (655 fractions) were treated under the guidance of Calypso surface beacons with audio-feedback and 35 consecutive patients (631 fractions) were treated using C-RAD Catalyst HD surface imaging with audiovisual feedback. The Calypso system tracks a centroid determined by two radio-frequency transponders, with a manually enforced institutional tolerance, while the surface image based CatalystHD system utilizes real-time biometric feedback to track a pre-selected point with an institutional tolerance enforced by the Elekta Response gating interface. DIBH motion data from Calypso was extracted to obtain the displacement of breath hold marker in ant/post direction from a set-zero reference point. Ant/post point displacement data from CatalystHD was interpreted by computing the difference between raw tracking points and the center of individual gating windows. Mean overall errors were compared using Welsh's unequal variance t-test. Wilcoxon rank sum test were used for statistical analysis with P < 0.05 considered significant. RESULTS: Mean overall error for Calypso and CatalystHD were 0.33 ±â€¯1.17 mm and 0.22 ±â€¯0.43 mm, respectively, with t-test comparison P-value < 0.034. Absolute errors for Calypso and CatalystHD were 0.95 ±â€¯0.75 mm and 0.38 ±â€¯0.30 mm, respectively, with Wilcoxon rank sum test P-value <2×10-16. Average standard deviation per fraction was found to be 0.74 ±â€¯0.44 mm for Calypso patients versus 0.54 ±â€¯0.22 mm for CatalystHD. CONCLUSION: Reduced error distribution widths in overall positioning, deviation of position, and per fraction deviation suggest that the use of functionalities available in CatalystHD such as audiovisual biofeedback and patient surface matching improves accuracy and stability during DIBH gated left breast radiotherapy.

Utilizing simulated errors in radiotherapy plans to quantify the effectiveness of the physics plan review.

PURPOSE: The review of a radiation therapy plan by a physicist prior to treatment is a standard tool for ensuring the quality of treatments. However, little is known about how well this task is performed in practice. The goal of this study is to present a novel method to measure the effectiveness of physics plan review by introducing simulated errors into computerized "mock" treatment charts and measuring the performance of plan review by physicists. METHODS: We generated six simulated treatment charts containing multiple errors. To select errors, we compiled a list based on events from a departmental incident learning system and an international incident learning system (SAFRON). Seventeen errors with the highest scores for frequency and severity were included in the simulations included six mock treatment charts. Eight physicists reviewed the simulated charts as they would a normal pretreatment plan review, with each chart being reviewed by at least six physicists. There were 113 data points for evaluation. Observer bias was minimized using a simple error vs hidden error approach, using detectability scores for stratification. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. RESULTS: Simulated errors were detected in 67% of reviews [58-75%] (95% confidence interval [CI] in brackets). Of the errors included in the simulated plans, the following error scenarios had the highest detection rates: an incorrect isocenter in DRR (93% [70-99%]), a planned dose different from the prescribed dose (92% [67-99%]) and invalid QA (85% [58-96%]). Errors with low detection rates included incorrect CT dataset (0%, [0-39%]) and incorrect isocenter localization in planning system (38% [18-64%]). Detection rates of errors from simulated charts were compared against observed detection rates of errors from a departmental incident learning system. CONCLUSIONS: It has been notoriously difficult to quantify error and safety performance in oncology. This study uses a novel technique of simulated errors to quantify performance and suggests that the pretreatment physics plan review identifies some errors with high fidelity while other errors are more challenging to detect. These data will guide future work on standardization and automation. The example process studied here was physics plan review, but this approach of simulated errors may be applied in other contexts as well and may also be useful for training and education purposes.

Decision analytic modeling for the economic analysis of proton radiotherapy for non-small cell lung cancer.

BACKGROUND: Although proton radiation treatments are more costly than photon/X-ray therapy, they may lower overall treatment costs through reducing rates of severe toxicities and the costly management of those toxicities. To study this issue, we created a decision-model comparing proton vs. X-ray radiotherapy for locally advanced non-small cell lung cancer patients. METHODS: An influence diagram was created to model for radiation delivery, associated 6-month pneumonitis/esophagitis rates, and overall costs (radiation plus toxicity costs). Pneumonitis (age, chemo type, V20, MLD) and esophagitis (V60) predictors were modeled to impact toxicity rates. We performed toxicity-adjusted, rate-adjusted, risk group-adjusted, and radiosensitivity analyses. RESULTS: Upfront proton treatment costs exceeded that of photons [$16,730.37 (3DCRT), $23,893.83 (IMRT), $41,061.80 (protons)]. Based upon expected population pneumonitis and esophagitis rates for each modality, protons would be expected to recover $1,065.62 and $1,139.63 of the cost difference compared to 3DCRT or IMRT. For patients treated with IMRT experiencing grade 4 pneumonitis or grade 4 esophagitis, costs exceeded patients treated with protons without this toxicity. 3DCRT patients with grade 4 esophagitis had higher costs than proton patients without this toxicity. For the risk group analysis, high risk patients (age >65, carboplatin/paclitaxel) benefited more from proton therapy. A biomarker may allow patient selection for proton therapy, although the AUC alone is not sufficient to determine if the biomarker is clinically useful. CONCLUSIONS: The comparison between proton and photon/X-ray radiation therapy for NSCLC needs to consider both the up-front cost of treatment and the possible long term cost of complications. In our analysis, current costs favor X-ray therapy. However, relatively small reductions in the cost of proton therapy may result in a shift to the preference for proton therapy.

Applying Probabilistic Decision Models to Clinical Trial Design.

Clinical trial design most often focuses on a single or several related outcomes with corresponding calculations of statistical power. We consider a clinical trial to be a decision problem, often with competing outcomes. Using a current controversy in the treatment of HPV-positive head and neck cancer, we apply several different probabilistic methods to help define the range of outcomes given different possible trial designs. Our model incorporates the uncertainties in the disease process and treatment response and the inhomogeneities in the patient population. Instead of expected utility, we have used a Markov model to calculate quality adjusted life expectancy as a maximization objective. Monte Carlo simulations over realistic ranges of parameters are used to explore different trial scenarios given the possible ranges of parameters. This modeling approach can be used to better inform the initial trial design so that it will more likely achieve clinical relevance.

Clinical Positioning Accuracy for Multisession Stereotactic Radiotherapy With the Gamma Knife Perfexion.

Multisession stereotactic radiation therapy is increasingly being seen as a preferred option for intracranial diseases in close proximity to critical structures and for larger target volumes. The objective of this study is to investigate the reproducibility of the Extend system from Elekta. A retrospective review was conducted for all patients treated with multisession Gamma Knife between July 2010 and June 2015, including both malignant and benign lesions. Eighty-four patients were treated in this 5-year span. The average residual daily setup uncertainty was 0.48 (0.19) mm. We compare measurements of setup uncertainty from the Extend system to measurements performed with a linac-based approach previously used in our center. The Extend system has significantly reduced setup uncertainty for fractionated intracranial treatments at our institution. Positive results were observed in a small population of edentulous patients. The Extend system compares favorably with other approaches to delivering intracranial stereotactic radiotherapy and is a robust, simple-to-use, and precise method for treating multisession intracranial lesions.

Electron beam energy QA - a note on measurement tolerances.

Monthly QA is recommended to verify the constancy of high-energy electron beams generated for clinical use by linear accelerators. The tolerances are defined as 2%/2 mm in beam penetration according to AAPM task group report 142. The practical implementation is typically achieved by measuring the ratio of readings at two different depths, preferably near the depth of maximum dose and at the depth corresponding to half the dose maximum. Based on beam commissioning data, we show that the relationship between the ranges of energy ratios for different electron energies is highly nonlinear. We provide a formalism that translates measurement deviations in the reference ratios into change in beam penetration for electron energies for six Elekta (6-18 MeV) and eight Varian (6-22 MeV) electron beams. Experimental checks were conducted for each Elekta energy to compare calculated values with measurements, and it was shown that they are in agreement. For example, for a 6 MeV beam a deviation in the measured ionization ratio of ± 15% might still be acceptable (i.e., be within ± 2 mm), whereas for an 18 MeV beam the corresponding tolerance might be ± 6%. These values strongly depend on the initial ratio chosen. In summary, the relationship between differences of the ionization ratio and the corresponding beam energy are derived. The findings can be translated into acceptable tolerance values for monthly QA of electron beam energies.

Personalized treatment planning with a model of radiation therapy outcomes for use in multiobjective optimization of IMRT plans for prostate cancer.

PURPOSE: To build a new treatment planning approach that extends beyond radiation transport and IMRT optimization by modeling the radiation therapy process and prognostic indicators for more outcome-focused decision making. METHODS: An in-house treatment planning system was modified to include multiobjective inverse planning, a probabilistic outcome model, and a multi-attribute decision aid. A genetic algorithm generated a set of plans embodying trade-offs between the separate objectives. An influence diagram network modeled the radiation therapy process of prostate cancer using expert opinion, results of clinical trials, and published research. A Markov model calculated a quality adjusted life expectancy (QALE), which was the endpoint for ranking plans. RESULTS: The Multiobjective Evolutionary Algorithm (MOEA) was designed to produce an approximation of the Pareto Front representing optimal tradeoffs for IMRT plans. Prognostic information from the dosimetrics of the plans, and from patient-specific clinical variables were combined by the influence diagram. QALEs were calculated for each plan for each set of patient characteristics. Sensitivity analyses were conducted to explore changes in outcomes for variations in patient characteristics and dosimetric variables. The model calculated life expectancies that were in agreement with an independent clinical study. CONCLUSIONS: The radiation therapy model proposed has integrated a number of different physical, biological and clinical models into a more comprehensive model. It illustrates a number of the critical aspects of treatment planning that can be improved and represents a more detailed description of the therapy process. A Markov model was implemented to provide a stronger connection between dosimetric variables and clinical outcomes and could provide a practical, quantitative method for making difficult clinical decisions.

Role of positron emission tomography in the treatment of occult disease in head-and-neck cancer: a modeling approach.

PURPOSE: To determine under what conditions positron emission tomography (PET) imaging will be useful in decisions regarding the use of radiotherapy for the treatment of clinically occult lymph node metastases in head-and-neck cancer. METHODS AND MATERIALS: A decision model of PET imaging and its downstream effects on radiotherapy outcomes was constructed using an influence diagram. This model included the sensitivity and specificity of PET, as well as the type and stage of the primary tumor. These parameters were varied to determine the optimal strategy for imaging and therapy for different clinical situations. Maximum expected utility was the metric by which different actions were ranked. RESULTS: For primary tumors with a low probability of lymph node metastases, the sensitivity of PET should be maximized, and 50 Gy should be delivered if PET is positive and 0 Gy if negative. As the probability for lymph node metastases increases, PET imaging becomes unnecessary in some situations, and the optimal dose to the lymph nodes increases. The model needed to include the causes of certain health states to predict current clinical practice. CONCLUSION: The model demonstrated the ability to reproduce expected outcomes for a range of tumors and provided recommendations for different clinical situations. The differences between the optimal policies and current clinical practice are likely due to a disparity between stated clinical decision processes and actual decision making by clinicians.

A decision aid for intensity-modulated radiation-therapy plan selection in prostate cancer based on a prognostic Bayesian network and a Markov model.

OBJECTIVE: The prognosis of cancer patients treated with intensity-modulated radiation-therapy (IMRT) is inherently uncertain, depends on many decision variables, and requires that a physician balance competing objectives: maximum tumor control with minimal treatment complications. METHODS: In order to better deal with the complex and multiple objective nature of the problem we have combined a prognostic probabilistic model with multi-attribute decision theory which incorporates patient preferences for outcomes. RESULTS: The response to IMRT for prostate cancer was modeled. A Bayesian network was used for prognosis for each treatment plan. Prognoses included predicting local tumor control, regional spread, distant metastases, and normal tissue complications resulting from treatment. A Markov model was constructed and used to calculate a quality-adjusted life-expectancy which aids in the multi-attribute decision process. CONCLUSIONS: Our method makes explicit the tradeoffs patients face between quality and quantity of life. This approach has advantages over current approaches because with our approach risks of health outcomes and patient preferences determine treatment decisions.

Toward understanding MHC disease associations: partial resequencing of 46 distinct HLA haplotypes.

We carried out a resequencing project that examined 552 kb of sequence from each of 46 individual HLA haplotypes representing a diversity of HLA allele types, generating nearly 27 Mb of fully phased genomic sequence. Haplotype blocks were defined extending from telomeric of HLA-F to centromeric of HLA-DP including in total 5186 MHC SNPs. To investigate basic questions about the evolutionary origin of common HLA haplotypes, and to obtain an estimate of rare variation in the MHC, we similarly examined two additional sets of samples. In 19 independent HLA-A1, B8, DR3 chromosomes, the most common HLA haplotype in Northern European Caucasians, variation was found at 11 SNP positions in the 3600-kb region from HLA-A to DR. Partial resequencing of 282 individuals in the gene-dense class III region identified significant variability beyond what could have been detected by linkage to common SNPs.