Rounded leaf end modeling in Pinnacle VMAT treatment planning for fixed jaw linacs.

During volume-modulated arc therapies (VMAT), dosimetric errors are introduced by multiple open dynamic leaf gaps that are present in fixed diaphragm linear accelerators. The purpose of this work was to develop a methodology for adjusting the rounded leaf end modeling parameters to improve out-of-field dose agreement in SmartArc VMAT treatment plans delivered by fixed jaw linacs where leaf gap dose is not negligible. Leaf gap doses were measured for an Elekta beam modulator linac with 0.4 cm micro-multileaf collimators (MLC) using an A16 micro-ionization chamber, a MatriXX ion chamber detector array, and Kodak EDR2 film dosimetry in a solid water phantom. The MLC offset and rounded end tip radius were adjusted in the Pinnacle treatment planning system (TPS) to iteratively arrive at the optimal configuration for 6 MV and 10 MV photon energies. Improvements in gamma index with a 3%/3 mm acceptance criteria and an inclusion threshold of 5% of maximum dose were measured, analyzed, and validated using an ArcCHECK diode detector array for field sizes ranging from 1.6 to 14 cm square field arcs and Task Group (TG) 119 VMAT test cases. The best results were achieved for a rounded leaf tip radius of 13 cm with a 0.1 cm MLC offset. With the optimized MLC model, measured gamma indices ranged between 99.9% and 91.7% for square field arcs with sizes between 3.6 cm and 1.6 cm, with a maximum improvement of 42.7% for the 1.6 cm square field size. Gamma indices improved up to 2.8% in TG-119 VMAT treatment plans. Imaging and Radiation Oncology Core (IROC) credentialing of a VMAT plan with the head and neck phantom passed with a gamma index of 100%. Fine-tune adjustments to MLC rounded leaf ends may improve patient-specific QA pass rates and provide more accurate predictions of dose deposition to avoidance structures.

Characterizing a deformable registration algorithm for surface-guided breast radiotherapy.

PURPOSE: Surface-guided radiation therapy (SGRT) is a nonionizing imaging approach for patient setup guidance, intra-fraction monitoring, and automated breath-hold gating of radiation treatments. SGRT employs the premise that the external patient surface correlates to the internal anatomy, to infer the treatment isocenter position at time of treatment delivery. Deformations and posture variations are known to impact the correlation between external and internal anatomy. However, the degree, magnitude, and algorithm dependence of this impact are not intuitive and currently no methods exist to assess this relationship. The primary aim of this work was to develop a framework to investigate and understand how a commercial optical surface imaging system (C-RAD, Uppsala, Sweden), which uses a nonrigid registration algorithm, handles rotations and surface deformations. METHODS: A workflow consisting of a female torso phantom and software-introduced transformations to the corresponding digital reference surface was developed. To benchmark and validate the approach, known rigid translations and rotations were first applied. Relevant breast radiotherapy deformations related to breast size, hunching/arching back, distended/deflated abdomen, and an irregular surface to mimic a cover sheet over the lower part of the torso were investigated. The difference between rigid and deformed surfaces was evaluated as a function of isocenter location. RESULTS: For all introduced rigid body transformations, C-RAD computed isocenter shifts were determined within 1 mm and 1˚. Additional translational shifts to correct for rotations as a function of isocenter location were determined with the same accuracy. For yaw setup errors, the difference in shift corrections between a plan with an isocenter placed in the center of the breast (BrstIso) and one located 12 cm superiorly (SCFIso) was 2.3 mm/1˚ in lateral direction. Pitch setup errors resulted in a difference of 2.1 mm/1˚ in vertical direction. For some of the deformation scenarios, much larger differences up to 16 mm and 7˚ in the calculated shifts between BrstIso and SCFIso were observed that could lead to large unintended gaps or overlap between adjacent matched fields if uncorrected. CONCLUSIONS: The methodology developed lends itself well for quality assurance (QA) of SGRT systems. The deformable C-RAD algorithm determined accurate shifts for rigid transformations, and this was independent of isocenter location. For surface deformations, the position of the isocenter had considerable impact on the registration result. It is recommended to avoid off-axis isocenters during treatment planning to optimally utilize the capabilities of the deformable image registration algorithm, especially when multiple isocenters are used with fields that share a field edge.

Predictors of cardiac and lung dose sparing in DIBH for left breast treatment.

This retrospective study of left breast radiation therapy (RT) investigates the correlation between anatomical parameters and dose to heart or/and left lung in deep inspiration breath-hold (DIBH) compared to free-breathing (FB) technique. Anatomical parameters of sixty-seven patients, treated with a step-and-shoot technique to 50 Gy or 50.4 Gy were included. They consisted of the cardiac contact distances in axial (CCDax) and parasagittal (CCDps) planes, and the lateral heart-to-chest distance (HCD). Correlation analysis was performed to identify predictors for heart and lung dose sparing. Paired t-test and linear regression were used for data analysis with significance level of p = 0.05. All dose metrics for heart and lung were significantly reduced with DIBH, however 21% of patients analyzed had less than 1.0 Gy mean heart dose reduction. Both FB-CCDpsdistance and FB-HCD correlated with FB mean heart dose and mean DIBH heart dose reduction. The strongest correlation was observed for the ratio of FB-CCDpsand FB-HCD with heart dose sparing. A FB-CCDps and FB-HCD model was developed to predict DIBH induced mean heart dose reduction, with 1.04 Gy per unit of FB-CCDps/FB-HCD. Variation between predicted and actual mean heart dose reduction ranged from -0.6 Gy to 0.6 Gy. In this study, FB-CCDps and FB-HCD distance served as predictors for heart dose reduction with DIBH equally, with FB-CCDps/FB-HCD as a stronger predictor. These parameters and the prediction model could be further investigated for use as a tool to better select patients who will benefit from DIBH.

Characterization of a Bayesian network-based radiotherapy plan verification model.

PURPOSE: The current process for radiotherapy treatment plan quality assurance relies on human inspection of treatment plans, which is time-consuming, error prone and oft reliant on inconsistently applied professional judgments. A previous proof-of-principle paper describes the use of a Bayesian network (BN) to aid in this process. This work studied how such a BN could be expanded and trained to better represent clinical practice. METHODS: We obtained 51 540 unique radiotherapy cases including diagnostic, prescription, plan/beam, and therapy setup factors from a de-identified Elekta oncology information system from the years 2010-2017 from a single institution. Using a knowledge base derived from clinical experience, factors were coordinated into a 29-node, 40-edge BN representing dependencies among the variables. Conditional probabilities were machine learned using expectation maximization module using all data except a subset of 500 patient cases withheld for testing. Different classes of errors that were obtained from incident learning systems were introduced to the testing set of cases which were withheld from the dataset used for building the BN. Different sizes of datasets were used to train the network. In addition, the BN was trained using data from different length epochs as well as different eras. Its performance under these different conditions was evaluated by means of Areas Under the receiver operating characteristic Curve (AUC). RESULTS: Our performance analysis found AUCs of 0.82, 0.85, 0.89, and 0.88 in networks trained with 2-yr, 3-yr 4-yr and 5-yr windows. With a 4-yr sliding window, we found AUC reduction of 3% per year when moving the window back in time in 1-yr steps. Compared to the 4-yr window moved back by 4 yrs (2010-2013 vs 2014-2017), the largest component of overall reduction in AUC over time was from the loss of detection performance in plan/beam error types. CONCLUSIONS: The expanded BN method demonstrates the ability to detect classes of errors commonly encountered in radiotherapy planning. The results suggest that a 4-yr training dataset optimizes the performance of the network in this institutional dataset, and that yearly updates are sufficient to capture the evolution of clinical practice and maintain fidelity.

Quantitative radiomics: Validating image textural features for oncological PET in lung cancer.

BACKGROUND AND PURPOSE: Radiomics textural features derived from PET imaging are of broad and current interest due to recent evidence of their prognostic value during cancer management. An inherent assumption is the link between these imaging features and the underlying tumoral phenotypic spatial heterogeneity. The purpose of this work was to validate this assumption for tumors within the lung through a comparison of image based textural features and the ground truth activity distribution from which the images were created. A second purpose was to assess the level at which PET imaging introduces spatial texture not present in the associated ground truth activity distribution. MATERIALS AND METHODS: 25 lung lesions were created using an anthropomorphic phantom. Ten of the lesions had a spherical shape with a uniform activity distribution. The remaining 15 had an irregular shape with a heterogeneous activity distribution. PET images were created for each lesion using Monte Carlo simulation. 79 textural features related to the gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, run length, and size zone matrices were derived from both the simulated PET images and ground truth activity maps. A comparison was made between the two datasets using statistical analysis. RESULTS: For homogenous lesions, features extracted from the PET images were largely irrelevant to the underlying uniform activity distribution. Additionally, the majority of these features assumed substantial values implying that an extensive amount of spatial texture had been introduced into the final imaging data. For heterogeneous lesions, complex trends were observed in the deviation between features extracted from PET images and those extracted from the ground truth activity maps. Moreover, the extent of both the deviation and the associated dynamic range was seen to be greatly feature-dependent. CONCLUSION: The use of image based textural features as a surrogate for tumoral phenotypic spatial heterogeneity could not be clearly validated. The association between the two is complex and a significant amount of uncertainty exist due to the introduction of incidental texture during image acquisition and reconstruction.

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.

Quantitative imaging: Correlating image features with the segmentation accuracy of PET based tumor contours in the lung.

The purpose of this study was to investigate the correlation between image features extracted from PET images and the accuracy of manually drawn lesion contours in the lung. Such correlations are interesting in that they could potentially be used in predictive models to help guide physician contouring. In this work, 26 synthetic PET datasets were created using an anthropomorphic phantom and Monte Carlo simulation. Manual contours of simulated lesions were provided by 10 physicians. Contour accuracy was quantified using five commonly used similarity metrics which were then correlated with several features extracted from the images. Features were sub-divided into three groups using intensity, geometry, and texture as categorical descriptors. When averaged among the participants, the results showed relatively strong correlations with complexity and contrastI (r≥0.65, p<0.001), and moderate correlations with several other image features (r≥0.5, p<0.01). The predictive nature of these correlations was improved through stepwise regression and the creation of multi-feature models. Imaging features were also correlated with the standard deviation of contouring error in order to investigate inter-observer variability. Several features were consistently identified as influential including integral of mean curvature and complexity. These relationships further the understanding as to what causes variation in the contouring of PET positive lesions.

Commissioning optically stimulated luminescence in vivo dosimeters for fast neutron therapy.

PURPOSE: Clinical in vivo dosimeters intended for use with photon and electron therapies have not been utilized for fast neutron therapy because they are highly susceptible to neutron damage. The objective of this work was to determine if a commercial optically stimulated luminescence (OSL) in vivo dosimetry system could be adapted for use in fast neutron therapy. METHODS: A 50.5 MeV fast neutron beam generated by a clinical neutron therapy cyclotron was used to irradiate carbon doped aluminum oxide (Al2O3:C) optically simulated luminescence dosimeters (OSLDs) in a solid water phantom under standard calibration conditions, 150 cm SAD, 1.7 cm depth, and 10.3 × 10.0 cm field size. OSLD fading and electron trap depletion studies were performed with the OSLDs irradiated with 20 and 50 cGy and monitored over a 24-h period to determine the optimal time for reading the dosimeters during calibration. Four OSLDs per group were calibrated over a clinical dose range of 0-150 cGy. RESULTS: OSLD measurement uncertainties were lowered to within ±2%-3% of the expected dose by minimizing the effect of transient fading that occurs with neutron irradiation and maintaining individual calibration factors for each dosimeter. Dose dependent luminescence fading extended beyond the manufacturer's recommended 10 min period for irradiation with photon or electron beams. To minimize OSL variances caused by inconsistent fading among dosimeters, the observed optimal time for reading the OSLDs postirradiation was between 30 and 90 min. No field size, wedge factor, or gantry angle dependencies were observed in the OSLDs irradiated by the studied fast neutron beam. CONCLUSIONS: Measurements demonstrated that uncertainties less than ±3% were attainable in OSLDs irradiated with fast neutrons under clinical conditions. Accuracy and precision comparable to clinical OSL measurements observed with photons can be achieved by maintaining individual OSLD calibration factors and minimizing transient fading effects.