Development of a New Independent Monte Carlo Dose Calculation Quality Assurance Audit Tool for Clinical Trials

Introduction: Commercially available treatment planning systems (TPS) may use a number of different radiation dose calculation algorithms during the planning process. The Radiological Physics Center (RPC), tasked with ensuring clinically comparable and consistent dose delivery amongst institutions participating in NCI funded multi-institutional clinical trials, has traditionally relied upon measurements to achieve this objective. As a supplement to the tools used by the RPC, an independent dose calculation tool is needed to determine patient dose distributions in three dimensions so as to act as a quality assurance tool for the dose calculations. Methods: Multiple source models representing the output of Elekta 6MV and 10MV and Varian TrueBeam Flattening Filter Free (FFF) 6MV and FFF 10MV therapeutic x-ray beams were developed. The Monte Carlo technique, using the Dose Planning Method (DPM) algorithm, was used in radiation dose calculations. During validation calculations were compared to open field measurements in a water phantom. Benchmarking was a measurement based comparison of mock treatment plans in anthropomorphic phantoms. Treatment plans included intensity modulated radiation therapy and stereotactic body radiation therapy techniques. Past phantom treatment plans submitted through a remote auditing program were recalculated using the tool and compared to submitted measurement data as a test of the models’ robustness. Results: The average percentage of data passing a ±2%/2mm gamma criterion during validation testing was 99.5%, 99.6%, 98.1%, and 98.1% for Elekta 6MV, 10MV, Varian TrueBeam FFF 6MV, and FFF 10MV beams, respectively. The percentage of data passing the benchmarking evaluation criterion of ±3%/2mm was 87.4%, 89.9%, 90.1%, and 90.8% for Elekta 6MV, Elekta 10MV, Varian TrueBeam FFF 6MV, and Varian TrueBeam FFF 10MV beams, respectively. Conclusions: Elekta 6MV and 10MV and Varian TrueBeam FFF 6MV and FFF 10MV multiple source models based on dose calculations using the DPM Monte Carlo code were successfully developed, validated, and benchmarked against measurements. A recalculation of TPS dose from archived phantom credentialing audits was performed as a proof of concept for the models’ utility as a quality assurance tool for use in clinical trial audits

Validation of an automated contouring and treatment planning tool for pediatric craniospinal radiation therapy

PurposeTreatment planning for craniospinal irradiation (CSI) is complex and time-consuming, especially for resource-constrained centers. To alleviate demanding workflows, we successfully automated the pediatric CSI planning pipeline in previous work. In this work, we validated our CSI autosegmentation and autoplanning tool on a large dataset from St. Jude Children’s Research Hospital.MethodsSixty-three CSI patient CT scans were involved in the study. Pre-planning scripts were used to automatically verify anatomical compatibility with the autoplanning tool. The autoplanning pipeline generated 15 contours and a composite CSI treatment plan for each of the compatible test patients (n=51). Plan quality was evaluated quantitatively with target coverage and dose to normal tissue metrics and qualitatively with physician review, using a 5-point Likert scale. Three pediatric radiation oncologists from 3 institutions reviewed and scored 15 contours and a corresponding composite CSI plan for the final 51 test patients. One patient was scored by 3 physicians, resulting in 53 plans scored total.ResultsThe algorithm automatically detected 12 incompatible patients due to insufficient junction spacing or head tilt and removed them from the study. Of the 795 autosegmented contours reviewed, 97% were scored as clinically acceptable, with 92% requiring no edits. Of the 53 plans scored, all 51 brain dose distributions were scored as clinically acceptable. For the spine dose distributions, 92%, 100%, and 68% of single, extended, and multiple-field cases, respectively, were scored as clinically acceptable. In all cases (major or minor edits), the physicians noted that they would rather edit the autoplan than create a new plan.ConclusionsWe successfully validated an autoplanning pipeline on 51 patients from another institution, indicating that our algorithm is robust in its adjustment to differing patient populations. We automatically generated 15 contours and a comprehensive CSI treatment plan for each patient without physician intervention, indicating the potential for increased treatment planning efficiency and global access to high-quality radiation therapy

Evaluating Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy

PurposeFour-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP).Methods and materialsSeventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics.ResultsThe most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20 Gy (AUC, 0.70). Probabilities of grade 3+ RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20 Gy) to the functional subvolumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20 Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55).ConclusionsA full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiation therapy process

Evaluating the Toxicity Reduction With Computed Tomographic Ventilation Functional Avoidance Radiation Therapy

PurposeComputed tomographic (CT) ventilation imaging is a new modality that uses 4-dimensional (4D) CT information to calculate lung ventilation. Although retrospective studies have reported on the reduction in dose to functional lung, no work to our knowledge has been published in which the dosimetric improvements have been translated to a reduction in the probability of pulmonary toxicity. Our work estimates the reduction in toxicity for CT ventilation-based functional avoidance planning.Methods and materialsSeventy previously treated lung cancer patients who underwent 4DCT imaging were used for the study. CT ventilation maps were calculated with 4DCT deformable image registration and a density change-based algorithm. Pneumonitis was graded on the basis of imaging and clinical presentation. Maximum likelihood methods were used to generate normal tissue complication probability (NTCP) models predicting grade 2 or higher (2+) and grade 3+ pneumonitis as a function of dose (V5 Gy, V10 Gy, V20 Gy, V30 Gy, and mean dose) to functional lung. For 30 patients a functional plan was generated with the goal of reducing dose to the functional lung while meeting Radiation Therapy Oncology Group 0617 constraints. The NTCP models were applied to the functional plans and the clinically used plans to calculate toxicity reduction.ResultsBy the use of functional avoidance planning, absolute reductions in grade 2+ NTCP of 6.3%, 7.8%, and 4.8% were achieved based on the mean fV20 Gy, fV30 Gy, and mean dose to functional lung metrics, respectively. Absolute grade 3+ NTCP reductions of 3.6%, 4.8%, and 2.4% were achieved with fV20 Gy, fV30 Gy, and mean dose to functional lung. Maximum absolute reductions of 52.3% and 16.4% were seen for grade 2+ and grade 3+ pneumonitis for individual patients.ConclusionOur study quantifies the possible toxicity reduction from CT ventilation-based functional avoidance planning. Reductions in grades 2+ and 3+ pneumonitis were 7.1% and 4.7% based on mean dose-function metrics, with reductions as high as 52.3% for individual patients. Our work provides seminal data for determining the potential toxicity benefit from incorporating CT ventilation into thoracic treatment planning

Image-based data mining applies to data collected from children

PURPOSE: Image-based data mining (IBDM) is a novel voxel-based method for analyzing radiation dose responses that has been successfully applied in adult data. Because anatomic variability and side effects of interest differ for children compared to adults, we investigated the feasibility of IBDM for pediatric analyses. METHODS: We tested IBDM with CT images and dose distributions collected from 167 children (aged 10 months to 20 years) who received proton radiotherapy for primary brain tumors. We used data from four reference patients to assess IBDM sensitivity to reference selection. We quantified spatial-normalization accuracy via contour distances and deviations of the centers-of-mass of brain substructures. We performed dose comparisons with simplified and modified clinical dose distributions with a simulated effect, assessing their accuracy via sensitivity, positive predictive value (PPV) and Dice similarity coefficient (DSC). RESULTS: Spatial normalizations and dose comparisons were insensitive to reference selection. Normalization discrepancies were small (average contour distance < 2.5 mm, average center-of-mass deviation < 6 mm). Dose comparisons identified differences (p < 0.01) in 81% of simplified and all modified clinical dose distributions. The DSCs for simplified doses were high (peak frequency magnitudes of 0.9–1.0). However, the PPVs and DSCs were low (maximum 0.3 and 0.4, respectively) in the modified clinical tests. CONCLUSIONS: IBDM is feasible for childhood late-effects research. Our findings may inform cohort selection in future studies of pediatric radiotherapy dose responses and facilitate treatment planning to reduce treatment-related toxicities and improve quality of life among childhood cancer survivors

Feasibility of the Audio-Visual Assisted Therapeutic Ambience in Radiotherapy (AVATAR) System for Anesthesia Avoidance in Pediatric Patients: A Multicenter Trial.

PurposeThe Audio-Visual Assisted Therapeutic Ambience in Radiotherapy (AVATAR) system was the first published radiation therapy (RT)-compatible system to reduce the need for pediatric anesthesia through video-based distraction. We evaluated the feasibility of AVATAR implementation and effects on anesthesia use, quality of life, and anxiety in a multicenter pediatric trial.Methods and materialsPediatric patients 3 to 10 years of age preparing to undergo RT at 10 institutions were prospectively enrolled. Children able to undergo at least 1 fraction of RT using AVATAR without anesthesia were considered successful (S). Patients requiring anesthesia for their entire treatment course were nonsuccessful (NS). The PedsQL3.0 Cancer Module (PedsQL) survey assessed quality of life and was administered to the patient and guardian at RT simulation, midway through RT, and at final treatment. The modified Yale Preoperative Anxiety Scale (mYPAS) assessed anxiety and was performed at the same 3 time points. Success was evaluated using the χ2 test. PedsQL and mYPAS scores were assessed using mixed effects models with time points evaluated as fixed effects and a random intercept on the subject.ResultsEighty-one children were included; median age was 7 years. AVATAR was successful at all 10 institutions and with photon and proton RT. There were 63 (78%) S patients; anesthesia was avoided for a median of 20 fractions per patient. Success differed by age (P&nbsp;=&nbsp;.04) and private versus public insurance (P &lt; .001). Both patient (P&nbsp;=&nbsp;.008) and parent (P&nbsp;=&nbsp;.006) PedsQL scores significantly improved over the course of RT for patients aged 5 to 7. Anxiety in the treatment room decreased for both S and NS patients over RT course (P &lt; .001), by age (P &lt; .001), and by S versus NS patients (P &lt; .001).ConclusionsIn this 10-center prospective trial, anesthesia avoidance with AVATAR was 78% in children aged 3 to 10 years, higher than among age-matched historical controls (49%; P &lt; .001). AVATAR implementation is feasible across multiple institutions and should be further studied and made available to patients who may benefit from video-based distraction