Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans.

PURPOSE: Performing measurement-based patient-specific quality assurance (PSQA) is recognized as a resource-intensive and time inefficient task in the radiation therapy treatment workflow. Paired with technological refinements in modern radiation therapy, research toward measurement-free PSQA has seen increased interest during the past 5 years. However, these efforts have not been clinically implemented or prospectively validated in the United States. We propose a virtual QA (VQA) system and workflow to assess the safety and workload reduction of measurement-free PSQA. METHODS: An XGBoost machine learning model was designed to predict PSQA outcomes of volumetric modulated arc therapy plans, represented as percent differences between the measured ion chamber point dose in a phantom and the corresponding planned dose. The final model was deployed within a web application to predict PSQA outcomes of clinical plans within an existing clinical workflow. The application also displays relevant feature importance and plan-specific distribution analyses relative to database plans for documentation and to aid physicist interpretation and evaluation. VQA predictions were prospectively validated over 3 months of measurements at our clinic to assess safety and efficiency gains. RESULTS: Over 3 months, VQA predictions for 445 volumetric modulated arc therapy plans were prospectively validated at our institution. VQA predictions for these plans had a mean absolute error of 1.08% ± 0.77%, with a maximum absolute error of 2.98%. Using a 1% prediction threshold (ie, plans predicted to have an absolute error <1% would not require a measurement) would yield a 69.2% reduction in QA workload, saving 32.5 hours per month on average, with 81.5% sensitivity, 72.4% specificity, and an area under the curve of 0.81 at a 3% clinical threshold and 100% sensitivity, 70% specificity, and an area under the curve of 0.93 at a 4% clinical threshold. CONCLUSIONS: This is the first prospective clinical implementation and validation of VQA in the United States, which we observed to be efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for PSQA, leading to more effective allocation of clinical resources.

Bilevel Positive Airway Pressure Ventilation for Improving Respiratory Reproducibility in Radiation Oncology: A Pilot Study.

BACKGROUND: Strategies for managing respiratory motion, specifically motion-encompassing methods, in radiation therapy typically assume reproducible breathing. In reality, respiratory motion variations occur and ultimately cause tumor motion variations, which can result in differences between the planned and delivered dose distributions. Therefore, breathing guidance techniques have been investigated to improve respiratory reproducibility. To our knowledge, bilevel positive airway pressure (BIPAP) ventilation assistance has not been previously investigated as a technique for improving respiratory reproducibility and is the focus of this work. METHODS AND MATERIALS: Ten patients undergoing radiation therapy treatment for cancers affected by respiratory motion (eg, lung and esophagus) participated in sessions in which their breathing was recorded during their course of treatment; these sessions occurred either before or after radiation treatments. Both unassisted free-breathing (FB) and BIPAP ventilation-assisted respiratory volume data were collected from each patient using spirometry. Patients used 2 different BIPAP ventilators (fixed BIPAP and flexible BIPAP), each configured to deliver the same volume of air per breath (ie, tidal volume). The flexible BIPAP ventilator permitted patient triggering (ie, it permitted patients to initiate each breath), and the fixed BIPAP did not. Intrasession and intersession metrics quantifying tidal volume variations were calculated and compared between the specific breathing platforms (FB or BIPAP). In addition, patient tolerance of both BIPAP ventilators was qualitatively assessed through verbal feedback. RESULTS: Both BIPAP ventilators were tolerated by patients, although the fixed BIPAP was not as well tolerated as the flexible BIPAP. Both BIPAP ventilators showed significant reductions (P < .05) in intrasession tidal volume variation compared with FB. However, only the fixed BIPAP significantly reduced the intersession tidal volume variation compared with FB. CONCLUSIONS: Based on the established correlation between tidal volume and tumor motion, any reduction of the tidal volume variation could result in reduced tumor motion variation. Fixed BIPAP ventilation was found to be tolerated by patients and was shown to significantly reduce intrasession and intersession tidal volume variations compared with FB. Therefore, future investigation into the potential of fixed BIPAP ventilation is warranted to define the possible clinical benefits.

Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning.

INTRODUCTION: Previous literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique. MATERIALS AND METHODS: A support vector machine (SVM) was developed - using a database of 500 previous VMAT plans - to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed. RESULTS: Predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum). CONCLUSION: This study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique.

PURPOSE: Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided by an established in-house KBP system. The purpose of this work was to assess dosimetric, mechanical, and delivery properties of plans designed with a common KBP method for prostate cases treated via volumetric modulated arc therapy (VMAT). METHODS: Thirty-one prostate patients previously treated with VMAT were replanned with an in-house KBP method based on the overlap volume histogram. VMAT plan complexities of the KBP plans and the reference clinical plans were quantified via monitor units, modulation complexity scores, the edge metric, and average leaf motion per degree of gantry rotation. Each set of plans was delivered to the same diode array and agreement between computed and measured dose distributions was evaluated using the gamma index. Varying percent dose-difference (1-3%) and distance-to-agreement (1 mm to 3 mm) thresholds were assessed for gamma analyses. RESULTS: Knowledge-based planning (KBP) plans achieved average reductions of 6.4 Gy (P < 0.001) and 8.2 Gy (P < 0.001) in mean bladder and rectum dose compared to reference plans, while maintaining clinically acceptable target dose. However, KBP plans were significantly more complex than reference plans in each evaluated metric (P < 0.001). KBP plans also showed significant reductions (P < 0.05) in gamma passing rates at each evaluated criterion compared to reference plans. CONCLUSIONS: While KBP plans had significantly reduced bladder and rectum dose, they were significantly more complex and had significantly worse quality assurance outcomes than reference plans. These results suggest caution should be taken when implementing an in-house KBP technique.

Impact of database quality in knowledge-based treatment planning for prostate cancer.

PURPOSE: This article investigates dose-volume prediction improvements in a common knowledge-based planning (KBP) method using a Pareto plan database compared with using a conventional, clinical plan database. METHODS AND MATERIALS: Two plan databases were created using retrospective, anonymized data of 124 volumetric modulated arc therapy (VMAT) prostate cancer patients. The clinical plan database (CPD) contained planning data from each patient's clinically treated VMAT plan, which were manually optimized by various planners. The multicriteria optimization database (MCOD) contained Pareto-optimal plan data from VMAT plans created using a standardized multicriteria optimization protocol. Overlap volume histograms, incorporating fractional organ at risk volumes only within the treatment fields, were computed for each patient and used to match new patient anatomy to similar database patients. For each database patient, CPD and MCOD KBP predictions were generated for D10, D30, D50, D65, and D80 of the bladder and rectum in a leave-one-out manner. Prediction achievability was evaluated through a replanning study on a subset of 31 randomly selected database patients using the best KBP predictions, regardless of plan database origin, as planning goals. RESULTS: MCOD predictions were significantly lower than CPD predictions for all 5 bladder dose-volumes and rectum D50 (P = .004) and D65 (P < .001), whereas CPD predictions for rectum D10 (P = .005) and D30 (P < .001) were significantly less than MCOD predictions. KBP predictions were statistically achievable in the replans for all predicted dose-volumes, excluding D10 of bladder (P = .03) and rectum (P = .04). Compared with clinical plans, replans showed significant average reductions in Dmean for bladder (7.8 Gy; P < .001) and rectum (9.4 Gy; P < .001), while maintaining statistically similar planning target volume, femoral head, and penile bulb dose. CONCLUSION: KBP dose-volume predictions derived from Pareto plans were more optimal overall than those resulting from manually optimized clinical plans, which significantly improved KBP-assisted plan quality. SUMMARY: This work investigates how the plan quality of knowledge databases affects the performance and achievability of dose-volume predictions from a common knowledge-based planning approach for prostate cancer. Bladder and rectum dose-volume predictions derived from a database of standardized Pareto-optimal plans were compared with those derived from clinical plans manually designed by various planners. Dose-volume predictions from the Pareto plan database were significantly lower overall than those from the clinical plan database, without compromising achievability.

An improved distance-to-dose correlation for predicting bladder and rectum dose-volumes in knowledge-based VMAT planning for prostate cancer.

The overlap volume histogram (OVH) is an anatomical metric commonly used to quantify the geometric relationship between an organ at risk (OAR) and target volume when predicting expected dose-volumes in knowledge-based planning (KBP). This work investigated the influence of additional variables contributing to variations in the assumed linear DVH-OVH correlation for the bladder and rectum in VMAT plans of prostate patients, with the goal of increasing prediction accuracy and achievability of knowledge-based planning methods. VMAT plans were retrospectively generated for 124 prostate patients using multi-criteria optimization. DVHs quantified patient dosimetric data while OVHs quantified patient anatomical information. The DVH-OVH correlations were calculated for fractional bladder and rectum volumes of 30, 50, 65, and 80%. Correlations between potential influencing factors and dose were quantified using the Pearson product-moment correlation coefficient (R). Factors analyzed included the derivative of the OVH, prescribed dose, PTV volume, bladder volume, rectum volume, and in-field OAR volume. Out of the selected factors, only the in-field bladder volume (mean R = 0.86) showed a strong correlation with bladder doses. Similarly, only the in-field rectal volume (mean R = 0.76) showed a strong correlation with rectal doses. Therefore, an OVH formalism accounting for in-field OAR volumes was developed to determine the extent to which it improved the DVH-OVH correlation. Including the in-field factor improved the DVH-OVH correlation, with the mean R values over the fractional volumes studied improving from -0.79 to -0.85 and -0.82 to -0.86 for the bladder and rectum, respectively. A re-planning study was performed on 31 randomly selected database patients to verify the increased accuracy of KBP dose predictions by accounting for bladder and rectum volume within treatment fields. The in-field OVH led to significantly more precise and fewer unachievable KBP predictions, especially for lower bladder and rectum dose-volumes.