ABSTRACT
Purpose or Objective The COVID-19 pandemic forced radiation oncology departments to alter clinical workflows to reduce exposure risks in the clinic. Performing patient-specific quality assurance (PSQA) is one of the most resource intensive and time-consuming tasks. With technological advancements in radiotherapy treatment planning and quality assurance, research towards measurement-free PSQA has become a focus within the field. Most of these techniques involve modeling the relationship between treatment plan complexity and corresponding PSQA outcomes. However, to our knowledge, none of these efforts have been assessed and prospectively validated for clinical use. We implemented and a machine learning-based virtual VMAT QA (VQA) workflow to assess the safety and workload reduction of measurement-free patient-specific QA at a multi-site institution in light of COVID-19. Materials and Methods An XGBoost machine learning model was trained and tuned to predict QA outcomes of VMAT plans, represented as percent differences between the planned dose and measured ion chamber point dose in a phantom. The model was developed using a dataset of 579 previous clinical VMAT plans and associated QA measurements from our institution. 30 classes of complexity features were extracted from each VMAT plan and used as input for the model, which was tuned using a grid search over learning rate and tree depth hyperparameters and evaluated with 10-fold cross-validation. The final model was implemented within a webbased VQA application to predict QA outcomes of clinical plans within our existing clinical workflow. The application also displays relevant plan-specific feature importance and nearest neighbor analyses relative to database plans for physicist evaluation and documentation (Figure 1). VQA predictions were prospectively validated over one month of measurements at our clinic to assess the safety and efficiency gains of clinical implementation. $Φg Results 147 VMAT plans were measured at our institution over the course of one month, taking an average of approximately 20 minutes per plan for QA. VQA predictions for these plans had a mean absolute error of 0.97 +/- 0.69%, with a maximum absolute error of 2.75% (Figure 2). Employing a prediction decision threshold of 1% - meaning plans with absolute predictions of less than 1% would not need measurements - would flag all plans that may have ion chamber disagreements greater than 4%. This translates to a 73% reduction in QA workload in terms of time. A more conservative implementation of this workflow, where all SBRT plans will continue to be measured, would still result in a 46% reduction in QA workload. $Φg Conclusion To our knowledge, this is the first prospective clinical implementation and validation of VQA, which we observed to be safe and efficient. Using a conservative threshold, VQA can substantially reduce the number of required measurements for patient-specific QA, leading to a more effective allocation of clinical resources.
ABSTRACT
The 2019 coronavirus pandemic (COVID-19) had a broad impact on the care of cancer patients, including the rapid adoption of telehealth video visits. On March 15, 2020, our institutional leadership recommended transition to video visits, which posed a risk of altering patients' access to care. We assessed the impact of this rapid transition on demographic patterns in an urban academic radiation oncology department. Consultation and follow-up visits from the pre-COVID-19 period (January 1, 2019 to March 14, 2020) and COVID-19 period (March 15, 2020 to August 31, 2020) in a single radiation oncology department were identified. Demographics and appointment data were abstracted from the institutional electronic health record. Time trends and patient and visit characteristics were compared across the pre-COVID-19 and COVID-19 periods. During the study period, 9,450 consult and follow-up visits were performed pre-COVID-19 and 3,298 visits in the COVID-19 period. The proportion of video visits increased markedly in the transition period, from 0.6% of all visits in the week of March 2, 2020, to 87% in the week of March 23, 2020. In-person visits decreased from 98% to 3%. Among all visits (in-person and telehealth), those during the COVID-19 period were less likely to be new consultations (43.1% from 60.1%;P < 0.001). There was a small and significant increase in the proportion of visits with patients who: identified as white (61.8% from 58.4%, P = 0.019), spoke English as their primary language (91.3% from 89.4%, P = 0.002), and had commercial insurance (34.1% from 32.0%;P = 0.009). The overall COVID-19 clinic population retained demographic features similar to the pre-COVID-19 population despite a very rapid near-complete transition to telehealth. Nonetheless, the telehealth-predominant COVID-19 period had slightly increased visits with patients who were white or primarily English speaking or had commercial insurance. Strategies for ensuring telehealth is accessible to diverse populations should be a priority as telemedicine is integrated into long-term clinical operations. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Radiation Oncology, Biology, Physics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
PURPOSE: Our purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019. METHODS AND MATERIALS: In this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements. RESULTS: In the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement. CONCLUSIONS: It is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.