The impact of advancing the standard of care in radiotherapy on operational treatment resources.

PURPOSE: To demonstrate the impact of implementing hypofractionated prescription regimens and advanced treatment techniques on institutional operational hours and radiotherapy personnel resources in a multi-institutional setting. The study may be used to describe the impact of advancing the standard of care with modern radiotherapy techniques on patient and staff resources. METHODS: This study uses radiation therapy data extracted from the radiotherapy information system from two tertiary care, university-affiliated cancer centers from 2012 to 2021. Across all patients in the analysis, the average fraction number for curative and palliative patients was reported each year in the decade. Also, the institutional operational treatment hours are reported for both centers. A sub-analysis for curative intent breast and lung radiotherapy patients was performed to contextualize the impact of changes to imaging, motion management, and treatment technique. RESULTS: From 2012 to 2021, Center 1 had 42 214 patient plans and Center 2 had 43 252 patient plans included in the analysis. Averaged over both centers across the decade, the average fraction number per patient decreased from 6.9 to 5.2 (25%) and 21.8 to 17.2 (21%) for palliative and curative patients, respectively. The operational treatment hours for both institutions increased from 8 h 15 min to 9 h 45 min (18%), despite a patient population increase of 45%. CONCLUSION: The clinical implementation of hypofractionated treatment regimens has successfully reduced the radiotherapy workload and operational treatment hours required to treat patients. This analysis describes the impact of changes to the standard of care on institutional resources.

Advanced automated treatment planning for total body irradiation: Implementation and effects on standardization.

PURPOSE: This work describes the automation of our volumetric modulated arc therapy (VMAT) total body irradiation (TBI) treatment planning. It also aims to determine if plan standardization is impacted by automation. METHODS: We introduced automated beam placement for TBI in March 2021. For manual beam placement pre-2021, Python-modified DICOM files were imported to pre-set cumulative meterset weights, with other parameters selected by dosimetrists. Our automated planning script automates these processes and sets gantry stop angles and isocentre placement. To determine the impact of automation on plan standardization, we performed a retrospective review of a matched cohort of 168 patients. Plan parameters were compared with an external standard, and passing rates compared between patient cohorts. The dosimetric impact was investigated by comparing a Body-5 mm homogeneity index (HI = D2%/D98%) and mean lung dose (MLD) between cohorts. RESULTS: Results are listed for manual and automated groups respectively. Median (range) passing rates were 97.7% (96.1-100) and 99.2% (98.3-100). Automated plans had a significantly higher passing rate (p â‰ª 0.05) and smaller variance (p = 0.001). Most failures were attributed to human error. Automated plans also had more consistent parameter identifiers. After considering dimensional outliers, median (range) Body-5 mm HI were 1.18 (1.14-1.23) and 1.18 (1.15-1.26), and mean ± standard deviation MLD were 103.8 ± 1.3% and 104.1 ± 0.9%. Variances were not significantly different between Body-5 mm HI (p = 0.092) but were for MLD (p = 0.013). CONCLUSIONS: Implementation of automated planning in TBI resulted in significantly improved plan standardization. The decrease in variance of the MLD for the automated planning group points towards a potential dosimetric benefit of automation.

An updated approach for deriving PTV margins using image guidance and deformable dose accumulation.

Objective.To demonstrate an updated approach for deriving planning target volume (PTV) margins for a patient population treated with volumetric image-guided radiotherapy.Approach.The approach uses a semi-automated workflow within commercial radiotherapy applications that combines dose accumulation with the bidirectional local distance (BLD) metric. The patient cohort is divided into derivation and validation datasets. For each patient in the derivation dataset, a treatment plan is generated with a 0 mm PTV margin (the idealized treatment scenario without the influence of the standard margin). Deformable image registration enabled dose accumulation of these zero-margin plans. PTV margins are derived by using the BLD to calculate the geometric extent of underdosed regions of the clinical target volume (CTV). The PTV margin is validated by ensuring the specified CTV coverage criterion is met when the margin is applied to the validation dataset.Main results.The methodology was applied to two cohorts: 40 oropharyngeal cancer patients and 50 early-stage breast cancer patients. Ten patients from each cohort were used for validation. PTV margins derived for the oropharyngeal and early-stage breast cancer patient cohorts were 3 and 5 mm, respectively, and ensure that 95% of the prescription dose is delivered to 98% of the CTV for 90% of patients. Dose accumulation showed that the CTV coverage criterion was achieved for at least 90% of patients when the margins were applied.Significance.This methodology can be used to derive appropriate PTV margins for realistic treatment scenarios and any disease site, which will improve our understanding of patient outcomes.

Increasing Demand on Human Capital and Resource Utilization in Radiation Therapy: The Past Decade.

PURPOSE: To quantify the change resource utilization in radiation therapy in the context of advancing technologies and techniques over the last decade. METHODS AND MATERIALS: Prospectively, the time to complete radiation therapy workflow tasks was captured between January 1, 2020, and December 31, 2020. The institutional task workflows are specific to each technique and broadly organized into 4 categories: 3-dimenstional conformal radiation therapy, intensity modulated radiation therapy, volumetric modulated arc therapy simple, and volumetric modulated arc therapy complex. These discipline-specific task times were used to quantify a resource utilization factor, which is the median time taken to complete all tasks for each category divided by the median time for 3-dimensional conformal radiation therapy treatments. Retrospectively, all plans treated between January 1, 2012, and December 31, 2019, were quantified and categorized. The resource factor was applied to determine resource utilization. For context, institutional staffing levels were captured across the same decade for medical dosimetrists, medical physicists, and radiation oncologists. RESULTS: This analysis includes 30,229 patient plans in the retrospective data set and 4747 patient plans in the prospective data set. This analysis demonstrates that over this period, patient numbers increased by approximately 45%, whereas time-based human resources increased by almost 150%. The resource allocation factors for 3-dimenstional conformal radiation therapy, intensity modulated radiation therapy, volumetric modulated arc therapy simple, and volumetric arc therapy complex were 1.0, 2.4, 2.9, and 4.3, respectively. Across the 3 disciplines, staffing levels increased from 15 to 17 (13%) for medical dosimetrists, from 10 to 13 (30%) for medical physicists, and from 16 to 23 (44%) for radiation oncologists. CONCLUSIONS: This work demonstrates the increase in resource utilization due to the introduction of advanced technologies and changes in radiation therapy techniques over the past decade. Human resource utilization is the predominant factor and should be considered with increasing patient volume for operational planning.

Determining Clinical Patient Selection Guidelines for Head and Neck Adaptive Radiation Therapy Using Random Forest Modelling and a Novel Simplification Heuristic.

PURPOSE: To determine which head and neck adaptive radiotherapy (ART) correction objectives are feasible and to derive efficient ART patient selection guidelines. METHODS: We considered various head and neck ART objectives including independent consideration of dose-sparing of the brainstem/spinal cord, parotid glands, and pharyngeal constrictor, as well as prediction of patient weight loss. Two-hundred head and neck cancer patients were used for model development and an additional 50 for model validation. Patient chart data, pre-treatment images, treatment plans, on-unit patient measurements, and combinations thereof were assessed as potential predictors of each objective. A stepwise approach identified combinations of predictors maximizing the Youden index of random forest (RF) models. A heuristic translated RF results into simple patient selection guidelines which were further refined to balance predictive capability and practical resource costs. Generalizability of the RF models and simplified guidelines to new data was tested using the validation set. RESULTS: Top performing RF models used various categories of predictors, however, final simplified patient selection guidelines only required pre-treatment information for ART predictions, indicating the potential for significant ART process streamlining. The simplified guidelines for each objective predicted which patients would experience increases in dose to: brainstem/spinal cord with sensitivity = 1.0, specificity = 0.66; parotid glands with sensitivity = 0.82, specificity = 0.70; and pharyngeal constrictor with sensitivity = 0.84, specificity = 0.68. Weight loss could be predicted with sensitivity = 0.60 and specificity = 0.55. Furthermore, depending on the ART objective, 28%-58% of patients required replan assessment, less than for previous studies, indicating a step towards more effective patient selection. CONCLUSIONS: The above ART objectives appear to be practically achievable, with patients selected for ART according to simple clinical patient selection guidelines. Explicit ART guidelines are rare in the literature, and our guidelines may aid in balancing the potential clinical gains of ART with high associated resource costs, formalizing ART trials, and ensuring the reproducibility of clinical successes.

Technical Note: A standardized automation framework for monitoring institutional radiotherapy protocol compliance.

PURPOSE: To establish a framework for the standardization of monitoring radiotherapy protocol compliance. METHODS: An automated protocol compliance tool was developed using best practice in software design and a flexible framework to easily adapt to changing institutional standards. The Eclipse scripting environment was used to develop the application with the scripting application programing interface (API) and direct data extraction from ARIA. For each institutional protocol, external validation was specified in a JavaScript Object Notation (JSON) file that stores protocol specific constraints and evaluates compliance of the data from Eclipse and Aria. This tool was applied prospectively to a cohort of prostate cancer patients undergoing radiotherapy with a prescription regimen of 60 Gy in 20 fractions. RESULTS: The prospective evaluation was performed on 58 prostate cancer patients. For this cohort, the mean (standard deviation) pass rate is 92.3% (6.1%). The overall fail rate is 6.0% (5.8%); the percentage of these failures is in 2.6% in Patient Assessment, 0% in Simulation, and 97.4% in Treatment Planning. CONCLUSIONS: A protocol compliance application is developed and implemented in a standard radiotherapy information system. The application functionality is demonstrated on a cohort of 58 patients undergoing prostate radiotherapy, which highlights the utility of assessing adherence to institutional protocols. A unified method must be available for the community to ensure consistency in compliance reporting.