Improving treatment plan evaluation with automation

Covington, Elizabeth; Chen, Xiaoping; Younge, K. C.; Lee, Choonik; Matuszak, Martha M.; Kessler, Marc L.; Keranen, W; Acosta, Eduardo; Dougherty, Ashley; Filpansick, Stephanie; Moran, Jean M.

doi:10.1120/jacmp.v17i6.6322

Cited by 63 publications

(75 citation statements)

References 20 publications

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“…If the reference localization point is not set correctly, this results in incorrect shifts to the treatment isocenter. Many of these same error scenarios are outlined in previous studies …”

Section: Methodsmentioning

confidence: 79%

Utilizing simulated errors in radiotherapy plans to quantify the effectiveness of the physics plan review

et al. 2018

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Purpose The review of a radiation therapy plan by a physicist prior to treatment is a standard tool for ensuring the quality of treatments. However, little is known about how well this task is performed in practice. The goal of this study is to present a novel method to measure the effectiveness of physics plan review by introducing simulated errors into computerized “mock” treatment charts and measuring the performance of plan review by physicists. Methods We generated six simulated treatment charts containing multiple errors. To select errors, we compiled a list based on events from a departmental incident learning system and an international incident learning system (SAFRON). Seventeen errors with the highest scores for frequency and severity were included in the simulations included six mock treatment charts. Eight physicists reviewed the simulated charts as they would a normal pretreatment plan review, with each chart being reviewed by at least six physicists. There were 113 data points for evaluation. Observer bias was minimized using a simple error vs hidden error approach, using detectability scores for stratification. The confidence interval for the proportion of errors detected was computed using the Wilson score interval. Results Simulated errors were detected in 67% of reviews [58–75%] (95% confidence interval [CI] in brackets). Of the errors included in the simulated plans, the following error scenarios had the highest detection rates: an incorrect isocenter in DRR (93% [70–99%]), a planned dose different from the prescribed dose (92% [67–99%]) and invalid QA (85% [58–96%]). Errors with low detection rates included incorrect CT dataset (0%, [0–39%]) and incorrect isocenter localization in planning system (38% [18–64%]). Detection rates of errors from simulated charts were compared against observed detection rates of errors from a departmental incident learning system. Conclusions It has been notoriously difficult to quantify error and safety performance in oncology. This study uses a novel technique of simulated errors to quantify performance and suggests that the pretreatment physics plan review identifies some errors with high fidelity while other errors are more challenging to detect. These data will guide future work on standardization and automation. The example process studied here was physics plan review, but this approach of simulated errors may be applied in other contexts as well and may also be useful for training and education purposes.

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“…If the reference localization point is not set correctly, this results in incorrect shifts to the treatment isocenter. Many of these same error scenarios are outlined in previous studies …”

Section: Methodsmentioning

confidence: 79%

Utilizing simulated errors in radiotherapy plans to quantify the effectiveness of the physics plan review

et al. 2018

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“…As discussed in numerous publications, processes that require manual calculation or transcription are prone to error . The fault tree for providing wrong shifts to therapists by Ezzell et al shows three major pathways including “error in manually calculating or transcribing” shifts.…”

Section: Discussionmentioning

confidence: 99%

Technical Note: Use of automation to eliminate shift errors

Covington

Popple

Cardan

2020

J Applied Clin Med Phys

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Purpose To create automated tools within the treatment planning system (TPS) that eliminate the common error pathway of providing incorrect shift instructions to therapists. Materials/Methods Two scripts were created within the TPS using the Eclipse API (Varian Medical Systems, Palo Alto, CA). One script detects whether or not the user origin has been placed correctly at the intersection of the simulation markers while the other calculates a shift instruction sheet that can be printed for treatment. Results Analysis of our RO‐ILS database identified eight errors caused by improper setting of the user origin in the treatment planning system. The user origin script flagged all of the treatment plans for markers inconsistent with user origin. Automated calculation of shifts eliminated the error pathway of miscalculating or transcribing shift values. Conclusion Automation can eliminate the common error pathway of providing the wrong shifts to therapists. The scripts have been made available as open‐source software for implementation at other radiotherapy clinics.

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“…Despite the need to complete the activities within a reasonable timeframe, it is speculated that those activities that are identified as offline could be less prone to error due to reduced distraction and pressure from the presence of the patient and other team members. Currently, there are many investigations on the feasibility of automating volume segmentation, distribution generation and plan evaluation and quality control . Not only can automation reduce the human error in the process, it can improve efficiency, which in turn would enhance the cost‐effectiveness of any treatment strategy that entails more frequent adaptations.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, there are many investigations on the feasibility of automating volume segmentation, 22 distribution generation 23 and plan evaluation and quality control. 24 Not only can automation reduce the human error in the process, it can improve efficiency, which in turn would enhance the cost-effectiveness of any treatment strategy that entails more frequent adaptations.…”

Section: Discussionmentioning

confidence: 99%