Technical Note: Electronic chart checks in a paperless radiation therapy clinic

Yang, Deshan; Wu, Yu Te; Brame, R. Scott; Yaddanapudi, S; Rangaraj, D; Li, H. Harold; Goddu, S; Mutic, Sasa

doi:10.1118/1.4736825

Cited by 35 publications

(47 citation statements)

References 18 publications

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“…For example, such checks as verifying the correlation between prescription and plan and verifying accurate information transfer between plan and record and verify (R&V) system can be automated and have already been automated by some centers. [23][24][25][26] Trends across the three categories of classification (detected, potentially detectable, and not detectable) were investigated per year and tested for statistical significance using a cutoff p-value of 0.05.…”

Section: Methodsmentioning

confidence: 99%

The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

et al. 2016

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Pretreatment physics plan review is a key safety measure and can detect a high percentage of errors. However, the majority of errors that potentially could have been detected were not detected in this study, indicating the need to improve the pretreatment physics plan review performance. Suggestions for improvement include the automation of specific physics checks performed during the pretreatment physics plan review and the standardization of the review process.

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Section: Methodsmentioning

confidence: 99%

The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

et al. 2016

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“…In this study, the patient treatment delivery log files and the treatment plan data files are collected from the beam delivery and the planning stages respectively (4) . A total 120 log files were used and formed into a log file database.…”

Section: Methodsmentioning

confidence: 99%

“…In the US, half of men and one‐third of women will develop cancers during their lifetimes and about half of them will receive radiation treatments 1 , 2 , 3 . RT cancer treatments have developed from relatively simple processes into very complex procedures during the past two decades (4) . The ViewRay System (VRS) (i.e., MRIdian system;ViewRay Inc., Oakwood Village, OH) is an integrated magnetic resonance (MR) image‐guided radiation therapy system designed to provide simultaneous MR imaging (MRI) and external‐beam radiation therapy (EBRT) treatment (5) .…”

Section: Introductionmentioning

confidence: 99%

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning

Shi

Wooten

et al. 2016

J Applied Clin Med Phys

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A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image‐guided radiation therapy (MR‐IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam‐on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam‐on time can be calculated using both the planned beam‐on time and the decay‐corrected dose rate. To predict the remain‐ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity.PACS number(s): 87.55.N

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“…Other approaches include the use of portal imaging or in vivo dosimetry before or during the treatment to detect errors [12,18]. Some efforts have been made to develop systematic approaches for collecting, processing, and reporting incidents [12,13,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Treatment Planning Validation

Jiang

2015

Machine Learning in Radiation Oncology

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In this chapter, we will discuss the use of machine learning to detect errors in radiotherapy plans and charts. We will cover some general principles and established techniques for detecting errors in radiotherapy. We will discuss the rationale for using machine learning to detect large errors or outliers in radiotherapy treatment plans. As a concrete example, an automated error detection system for radiation treatment plans will be described. The technique was based on unsupervised machine learning, i.e., data clustering, and achieved over 90 % success rates in detecting outliers in over 1,000 treatment plans. Finally, future research directions in the clinical applications of machine learning for treatment planning validation will be briefly discussed.

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Technical Note: Electronic chart checks in a paperless radiation therapy clinic

Cited by 35 publications

References 18 publications

The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

The effectiveness of pretreatment physics plan review for detecting errors in radiation therapy

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning

Treatment Planning Validation

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