Radiation therapy plan checks in a paperless clinic

Siochi, R; Pennington, Edward C.; Waldron, Timothy J.; Bayouth, John E.

doi:10.1120/jacmp.v10i1.2905

Cited by 59 publications

(59 citation statements)

References 7 publications

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“…New in vivo dosimetry techniques 30 have also been proposed to eliminate the need for measurements prior to the commencement of treatment. Different pretreatment filtration methods 23,31,32 could allow for the identification of gross errors prior to measurement, eliminate the need for certain measurements by establishing thresholds for which good results are statistically expected, or by improving the workflow by more quickly identifying potential errors. A complexity metric that was related to deliverability could potentially allow for a reduction in the quantity of patient-specific QA measurements, or when used as a filtering tool, a score could identify plans that require specific attention due to a score outside of a certain threshold.…”

Section: Introductionmentioning

confidence: 99%

A new metric for assessing IMRT modulation complexity and plan deliverability

2010

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Section: Introductionmentioning

confidence: 99%

A new metric for assessing IMRT modulation complexity and plan deliverability

2010

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“…Using automated QA techniques for plan review has been proposed by several groups . Here, we incorporated such checks for the consistency and reasonability of the treatment planning parameters as part of our QA program.…”

Section: Discussionmentioning

confidence: 99%

A risk assessment of automated treatment planning and recommendations for clinical deployment

et al. 2019

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Purpose: To assess the risk of failure of a recently developed automated treatment planning tool, the Radiation Planning Assistant (RPA) and to determine the reduction in these risks with implementation of a quality assurance (QA) program specifically designed for the RPA. Methods: We used failure mode and effects analysis (FMEA) to assess the risk of the RPA. The steps involved in the workflow of planning a 4-field box treatment of cervical cancer with the RPA were identified. Then, the potential failure modes at each step and their causes were identified and scored according to their likelihood of occurrence, severity, and likelihood of going undetected. Additionally, the impact of the components of the QA program on the detectability of the failure modes was assessed. The QA program was designed to supplement a clinic’s standard QA processes and consisted of 3 components: (1) automatic, independent verification of the results of automated planning; (2) automatic comparison of treatment parameters to expected values; and (3) guided manual checks of the treatment plan. A risk priority number (RPN) was calculated for each potential failure mode with and without use of the QA program. Results: In the RPA automated treatment planning workflow, we identified 68 potential failure modes with 113 causes. The average RPN was 91 without the QA program and 68 with the QA program (maximum RPNs were 504 and 315, respectively). The reduction in RPN was due to an improvement in the likelihood of detecting failures, resulting in lower detectability scores. The top-ranked failure modes included incorrect identification of the marked isocenter, inappropriate beam aperture definition, incorrect entry of the prescription into the RPA plan directive, and lack of a comprehensive plan review by the physician. Conclusions: Using FMEA, we assessed the risks in the clinical deployment of an automated treatment planning workflow and showed that a specialized QA program for the RPA, which included automatic QA techniques, improved the detectability of failures, reducing this risk. However, some residual risks persisted, which were similar to those found in manual treatment planning, and human error remained a major cause of potential failures. Through the risk analysis process, we identified 3 key aspects of safe deployment of automated planning: (1) user training on potential failure modes; (2) comprehensive manual plan review by physicians and physicists; and (3) automated QA of the treatment plan.

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“…CERR can be used to import and review [ 33 ] treatment plans and is compatible with multiple treatment planning systems. CERR is available online [ 34 ].…”

Section: Dicom-rtmentioning

confidence: 99%

Informatics in Radiation Oncology

Putora

Peters

Bovet

2015

Machine Learning in Radiation Oncology

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Radiation oncology informatics includes informatics from the perspectives of every discipline involved in radiation oncology. As there are many open questions and an abundance of data, machine learning technologies can be valuable. Available data includes handwritten notes on paper, imaging data available in digital formats, radiation treatment plan details, fi nancial data, and multilevel multicenter databases, to name a few. Tools of various complexity for various goals are available. The following chapter aims to portray this domain and present a selection of available tools. IntroductionRadiation oncology (RO) is the discipline dealing with the treatment of cancer with ionizing radiation (radiation therapy) for cure or palliation. Radiation therapy is often applied in combination with other treatment modalities (chemotherapy, surgery, hormonal therapy, etc.). Radiation therapy is typically delivered with linear accelerators (linacs); although other modalities and devices exist, linac-based treatments represent the vast majority of radiotherapy treatments in modern radiation oncology units.

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Radiation therapy plan checks in a paperless clinic

Cited by 59 publications

References 7 publications

A new metric for assessing IMRT modulation complexity and plan deliverability

A new metric for assessing IMRT modulation complexity and plan deliverability

A risk assessment of automated treatment planning and recommendations for clinical deployment

Informatics in Radiation Oncology

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