Optimizing Efficiency and Safety in External Beam Radiotherapy Using Automated Plan Check (APC) Tool and Six Sigma Methodology

Liu, S.; Bush, Karl; Bertini, J.; Fu, Yabo; Lewis, J. K.; Pham, Daniel; Yang, Yun-Jung; Niedermayr, Thomas; Skinner, Lawrie; Li, Xing; Beadle, Beth M.; Hsu, Annie; Kovalchuk, Nataliya

doi:10.1016/j.ijrobp.2019.06.422

Cited by 3 publications

(6 citation statements)

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“…We expect that clinical use of our chart review solution will provide data on the frequency of different types of errors encountered as has been reported in other studies 12,13,24,23 . This will permit subsequent data mining for a more complete risk assessment, and concomitant risk reduction 25 .…”

Section: Discussionmentioning

confidence: 85%

“…The combination of information on the source, impact and severity of an inconsistency allows us to assess the risk posed by a given error. The dependency information obtained from the graph is useful toward a failure modes and effects analysis (FMEA) 18,12 of the TPCR process or error pathway analysis as performed by Ezzel et al. on RO‐ILS data 23 since it allows automatic identification of the cause and effect of an inconsistency at different degrees of connection and across different modules and subprocesses.…”

Section: Discussionmentioning

confidence: 99%

“…A key recommendation of AAPM Task Group 275, the objective of which was to provide recommendations on the physics chart review, is the development of automated tools to assist with the chart review task 1 . Most current automated and semi‐automated chart review tools are similar to an automated checklist to verify the integrity of variables in a specific module or their consistency across software components used to create the treatment plan based on predefined rules 5–14 . Several studies have shown the use of such tools to increase efficiency and patient safety and to reduce the rate of plan revisions, avoidable safety events, and patient delays 10,11,13 …”

Section: Introductionmentioning

confidence: 99%

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Graph‐based risk assessment and error detection in radiation therapy

2021

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Purpose The objective of this study was to formalize and automate quality assurance (QA) in radiation oncology. Quality assurance in radiation oncology entails a multistep verification of complex, personalized radiation plans to treat cancer involving an interdisciplinary team and high technology, multivendor software and hardware systems. We addressed the pretreatment physics chart review (TPCR) using methods from graph theory and constraint programming to study the effect of dependencies between variables and automatically identify logical inconsistencies and how they propagate. Materials and methods We used a modular approach to decompose the TPCR process into tractable units comprising subprocesses, modules and variables. Modules represented the main software entities comprised in the radiation treatment planning workflow and subprocesses grouped the checks to be performed by functionality. Module‐associated variables served as inputs to the subprocesses. Relationships between variables were modeled by means of a directed graph. The detection of errors, in the form of inconsistencies, was formalized as a constraint satisfaction problem whereby checks were encoded as logical formulae. The sequence in which subprocesses were visited was described in an activity diagram. Results The comprehensive model for the TPCR process comprised 5 modules, 19 subprocesses and 346 variables, 225 of which were distinct. Modules included “Treatment Planning System” and “Record and Verify System.” Subprocesses included “Dose Prescription,” “Documents,” “CT Integrity,” “Anatomical Contours,” “Beam Configuration,” “Dose Calculation,” “3D Dose Distribution Quality,” and “Treatment Approval.” Variable inconsistencies, and their source and propagation were determined by checking for constraint violation and through graph traversal. Impact scores, obtained through graph traversal, combined with severity scores associated with an inconsistency, allowed risk assessment. Conclusions Directed graphs combined with constraint programming hold promise for formalizing complex QA processes in radiation oncology, performing risk assessment and automating the TPCR process. Though complex, the process is tractable.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph‐based risk assessment and error detection in radiation therapy

2021

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“…Therefore, we suggest that a probabilistic dose uncertainty model be considered by TPS vendors to build in the future a robust beam optimization module. In the clinic, several TPS systems such as Eclipse and Raystation (RaySearch Laboratories, Stockholm, Sweden) provide a scripting function that can access and modify the treatment plans [ 26 ] as well as assess the plan qualities [ 27 ]. The scripting application can be used to establish a workflow that can apply the setup shifts to the base plan, recalculate and generate the probability-weighted plan sum, and assess the plan sum quality.…”

Section: Discussionmentioning

confidence: 99%

A Probability-Based Investigation on the Setup Robustness of Pencil-beam Proton Radiation Therapy for Skull-Base Meningioma

Zou

Kurtz

Nakib

et al. 2021

International Journal of Particle Therapy

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Introduction The intracranial skull-base meningioma is in proximity to multiple critical organs and heterogeneous tissues. Steep dose gradients often result from avoiding critical organs in proton treatment plans. Dose uncertainties arising from setup errors under image-guided radiation therapy are worthy of evaluation. Patients and Methods Fourteen patients with skull-base meningioma were retrospectively identified and planned with proton pencil beam scanning (PBS) single-field uniform dose (SFUD) and multifield optimization (MFO) techniques. The setup uncertainties were assigned a probability model on the basis of prior published data. The impact on the dose distribution from nominal 1-mm and large, less probable setup errors, as well as the cumulative effect, was analyzed. The robustness of SFUD and MFO planning techniques in these scenarios was discussed. Results The target coverage was reduced and the plan dose hot spot increased by all setup uncertainty scenarios regardless of the planning techniques. For 1 mm nominal shifts, the deviations in clinical target volume (CTV) coverage D99% was −11 ± 52 cGy and −45 ± 147 cGy for SFUD and MFO plans. The setup uncertainties affected the organ at risk (OAR) dose both positively and negatively. The statistical average of the setup uncertainties had <100 cGy impact on the plan qualities for all patients. The cumulative deviations in CTV D95% were 1 ± 34 cGy and −7 ± 18 cGy for SFUD and MFO plans. Conclusion It is important to understand the impact of setup uncertainties on skull-base meningioma, as the tumor target has complex shape and is in proximity to multiple critical organs. Our work evaluated the setup uncertainty based on its probability distribution and evaluated the dosimetric consequences. In general, the SFUD plans demonstrated more robustness than the MFO plans in target coverages and brainstem dose. The probability-weighted overall effect on the dose distribution is small compared to the dosimetric shift during single fraction.

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“…Other recently published papers describe and quantify time savings and error reduction using different analyses. 21,[32][33][34] A review of these new techniques and publications is instructive in looking at the future of initial chart check automation.…”

mentioning

confidence: 99%

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

Zhang

Guerrero

et al. 2021

J Applied Clin Med Phys

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Purpose: The recently published AAPM TG-275 and the public review version of TG-315 list new recommendations for comprehensive and minimum physics initial chart checks, respectively. This article addresses the potential development and benefit of initial chart check automation when these recommendations are implemented for clinical photon/electron EBRT. Methods: Eight board-certified physicists with 2-20 years of clinical experience performed initial chart checks using checklists from TG-275 and TG-315. Manual check times were estimated for three types of plans (IMRT/VMAT, 3D, and 2D) and for prostate, whole pelvis, lung, breast, head and neck, and brain cancers. An expert development team of three physicists re-evaluated the automation feasibility of TG-275 checklist based on their experience of developing and implementing the in-house and the commercial automation tools in our institution. Three levels of initial chart check automation were simulated: (1) Auto_UMMS_tool (which consists of in-house program and commercially available software); (2) Auto_TG275 (with full and partial automation as indicated in TG-275); and (3) Auto_UMMS_exp (with full and partial automation as determined by our experts' re-evaluation). Results: With no automation of initial chart checks, the ranges of manual check times were 29-56 min (full TG-315 list) and 102-163 min (full TG-275 list), which varied significantly with physicists but varied little at different tumor sites. The 69 of 71 checks which were considered as "not fully automated" in TG-275 were reevaluated with more automation feasibility. Compared to no automation, the higher levels of automation yielded a great reduction in both manual check times (by 44%-98%) and potentially residual detectable errors (by 15-85%). Conclusion: The initial chart check automation greatly improves the practicality and efficiency of implementing the new TG recommendations. Revisiting the TG reports with new technology/practice updates may help develop and utilize more automation clinically.

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Optimizing Efficiency and Safety in External Beam Radiotherapy Using Automated Plan Check (APC) Tool and Six Sigma Methodology

Cited by 3 publications

References 0 publications

Graph‐based risk assessment and error detection in radiation therapy

Graph‐based risk assessment and error detection in radiation therapy

A Probability-Based Investigation on the Setup Robustness of Pencil-beam Proton Radiation Therapy for Skull-Base Meningioma

Toward automation of initial chart check for photon/electron EBRT: the clinical implementation of new AAPM task group reports and automation techniques

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