Eliminating Inconsistencies in Simulation and Treatment Planning Orders in Radiation Therapy

Santanam, Lakshmi; Brame, R. Scott; Lindsey, Andrew; DeWees, T.A.; Danieley, J.; LaBrash, Jason; Parikh, Parag J.; Bradley, Jeffrey D.; Zoberi, Imran; Michalski, Jeff M.; Mutic, Sasa

doi:10.1016/j.ijrobp.2012.03.023

Cited by 17 publications

(18 citation statements)

References 10 publications

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“…The estimation of Θ n and A n is similar to that of the estimation of Θ k and B k shown in Eqs. (5)- (7). Then, the closest distance for each point on the given surface with respect to the fitted GAD model surface is calculated, and the mean distance E i,c of all the surface points on the cth slice is obtained as…”

Section: C Supervised Contouring Error Detection Strategymentioning

confidence: 99%

“…These errors include mislabeling, missed slices, overlapped structures, isolated voxels or small regions, extremely large or small slices, and structure volume and location distortions. [5][6][7] The contouring errors are major patient safety issues in radiation therapy because they degrade dose distributions, dosimetry analyses, and contour-based visual guidance used in image-guided procedures. [7][8][9][10][11][12] High rates of OAR contouring errors have recently been reported, strongly suggesting the need for effective and practical peer review.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] The contouring errors are major patient safety issues in radiation therapy because they degrade dose distributions, dosimetry analyses, and contour-based visual guidance used in image-guided procedures. [7][8][9][10][11][12] High rates of OAR contouring errors have recently been reported, strongly suggesting the need for effective and practical peer review. 7,[11][12][13][14][15][16][17][18][19][20] While peer review may be practical for larger clinics, smaller clinics will be challenged to develop such a review process due to limited staffing.…”

Section: Introductionmentioning

confidence: 99%

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Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

et al. 2015

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The proposed strategy can reliably identify contouring errors based upon inter- and intrastructural constraints derived from clinically approved contours. It holds great potential for improving the radiation therapy workflow. ROC and box plot analyses allow for analytically tuning of the system parameters to satisfy clinical requirements. Future work will focus on the improvement of strategy reliability by utilizing more training sets and additional geometric attribute constraints.

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Section: C Supervised Contouring Error Detection Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

et al. 2015

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“…Electronic physician ordering and whiteboard systems are also an effective means for mitigating communication failures. 8,9 Quality control measures for dataset fusion include both automated fusion tools and trained manual inspection. 10 Although automated segmentation tools will be commonplace for online ART to increase efficiency and reduce human error, automated quality control software is necessary for error detection.…”

Section: D Quality Control Strategiesmentioning

confidence: 99%

Process‐based quality management for clinical implementation of adaptive radiotherapy

et al. 2014

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Purpose: Intensity-modulated adaptive radiotherapy (ART) has been the focus of considerable research and developmental work due to its potential therapeutic benefits. However, in light of its unique quality assurance (QA) challenges, no one has described a robust framework for its clinical implementation. In fact, recent position papers by ASTRO and AAPM have firmly endorsed pretreatment patient-specific IMRT QA, which limits the feasibility of online ART. The authors aim to address these obstacles by applying failure mode and effects analysis (FMEA) to identify high-priority errors and appropriate risk-mitigation strategies for clinical implementation of intensity-modulated ART. Methods: An experienced team of two clinical medical physicists, one clinical engineer, and one radiation oncologist was assembled to perform a standard FMEA for intensity-modulated ART. A set of 216 potential radiotherapy failures composed by the forthcoming AAPM task group 100 (TG-100) was used as the basis. Of the 216 failures, 127 were identified as most relevant to an ART scheme. Using the associated TG-100 FMEA values as a baseline, the team considered how the likeliness of occurrence (O), outcome severity (S), and likeliness of failure being undetected (D) would change for ART. New risk priority numbers (RPN) were calculated. Failures characterized by RPN ≥ 200 were identified as potentially critical. Results: FMEA revealed that ART RPN increased for 38% (n = 48/127) of potential failures, with 75% (n = 36/48) attributed to failures in the segmentation and treatment planning processes. Fortythree of 127 failures were identified as potentially critical. Risk-mitigation strategies include implementing a suite of quality control and decision support software, specialty QA software/hardware tools, and an increase in specially trained personnel. Conclusions: Results of the FMEA-based risk assessment demonstrate that intensity-modulated ART introduces different (but not necessarily more) risks than standard IMRT and may be safely implemented with the proper mitigations.

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“…Inconsistencies or poorly chosen naming practices in a radiation oncology clinic may lead to confusion in the interpretation of important details, which may cause patient treatment errors or the misadministration of therapy 13 , 14 , 15 , 16 , 17 . It has been reported in the literature that lack of naming standardization contributes to the incident of event reporting via mechanisms such as PSRS (18) .…”

Section: Introductionmentioning

confidence: 99%

Guidelines for treatment naming in radiation oncology

Denton¹,

Shields

Hahl

et al. 2015

J Applied Clin Med Phys

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Safety concerns may arise from a lack of standardization and ambiguity during the treatment planning and delivery process in radiation therapy. A standardized target and organ‐at‐risk naming convention in radiation therapy was developed by a task force comprised of several Radiation Oncology Societies. We present a nested‐survey approach in a community setting to determine the methodology for radiation oncology departments to standardize their practice. Our Institution's continuous quality improvement (CQI) committee recognized that, due to growth from one to three centers, significant variability existed within plan parameters specific to patients’ treatment. A multidiscipline, multiclinical site consortium was established to create a guideline for standard naming. Input was gathered using anonymous, electronic surveys from physicians, physicists, dosimetrists, chief therapists, and nurse managers. Surveys consisted of several primary areas of interest: anatomical sites, course naming, treatment plan naming, and treatment field naming. Additional concepts included capitalization, specification of laterality, course naming in the event of multiple sites being treated within the same course of treatment, primary versus boost planning, the use of bolus, revisions for plans, image‐guidance field naming, forbidden characters, and standard units for commonly used physical quantities in radiation oncology practice. Guidelines for standard treatment naming were developed that could be readily adopted. This multidisciplinary study provides a clear, straightforward, and easily implemented protocol for the radiotherapy treatment process. Standard nomenclature facilitates the safe means of communication between team members in radiation oncology. The guidelines presented in this work serve as a model for radiation oncology clinics to standardize their practices.PACS number(s): 87.56.bd, 87.56.Fc, 87.55.Qr, 87.55.‐x, 87.55.N‐, 87.55.T‐, 87.55.D‐

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Eliminating Inconsistencies in Simulation and Treatment Planning Orders in Radiation Therapy

Cited by 17 publications

References 10 publications

Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

Process‐based quality management for clinical implementation of adaptive radiotherapy

Guidelines for treatment naming in radiation oncology

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