Analysis of human errors in the operation of various treatment planning systems over a 10-year period.

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors.

Strategies used to detect and mitigate system-related errors over time: A qualitative study in an Australian health district.

BACKGROUND: Electronic medical record (EMR) systems provide timely access to clinical information and have been shown to improve medication safety. However, EMRs can also create opportunities for error, including system-related errors or errors that were unlikely or not possible with the use of paper medication charts. This study aimed to determine the detection and mitigation strategies adopted by a health district in Australia to target system-related errors and to explore stakeholder views on strategies needed to curb future system-related errors from emerging. METHODS: A qualitative descriptive study design was used comprising semi-structured interviews. Data were collected from three hospitals within a health district in Sydney, Australia, between September 2020 and May 2021. Interviews were conducted with EMR users and other key stakeholders (e.g. clinical informatics team members). Participants were asked to reflect on how system-related errors changed over time, and to describe approaches taken by their organisation to detect and mitigate these errors. Thematic analysis was conducted iteratively using a general inductive approach, where codes were assigned as themes emerged from the data. RESULTS: Interviews were conducted with 25 stakeholders. Participants reported that most system-related errors were detected by front-line clinicians. Following error detection, clinicians either reported system-related errors directly to the clinical informatics team or submitted reports to the incident information management system. System-related errors were also reported to be detected via reports run within the EMR, or during organisational processes such as incident investigations or system enhancement projects. EMR redesign was the main approach described by participants for mitigating system-related errors, however other strategies, like regular user education and minimising the use of hybrid systems, were also reported. CONCLUSIONS: Initial detection of system-related errors relies heavily on front-line clinicians, however other organisational strategies that are proactive and layered can improve the systemic detection, investigation, and management of errors. Together with EMR design changes, complementary error mitigation strategies, including targeted staff education, can support safe EMR use and development.

Time-resolved clinical dose volume metrics, calculations and predictions based on source tracking measurements and uncertainties to aid treatment verification and error detection for HDR brachytherapy-a proof-of-principle study.

Objective.High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom.Approach.Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of thein vivodosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification.Main results.The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time.Significance.Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

Delta4-based Dosimetric Error Detection in Volumetric-modulated Arc Therapy: Clinical Significance and Implications.

Background: Volumetric-modulated arc therapy (VMAT) is an efficient method of administering intensity-modulated radiotherapy beams. The Delta4 device was employed to examine patient data. Aims and Objectives: The utility of the Delta4 device in identifying errors for patient-specific quality assurance of VMAT plans was studied in this research. Materials and Methods: Intentional errors were purposely created in the collimator rotation, gantry rotation, multileaf collimator (MLC) position displacement, and increase in the number of monitor units (MU). Results: The results show that when the characteristics of the treatment plans were changed, the gamma passing rate (GPR) decreased. The largest percentage of erroneous detection was seen in the increasing number of MU, with a GPR ranging from 41 to 92. Gamma analysis was used to compare the dose distributions of the original and intentional error designs using the 2%/2 mm criteria. The percentage of dose errors (DEs) in the dose-volume histogram (DVH) was also analyzed, and the statistical association was assessed using logistic regression. A modest association (Pearson's R-values: 0.12-0.67) was seen between the DE and GPR in all intentional plans. The findings indicated a moderate association between DVH and GPR. The data reveal that Delta4 is effective in detecting mistakes in treatment regimens for head-and-neck cancer as well as lung cancer. Conclusion: The study results also imply that Delta4 can detect errors in VMAT plans, depending on the details of the defects and the treatment plans employed.

Error detection for radiotherapy planning validation based on deep learning networks.

BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. PURPOSE: The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. METHOD: We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3 mm; 3%, 2 mm; 2%, 2 mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. RESULTS: The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. CONCLUSION: When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Error detection using a multi-channel hybrid network with a low-resolution detector in patient-specific quality assurance.

PURPOSE: This study aimed to develop a hybrid multi-channel network to detect multileaf collimator (MLC) positional errors using dose difference (DD) maps and gamma maps generated from low-resolution detectors in patient-specific quality assurance (QA) for Intensity Modulated Radiation Therapy (IMRT). METHODS: A total of 68 plans with 358 beams of IMRT were included in this study. The MLC leaf positions of all control points in the original IMRT plans were modified to simulate four types of errors: shift error, opening error, closing error, and random error. These modified plans were imported into the treatment planning system (TPS) to calculate the predicted dose, while the PTW seven29 phantom was utilized to obtain the measured dose distributions. Based on the measured and predicted dose, DD maps and gamma maps, both with and without errors, were generated, resulting in a dataset with 3222 samples. The network's performance was evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1-score, ROC curves, and normalized confusion matrix. Besides, other baseline methods, such as single-channel hybrid network, ResNet-18, and Swin-Transformer, were also evaluated as a comparison. RESULTS: The experimental results showed that the multi-channel hybrid network outperformed other methods, demonstrating higher average precision, accuracy, sensitivity, specificity, and F1-scores, with values of 0.87, 0.89, 0.85, 0.97, and 0.85, respectively. The multi-channel hybrid network also achieved higher AUC values in the random errors (0.964) and the error-free (0.946) categories. Although the average accuracy of the multi-channel hybrid network was only marginally better than that of ResNet-18 and Swin Transformer, it significantly outperformed them regarding precision in the error-free category. CONCLUSION: The proposed multi-channel hybrid network exhibits a high level of accuracy in identifying MLC errors using low-resolution detectors. The method offers an effective and reliable solution for promoting quality and safety of IMRT QA.

Localization of reference points in electromagnetic tracking data and their application for treatment error detection in interstitial breast brachytherapy.

BACKGROUND: Electromagnetic tracking (EMT) is a promising technology that holds great potential to advance patient-specific pre-treatment verification in interstitial brachytherapy (iBT). It allows easy determination of the implant geometry without line-of-sight restrictions and without dose exposure to the patient. What it cannot provide, however, is a link to anatomical landmarks, such as the exit points of catheters or needles on the skin surface. These landmarks are required for the registration of EMT data with other imaging modalities and for the detection of treatment errors such as incorrect indexer lengths, and catheter or needle shifts. PURPOSE: To develop an easily applicable method to detect reference points in the positional data of the trajectory of an EMT sensor, specifically the exit points of catheters in breast iBT, and to apply the approach to pre-treatment error detection. METHODS: Small metal objects were attached to catheter fixation buttons that rest against the breast surface to intentionally induce a local, spatially limited perturbation of the magnetic field on which the working principle of EMT relies. This perturbation can be sensed by the EMT sensor as it passes by, allowing it to localize the metal object and thus the catheter exit point. For the proof-of-concept, different small metal objects (magnets, washers, and bushes) and EMT sensor drive speeds were used to find the optimal parameters. The approach was then applied to treatment error detection and validated in-vitro on a phantom. Lastly, the in-vivo feasibility of the approach was tested on a patient cohort of four patients to assess the impact on the clinical workflow. RESULTS: All investigated metal objects were able to measurably perturb the magnetic field, which resulted in missing sensor readings, that is two data gaps, one for the sensor moving towards the tip end and one when retracting from there. The size of the resulting data gaps varied depending on the choice of gap points used for calculation of the gap size; it was found that the start points of the gaps in both directions showed the smallest variability. The median size of data gaps was ⩽8 mm for all tested materials and sensor drive speeds. The variability of the determined object position was ⩽0.5 mm at a speed of 1.0 cm/s and ⩽0.7 mm at 2.5 cm/s, with an increase up to 2.3 mm at 5.0 cm/s. The in-vitro validation of the error detection yielded a 100% detection rate for catheter shifts of ≥2.2 mm. All simulated wrong indexer lengths were correctly identified. The in-vivo feasibility assessment showed that the metal objects did not interfere with the routine clinical workflow. CONCLUSIONS: The developed approach was able to successfully detect reference points in EMT data, which can be used for registration to other imaging modalities, but also for treatment error detection. It can thus advance the automation of patient-specific, pre-treatment quality assurance in iBT.

A User-Friendly System for Mailed Dosimetric Audits of 192Ir or 60Co HDR Brachytherapy Sources.

OBJECTIVES: The main goal of this work is to design and characterize a user-friendly methodology to perform mailed dosimetric audits in high dose rate (HDR) brachytherapy for systems using either Iridium-192 (192Ir) or Cobalt-60 (60Co) sources. METHODS: A solid phantom was designed and manufactured with four catheters and a central slot to place one dosimeter. Irradiations with an Elekta MicroSelectron V2 for 192Ir, and with a BEBIG Multisource for 60Co were performed for its characterization. For the dose measurements, nanoDots, a type of optically stimulated luminescent dosimeters (OSLDs), were characterized. Monte Carlo (MC) simulations were performed to evaluate the scatter conditions of the irradiation set-up and to study differences in the photon spectra of different 192Ir sources (Microselectron V2, Flexisource, BEBIG Ir2.A85-2 and Varisource VS2000) reaching the dosimeter in the irradiation set-up. RESULTS: MC simulations indicate that the surface material on which the phantom is supported during the irradiations does not affect the absorbed dose in the nanoDot. Generally, differences below 5% were found in the photon spectra reaching the detector when comparing the Microselectron V2, the Flexisource and the BEBIG models. However, differences up to 20% are observed between the V2 and the Varisource VS2000 models. The calibration coefficients and the uncertainty in the dose measurement were evaluated. CONCLUSIONS: The system described here is able to perform dosimetric audits in HDR brachytherapy for systems using either 192Ir or 60Co sources. No significant differences are observed between the photon spectra reaching the detector for the MicroSelectron V2, the Flexisource and the BEBIG 192Ir sources. For the Varisource VS2000, a higher uncertainty is considered in the dose measurement to allow for the nanoDot response.

Sensitivity and specificity of Varian Halcyon's portal dosimetry for plan-specific pre-treatment QA.

PURPOSE: Developed as a plan-specific pre-treatment QA tool, Varian portal dosimetry promises a fast, high-resolution, and integrated QA solution. In this study, the agreement between predicted fluence and measured cumulative portal dose was determined for the first 140 patient plans at our Halcyon linear accelerator. Furthermore, the capability of portal dosimetry to detect incorrect plan delivery was compared to that of a common QA phantom. Finally, tolerance criteria for verification of VMAT plan delivery with Varian portal dosimetry were derived. METHODS: All patient plans and the corresponding verification plans were generated within the Eclipse treatment planning system. Four representative plans of different treatment sites (prostate, prostate with lymphatic drainage, rectum, and head & neck) were intentionally altered to model incorrect plan delivery. Investigated errors included both systematic and random errors. Gamma analysis was conducted on both portal dose (criteria γ2%/2 mm , γ2%/1 mm , and γ1%/1 mm ) and ArcCHECK measurements (criteria γ3%/3 mm , γ3%/2 mm , and γ2%/2 mm ) with a 10% low-dose threshold. Performance assessment of various acceptance criteria for plan-specific treatment QA utilized receiver operating characteristic (ROC) analysis. RESULTS: Predicted and acquired portal dosimetry fluences demonstrated a high agreement evident by average gamma passing rates for the clinical patient plans of 99.90%, 96.64%, and 91.87% for γ2%/2 mm , γ2%/1 mm , and γ1%/1 mm , respectively. The ROC analysis demonstrated a very high capability of detecting erroneous plan delivery for portal dosimetry (area under curve (AUC) > 0.98) and in this regard outperforms QA with the ArcCHECK phantom (AUC ≈ 0.82). With the suggested optimum decision thresholds excellent sensitivity and specificity is simultaneously possible. CONCLUSIONS: Owing to the high achievable spatial resolution, portal dosimetry at the Halcyon can reliably be deployed as plan-specific pre-treatment QA tool to screen for errors. It is recommended to support the fluence integrated portal dosimetry QA by independent phantom-based measurements of a random sample survey of treatment plans.

Treatment verification in high dose rate brachytherapy using a realistic 3D printed head phantom and an imaging panel.

PURPOSE: Even though High Dose Rate (HDR) brachytherapy has good treatment outcomes in different treatment sites, treatment verification is far from widely implemented because of a lack of easily available solutions. Previously it has been shown that an imaging panel (IP) near the patient can be used to determine treatment parameters such as the dwell time and source positions in a single material pelvic phantom. In this study we will use a heterogeneous head phantom to test this IP approach, and simulate common treatment errors to assess the sensitivity and specificity of the error-detecting capabilities of the IP. METHODS AND MATERIALS: A heterogeneous head-phantom consisting of soft tissue and bone equivalent materials was 3D-printed to simulate a base of tongue treatment. An High Dose Rate treatment plan with 3 different catheters was used to simulate a treatment delivery, using dwell times ranging from 0.3 s to 4 s and inter-dwell distances of 2 mm. The IP was used to measure dwell times, positions and detect simulated errors. Measured dwell times and positions were used to calculate the delivered dose. RESULTS: Dwell times could be determined within 0.1 s. Source positions were measured with submillimeter accuracy in the plane of the IP, and average distance accuracy of 1.7 mm in three dimensions. All simulated treatment errors (catheter swap, catheter shift, afterloader errors) were detected. Dose calculations show slightly different distributions with the measured dwell positions and dwell times (gamma pass rate for 1 mm/1% of 96.5%). CONCLUSIONS: Using an IP, it was possible to verify the treatment in a realistic heterogeneous phantom and detect certain treatment errors.

Error detection using EPID-based 3D in vivo dose verification for lung stereotactic body radiotherapy.

PURPOSE: To investigate the error detectability limitations of an EPID-based 3D in vivo dosimetry verification system for lung stereotactic body radiation therapy (SBRT). METHODS: Thirty errors were intentionally introduced, consisting of dynamic and constant machine errors, to simulate the possible errors that may occur during delivery. The dynamic errors included errors in the output, gantry angle and MLC positions related to gantry inertial and gravitational effects, while the constant errors included errors in the collimator angle, jaw positions, central leaf positions, setup shift and thickness to simulate patient weight loss. These error plans were delivered to a CIRS phantom using the SBRT technique for lung cancer. Following irradiation of these error plans, the dose distribution was reconstructed using iViewDose™ and compared with the no error plan. RESULTS: All errors caused by the central leaf positions, dynamic MLC errors, Jaw inwards movements, setup shifts and patient anatomical changes were successfully detected. However, dynamic gantry angle and collimator angle errors were not detected in the lung case due to the rotation-symmetric target shape. The results showed that the γmean and γpassrate indicators can detect 13 (81.3%) and 14 (87.5%) of the 16 errors respectively without including the gantry angle error, collimator angle error and output error. CONCLUSIONS: In summary, iViewDose™ is an appropriate approach for detecting most types of clinical errors for lung SBRT. However, the phantom results also showed some detectability limitations of the system in terms of dynamic gantry angle and constant collimator angle errors.

A Deep-Learning Error Detection System in Radiation Therapy.

Delivering radiation therapy based on erroneous or corrupted treatment plan data has previously and unfortunately resulted in severe, sometimes grave patient harm. Aiming to prevent such harm and improve safety in radiation therapy treatment, this work introduces a novel, yet intuitive algorithm for strategically structuring the complex and unstructured data typical of modern treatment plans so their treatment sites may automatically be verified with deep-learning architectures. The proposed algorithm utilizes geometric and dose plan parameters to represent each plan's data as a heat map to feed a deep-learning classifier that will predict the plan's treatment site. Once it is returned by the classifier, a plan's predicted site can be compared to its documented intended site, and a warning raised should the two differ. Using real head-neck, breast, and prostate treatment plan data retrieved at two hospitals in the United States, the algorithm is evaluated by observing the accuracy of convolutional neural networks (ConvNets) in correctly classifying the structured heat map data. Many well-known ConvNet architectures are tested, and ResNet-18 performs the best with a testing accuracy of 97.8% and 0.979 F-1 score. Clearly, the heat maps generated by the proposed algorithm, despite using only a few of the many available plan parameters, retain enough information for correct treatment site classification. The simple construction and ease of interpretation make the heat maps an attractive choice for classification and error detection.

Errors detected during physics plan review for external beam radiotherapy.

Background and purpose: Risk management in radiotherapy is of high importance. There is not much data published on errors occurring in the treatment planning process of external beam techniques. The aim of this study was to investigate errors occurring during physics plan review in external beam radiotherapy. Materials and methods: Over a period of 14 months errors observed during the physical review process are reported. The errors were grouped and evaluated regarding treatment machine, technique, and treatment site. In addition, a correlation between frequency of errors and staff shortage was analyzed. Results: Subgroups of grave errors (g-errors) and slight errors (s-errors) were defined to consider the different impact on the patient and clinical workflow of the errors. In 1056 plans reviewed, 110 errors (41 g-errors, 69 s-errors) were detected. The most common g-errors and s-errors were "Wrong gantry angle at setup field" (n = 19) and "Wrong field label" (n = 24), respectively. A correlation of number of errors and treatment machine, technique, or anatomical site could not be found. No correlation between staff shortage and number of errors was observed. Conclusions: The process of reviewing treatment plans is a relevant topic to consider in risk analysis of the radiotherapy workflow. The review process could be improved by enhancements in the treatment planning systems, use of digital dose prescription, and treatment planning templates.

What is the optimal input information for deep learning-based pre-treatment error identification in radiotherapy?

Background and purpose: Deep learning (DL) provides high sensitivity for detecting and identifying errors in pre-treatment radiotherapy quality assurance (QA). This work's objective was to systematically evaluate the impact of different dose comparison and image preprocessing methods on DL model performance for error identification in pre-treatment QA. Materials and methods: For 53 volumetric modulated arc therapy (VMAT) and 69 stereotactic body radiotherapy (SBRT) treatment plans of lung cancer patients, mechanical errors were simulated (MLC leaf positions, monitor unit scaling, collimator rotation). Two classification levels were assessed: error type (Level 1) and error magnitude (Level 2). Portal dose images with and without errors were compared using standard (gamma analysis), simple (absolute/relative dose difference, ratio) and alternative (distance-to-agreement, structural similarity index, gradient) dose comparison methods. For preprocessing, different normalization methods (min/max and mean/standard deviation) and image resolutions (32 × 32, 64 × 64 and 128 × 128) were evaluated. All possible combinations of classification level, dose comparison, normalization method and image size resulted in 144 input datasets for DL networks for error identification. Results: Average accuracy was highest for simple dose comparison methods (Level 1: 97.7%, Level 2: 78.1%) while alternative methods scored lowest (Level 1: 91.6%, Level 2: 71.2%). Mean/stdev normalization particularly improved Level 2 classification. Higher image resolution improved error identification, although for SBRT lower image resolution was also sufficient. Conclusions: The choice of dose comparison method has the largest impact on error identification for pre-treatment QA using DL, compared to image preprocessing. Model performance can improve by using simple dose comparison methods, mean/stdev normalization and high image resolution.

Error Detectability of Isodose Volumes as ROIs in Prostate Intensity-modulated RT QA.

BACKGROUND/AIM: To evaluate the quality of error detectability with a three-dimensional verification system using isodose volumes as regions of interest (ROIs) in quality assurance (QA) of intensity-modulated radiation therapy. PATIENTS AND METHODS: Treatment plans with four types of intentional errors were created from the data of 20 patients with localized prostate cancer. These plans underwent QA using the three-dimensional verification system. The datasets of another 30 cases without inserted errors were assessed as controls. The ROIs used in the evaluations were those used in our conventional method (planning target volume, rectum, and bladder). The isodose volume method (5%, 50% and 95% isodose volume) and the error detection rates (measurement above the tolerance values, as set from the other 30 cases) were assessed and compared. RESULTS: There was significantly higher multileaf collimator systematic closed error detectability with the isodose volume method compared to the conventional method (A-side 0.2 mm: p=0.005, A-side 0.35 mm: p=0.002, B-side 0.2 mm: p=0.001 and B-side 0.35 mm: p=0.010). There were no error types for which the error detection rate of the isodose volume method was lower than that of the conventional method. CONCLUSION: The isodose volume method was able to evaluate the irradiated ROIs that could be delineated, and improved error detectability. This method has the potential to provide a wider margin of safety in intensity-modulated radiation therapy.

Evaluation of the ability of three commercially available dosimeters to detect systematic delivery errors in step-and-shoot IMRT plans.

BACKGROUND: There is limited data on error detectability for step-and-shoot intensity modulated radiotherapy (sIMRT) plans, despite significant work on dynamic methods. However, sIMRT treatments have an ongoing role in clinical practice. This study aimed to evaluate variations in the sensitivity of three patient-specific quality assurance (QA) devices to systematic delivery errors in sIMRT plans. MATERIALS AND METHODS: Four clinical sIMRT plans (prostate and head and neck) were edited to introduce errors in: Multi-Leaf Collimator (MLC) position (increasing field size, leaf pairs offset (1-3 mm) in opposite directions; and field shift, all leaves offset (1-3 mm) in one direction); collimator rotation (1-3 degrees) and gantry rotation (0.5-2 degrees). The total dose for each plan was measured using an ArcCHECK diode array. Each field, excluding those with gantry offsets, was also measured using an Electronic Portal Imager and a MatriXX Evolution 2D ionisation chamber array. 132 plans (858 fields) were delivered, producing 572 measured dose distributions. Measured doses were compared to calculated doses for the no-error plan using Gamma analysis with 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria (1716 analyses). RESULTS: Generally, pass rates decreased with increasing errors and/or stricter gamma criteria. Pass rate variations with detector and plan type were also observed. For a 3%/3 mm gamma criteria, none of the devices could reliably detect 1 mm MLC position errors or 1 degree collimator rotation errors. CONCLUSIONS: This work has highlighted the need to adapt QA based on treatment plan type and the need for detector specific assessment criteria to detect clinically significant errors.

Radiomics analysis of EPID measurements for patient positioning error detection in thyroid associated ophthalmopathy radiotherapy.

PURPOSE: Electronic portal imaging detector (EPID)-based patient positioning verification is an important component of safe radiotherapy treatment delivery. In computer simulation studies, learning-based approaches have proven to be superior to conventional gamma analysis in the detection of positioning errors. To approximate a clinical scenario, the detectability of positioning errors via EPID measurements was assessed using radiomics analysis for patients with thyroid-associated ophthalmopathy. METHODS: Treatment plans of 40 patients with thyroid-associated ophthalmopathy were delivered to a solid anthropomorphic head phantom. To simulate positioning errors, combinations of 0-, 2-, and 4-mm translation errors in the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions were introduced to the phantom. The positioning errors-induced dose differences between measured portal dose images were used to predict the magnitude and direction of positioning errors. The detectability of positioning errors was assessed via radiomics analysis of the dose differences. Three classification models-support vector machine (SVM), k-nearest neighbors (KNN), and XGBoost-were used for the detection of positioning errors (positioning errors larger or smaller than 3 mm in an arbitrary direction) and direction classification (positioning errors larger or smaller than 3 mm in a specific direction). The receiver operating characteristic curve and the area under the ROC curve (AUC) were used to evaluate the performance of classification models. RESULTS: For the detection of positioning errors, the AUC values of SVM, KNN, and XGBoost models were all above 0.90. For LR, SI, and AP direction classification, the highest AUC values were 0.76, 0.91, and 0.80, respectively. CONCLUSIONS: Combined radiomics and machine learning approaches are capable of detecting the magnitude and direction of positioning errors from EPID measurements. This study is a further step toward machine learning-based positioning error detection during treatment delivery with EPID measurements.

Clinical Validation of a Novel Quality Management System for Blood Gas, Electrolytes, Metabolites, and CO-Oximetry.

BACKGROUND: Quality management of point-of-care (POC) blood gas testing focuses on verifying instrument accuracy and precision, in addition to performing daily quality control (QC) checks every 8 h and with each patient test (unless internal calibration is verified every 30 min). At the POC, a risk-based approach is suitable to address both systemic and transient sample-specific errors that may negatively impact patient care. METHODS: We evaluated the performance of the GEM® Premier™ 5000 with next generation Intelligent Quality Management 2 (iQM®2) (Instrumentation Laboratory, Bedford, MA), from the analysis of approximately 84,000 patient samples across 4 sites. Continuous iQM2 was compared to intermittent liquid QC, either manual or automated, at 2 sites. Analysis of error flags for patient samples and statistical characteristics of QC processes, including method sigma and average detection time (ADT) for an error, were examined. RESULTS: ADT was approximately 2 min with iQM2 and varied from hours to days with intermittent QC. iQM2 Process Control Solutions (PCS) precision was similar or better (>6 sigma for all analytes) than manual (sigma 3.0 for pO2) or automated internal QC (sigma 1.3 for tHb and sigma 3.3 for pO2). In addition, iQM2 detected errors in ∼1.4% of samples, providing an additional safeguard against reporting erroneous results. CONCLUSIONS: The findings in this study demonstrate excellent performance of the GEM Premier 5000 with iQM2 including >6 sigma precision for all analytes and faster error detection times. These benefits address risk in different phases of testing that are not easily detected by intermittent performance of liquid QC (manual or automated).

8+ Year Performance of the Gamma Knife Perfexion/Icon Patient Positioning System and Possibilities for Preemptive Fault Detection Using Statistical Process Control.

BACKGROUND: The large fractional doses, steep dose gradients, and small targets found in intracranial radiosurgery require extremely low beam delivery uncertainty. In the case of Gamma Knife radiosurgery (GKRS), this includes minimizing patient positioning system (PPS) positioning uncertainty. Existing QA techniques are recipe based, and feature point in time pass/fail tolerances. However, modern treatment machines, including the Gamma Knife Perfexion/Icon systems, record extensive internal data in treatment logs. These data can be analyzed through statistical process control (SPC) methods which are designed to detect changes in process behavior. The purpose of this study was to characterize the long-term (8+ year) performance of a Perfexion/Icon unit and use SPC methods to determine if performance changes could be detected at levels lower than existing QA and internal manufacturer performance tolerances. METHODS: In-house software was developed to parse Perfexion/Icon log-files and store relevant information on shot delivery in a relational database. A last-in, first-out (LIFO) queuing algorithm was created to heuristically match messages associated with a given delivered shot. Filtering criteria were developed to filter QA and uncompleted shots. The resulting matched shots were extracted. Achieved versus planned PPS position was determined for each PPS motor as well as for the vector magnitude difference in PPS position. Exponentially weighted moving average (EWMA) control charts were plotted to determine when process behavior changed over time. RESULTS: 53833 shots were delivered over an 8+ year span in the study. The mean vector magnitude PPS difference was 32.7 µm, with 97.5% of all shots within 70.1 µm. Several changes in PPS positioning behavior were observed over time, corresponding with control system faults on several occasions requiring PPS recalibration. EWMA control charts clearly demonstrate that these faults could be identified and possibly predicted as many as 3 years before there were faults beyond control system tolerance. CONCLUSION: The PPS of Gamma Knife Perfexion/Icon systems has extremely low positioning uncertainties. EWMA control chart method can be utilized to track PPS performance over time and can potentially detect changes in performance that may indicate a component requiring maintenance. This would allow planned service visits to mitigate problems and prevent unplanned downtime.

Improving dose delivery accuracy with EPID in vivo dosimetry: results from a multicenter study.

PURPOSE: To investigate critical aspects and effectiveness of in vivo dosimetry (IVD) tests obtained by an electronic portal imaging device (EPID) in a multicenter and multisystem context. MATERIALS AND METHODS: Eight centers with three commercial systems-SoftDiso (SD, Best Medical Italy, Chianciano, Italy), Dosimetry Check (DC, Math Resolution, LCC), and PerFRACTION (PF, Sun Nuclear Corporation, SNC, Melbourne, FL)-collected IVD results for a total of 2002 patients and 32,276 tests. Data are summarized for IVD software, radiotherapy technique, and anatomical site. Every center reported the number of patients and tests analyzed, and the percentage of tests outside of the tolerance level (OTL%). OTL% was categorized as being due to incorrect patient setup, incorrect use of immobilization devices, incorrect dose computation, anatomical variations, and unknown causes. RESULTS: The three systems use different approaches and customized alert indices, based on local protocols. For Volumetric Modulated Arc Therapy (VMAT) treatments OTL% mean values were up to 8.9% for SD, 18.0% for DC, and 16.0% for PF. Errors due to "anatomical variations" for head and neck were up to 9.0% for SD and DC and 8.0% for PF systems, while for abdomen and pelvis/prostate treatments were up to 9%, 17.0%, and 9.0% for SD, DC, and PF, respectively. The comparison among techniques gave 3% for Stereotactic Body Radiation Therapy, 7.0% (range 4.7-8.9%) for VMAT, 10.4% (range 7.0-12.2%) for Intensity Modulated Radiation Therapy, and 13.2% (range 8.8-21.0%) for 3D Conformal Radiation Therapy. CONCLUSION: The results obtained with different IVD software and among centers were consistent and showed an acceptable homogeneity. EPID IVD was effective in intercepting important errors.