Institution-specific pre-treatment quality assurance control and specification limits: a tool to implement a new formalism and criteria optimization using statistical process control and heuristic methods

Introduction: Advanced radiotherapy methods include Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) requiring stringent quality assurance (QA) to ensure accurate dose delivery. This study assesses the effectiveness of IMRT and VMAT dosage verification by gamma index analysis and capability index (Cpml), which are statistical process control (SPC) tools, to assess QA process reliability and compare dosimetrical outcomes for head and neck and rectal cancer treatment plans. Materials and Methods: For 28 cases of head and neck (14 IMRT and 14 VMAT) were generated utilizing treatment planning system (TPS) Eclipse, which delivered 6 MV photon beam linear accelerator (Varian, Clinac IX). Dose distributions were measured using an aSi-1000 electronic portal imaging device (EPID) and OCTAVIUS Detector 1500 2D array, compared against TPS calculations using gamma index criteria (3%/3 mm). Control charts and Cpml evaluated QA process stability for 25 cases. Dosimetrical comparisons for rectal cancer assessed planning target volume coverage, organ-at-risk doses, and monitor units. Result: For head and neck the gamma passing rates showed strong agreement, with means of 99.24% ± 0.37 (EPID) and 98.76% ± 0.61 (2D array) for IMRT, and 99.00% ± 0.38 (EPID) and 98.91% ± 0.43 (2D array) for VMAT. For rectal cancer the capability index (Cpml) values of 1.36 (IMRT) and 1.47 (VMAT) indicated optimal QA performance. VMAT plans for rectal cancer reduced MUs and treatment time by approximately 50% while achieving comparable PTV coverage and OAR sparing. Conclusion: VMAT demonstrates efficiency advantages over IMRT, with robust QA processes, supported by SPC and capability index assessments, enhancing treatment precision and patient outcomes.

This work applied statistical process control to establish the control limits of the % gamma pass of patient-specific intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) quality assurance (QA), and to evaluate the efficiency of the QA process by using the process capability index (Cpml). A total of 278 IMRT QA plans in nasopharyngeal carcinoma were measured with MapCHECK, while 159 VMAT QA plans were undertaken with ArcCHECK. Six megavolts with nine fields were used for the IMRT plan and 2.5 arcs were used to generate the VMAT plans. The gamma (3%/3 mm) criteria were used to evaluate the QA plans. The % gamma passes were plotted on a control chart. The first 50 data points were employed to calculate the control limits. The Cpml was calculated to evaluate the capability of the IMRT/VMAT QA process. The results showed higher systematic errors in IMRT QA than VMAT QA due to the more complicated setup used in IMRT QA. The variation of random errors was also larger in IMRT QA than VMAT QA because the VMAT plan has more continuity of dose distribution. The average % gamma pass was 93.7% ± 3.7% for IMRT and 96.7% ± 2.2% for VMAT. The Cpml value of IMRT QA was 1.60 and VMAT QA was 1.99, which implied that the VMAT QA process was more accurate than the IMRT QA process. Our lower control limit for % gamma pass of IMRT is 85.0%, while the limit for VMAT is 90%. Both the IMRT and VMAT QA processes are good quality because Cpml values are higher than 1.0.

Purpose:To evaluate the sensitivity of gamma criteria for patient‐specific volumetric modulated arc therapy(VMAT) quality assurance of the Delta4 dosimetry program using the statistical process control(SPC) methodology.Methods:The authors selected 20 patient‐specific VMAT QA cases which were undertaken MapCHECK and ArcCHECK with gamma pass rate better than 97%. The QAs data were collected Delta4 Phantom+ and Elekta Agility six megavolts without using an angle incrementer. The gamma index(GI) were calculated in 2D planes with normalizing deviation to local dose(local gamma). The sensitivity of the GI methodology using criterion of 3%/3mm, 3%/2mm and 2%/3mm was analyzed with using process acceptability indices. We used local confidence(LC) level, the upper control limit(UCL) and lower control limit(LCL) of I‐MR chart for process capability index(Cp) and a process acceptability index (Cpk).Results:The lower local confidence levels of 3%/3mm, 3%/2mm and 2%/3mm were 92.0%, 83.6% and 78.8% respectively. All of the calculated Cp and Cpk values that used LC level were under 1.0 in this study. The calculated LCLs of I‐MR charts were 89.5%, 79.0% and 70.5% respectively. These values were higher than 1.0 which means good quality of QA. For the generally used lower limit of 90%, we acquired over 1.3 of Cp value for the gamma index of 3%/3mm and lower than 1.0 in the rest of GI.Conclusion:We applied SPC methodology to evaluate the sensitivity of gamma criteria and could see the lower control limits of VMAT QA for the Delta 4 dosimetry and could see that Delta 4 phantom+ dosimetry more affected by the position error and the I‐MR chart derived values are more suitable for establishing lower limits.Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (No. 2015R1D1A1A01060463)

The aim of this study was to compare the dosimetric performance and to evaluate the pretreatment quality assurance (QA) of a portal dosimetry and a commercial two-dimensional (2-D) array system. In the characteristics comparison study, the measured values for the dose linearity, dose rate response, reproducibility, and field size dependence for 6-MV photon beams were analyzed for both detector systems. To perform the qualitative evaluations of the 10 IMRT and the 10 VMAT plans, we used the Gamma index for quantifying the agreement between calculations and measurements. The performance estimates for both systems show that overall, minimal differences in the dosimetric characteristics exist between the Electron portal imaging device (EPID) and 2-D array system. In the qualitative analysis for pretreatment quality assurance, the EPID and 2-D array system yield similar passing rate results for the majority of clinical Intensity-modulated radiation therapy (IMRT) and Volumetric-modulated arc therapy (VMAT) cases. These results were satisfactory for IMRT and VMAT fields and were within the acceptable criteria of γ%≤1, γavg<0.5. The EPDI and the 2-D array systems showed comparable dosimetric results. In this study, the results revealed both systems to be suitable for patient-specific QA measurements for IMRT and VMAT. We conclude that, depending on the status of clinic, both systems can be used interchangeably for routine pretreatment QA.

This paper presents a quality control tool, that is, a quality control method, which in this case concernsthe mechanical processing of wood, but in general, it can be implemented in any form of production (mechanicalengineering, traffic, food technology...). The goal of the research is to show the way of applying the control chartmethod, that is, to determine the stability of the technological process of mechanical wood processing in order to beable to define appropriate corrective measures in order to improve that part of the production process. The controlchart that was created in this paper is a graph consisting of a horizontal (abscissa) and a vertical axis (ordinate). Theorder of observations is plotted on the abscissa, and the corresponding values on the ordinate. Apart from theprecise creation of control charts, their correct interpretation is also of great importance. It is easiest to interpretcontrol charts where the process is "outside statistical control", that is, the process where the values of individualmeasurements are outside the control limits. This means that some special cause of variation is present in the processand an adjustment needs to be made. However, placing all points within the control limits, that is, obtaining acontrol map on which the process is "within statistical control", does not necessarily mean that such a process isacceptable by statisticians. On the basis of the results, that is, on the basis of the graph that has the shape of a brokenline, we observe whether the control limits have been exceeded, and we draw certain conclusions about the stabilityof the process. If the graph is between the control limits, we can conclude that our process is stable and undercontrol, that is, that our quality variation is within normal limits. If there are any jumps, ie. if some value in someobservation is out of bounds (above the upper control limit or below the lower control limit), then we conclude thatour process is not stable, not under control and that our quality variation is not within normal limits. In that case,certain corrective measures were proposed, which should improve the technological process of mechanical woodprocessing and bring it under control.Control charts, as an engineering-mathematical tool, are a very suitable tool for achieving the goals of statisticalcontrol. Optimal application of control charts ensures constant monitoring of the process, direction towards thedesired flow and undertaking of possible corrective measures. Also, their application ensures the fulfillment of thedesigned or required quality characteristics, that is, the fulfillment of the requirements of a certain standard. Withtheir simplicity of application and reliability, they provide users with convenience in work and relevant processindicators, and provide process controllers with valid data about the process flow. As such, they represent a tool thatkeeps the entire process within the given limits.

Consider a mass production line for a single “part” and let us center our interest on a single “dimension” of the part. There will always be some variability in the dimension because of the sum of several variables whose causes are not usually understood and whose effects might not be controllable if they were. The dimension of the part is thus a random variable, but one whose mean and variance can change with time as something goes wrong with the “machinery” producing the part. If the process is stable and only the “usual” system of chance causes is operating (which implies that the mean θ and variance σ2 of the process are constant), we say that the process is in statistical control and that it has an inherent variability σ2. If the mean or variance wanders from those stable values, we say that an assignable cause is operating, meaning that it can be understood/controlled. The term quality control refers to control of quality by any means, but statistical quality control refers to the control of quality through the use of certain statistical tools to be described shortly. The best known of those tools is the Shewhart Control Chart, which is used to demonstrate statistical control and to detect the presence of assignable causes. There are control charts for the mean, range, standard deviation, fraction defective, and number of defects per unit. Three horizontal lines plus the data make up the control charts. The 3 lines are a centerline, an upper control limit, and a lower control limit. (One of the control limits may be omitted if there is no interest in it.) The centerline is an “average” of the statistic being plotted, and the control limits are at distances of 3 standard deviations (of the statistic) above and below the centerline.

The aim of this study is to set up statistical quality control for monitoring the volumetric modulated arc therapy (VMAT) delivery error by using the machine’s log data. Eclipse and a Clinac iX linac with the RapidArc system (Varian Medical Systems, Palo Alto, USA) are used for delivery of the VMAT plan. During the delivery of the RapidArc fields, the machine determines the delivered monitor units (MUs) and the gantry angle’s position accuracy and the standard deviations of the MU (σMU: dosimetric error) and the gantry angle (σGA: geometric error) are displayed on the console monitor after completion of the RapidArc delivery. In the present study, first, the log data were analyzed to confirm its validity and usability; then, statistical process control (SPC) was applied to monitor the σMU and the σGA in a timely manner for all RapidArc fields: a total of 195 arc fields for 99 patients. The MU and the GA were determined twice for all fields, that is, first during the patient-specific plan QA and then again during the first treatment. The sMU and the σGA time series were quite stable irrespective of the treatment site; however, the sGA strongly depended on the gantry’s rotation speed. The σGA of the RapidArc delivery for stereotactic body radiation therapy (SBRT) was smaller than that for the typical VMAT. Therefore, SPC was applied for SBRT cases and general cases respectively. Moreover, the accuracy of the potential meter of the gantry rotation is important because the σGA can change dramatically due to its condition. By applying SPC to the σMU and σGA, we could monitor the delivery error efficiently. However, the upper and the lower limits of SPC need to be determined carefully with full knowledge of the machine and log data.

Nurse shortage and rising healthcare resource burdens mean that appropriate workforce use is imperative. This paper aims to evaluate whether daily nursing staffing meets ward workload needs. Nurse attendance and daily nurses' workload capacity in three hospitals were evaluated. Statistical process control was used to evaluate intra-ward nurse workload capacity and day-to-day variations. Statistical process control is a statistics-based method for process monitoring that uses charts with predefined target measure and control limits. Standardization was performed for inter-ward analysis by converting ward-specific crude measures to ward-specific relative measures by dividing observed/expected. Two charts: acceptable and tolerable daily nurse workload intensity, were defined. Appropriate staffing indicators were defined as those exceeding predefined rates within acceptable and tolerable limits (50 percent and 80 percent respectively). A total of 42 percent of the overall days fell within acceptable control limits and 71 percent within tolerable control limits. Appropriate staffing indicators were met in only 33 percent of wards regarding acceptable nurse workload intensity and in only 45 percent of wards regarding tolerable workloads. The study work did not differentiate crude nurse attendance and it did not take into account patient severity since crude bed occupancy was used. Double statistical process control charts and certain staffing indicators were used, which is open to debate. Wards that met appropriate staffing indicators prove the method's feasibility. Wards that did not meet appropriate staffing indicators prove the importance and the need for process evaluations and monitoring. Methods presented for monitoring daily staffing appropriateness are simple to implement either for intra-ward day-to-day variation by using nurse workload capacity statistical process control charts or for inter-ward evaluation using standardized measure of nurse workload intensity. The real challenge will be to develop planning systems and implement corrective interventions such as dynamic and flexible daily staffing, which will face difficulties and barriers. The paper fulfils the need for workforce utilization evaluation. A simple method using available data for daily staffing appropriateness evaluation, which is easy to implement and operate, is presented. The statistical process control method enables intra-ward evaluation, while standardization by converting crude into relative measures enables inter-ward analysis. The staffing indicator definitions enable performance evaluation. This original study uses statistical process control to develop simple standardization methods and applies straightforward statistical tools. This method is not limited to crude measures, rather it uses weighted workload measures such as nursing acuity or weighted nurse level (i.e. grade/band).

Given the low early revision rate after total hip arthroplasty (THA) and total knee arthroplasty (TKA), hospital performance is typically compared using 3 years of data. The purpose of this study was to assess how much earlier worsening hospital performance in 1-year revision rates after THA and TKA can be detected. All 86,468 THA and 73,077 TKA procedures performed from 2014 to 2016 and recorded in the Dutch Arthroplasty Register were included. Negative outlier hospitals were identified by significantly higher O/E (observed divided by expected) 1-year revision rates in a funnel plot. Monthly Shewhart p-charts (with 2 and 3-sigma control limits) and cumulative sum (CUSUM) charts (with 3.5 and 5 control limits) were constructed to detect a doubling of revisions (odds ratio of 2), generating a signal when the control limit was reached. The median number of months until generation of a first signal for negative outliers and the number of false signals for non-negative outliers were calculated. Sensitivity, specificity, and accuracy were calculated for all charts and control limit settings using outlier status in the funnel plot as the gold standard. The funnel plot showed that 13 of 97 hospitals had significantly higher O/E 1-year revision rates and were negative outliers for THA and 7 of 98 hospitals had significantly higher O/E 1-year revision rates and were negative outliers for TKA. The Shewhart p-chart with the 3-sigma control limit generated 68 signals (34 false-positive) for THA and 85 signals (63 false-positive) for TKA. The sensitivity for THA and TKA was 92% and 100%, respectively; the specificity was 69% and 51%, respectively; and the accuracy was 72% and 54%, respectively. The CUSUM chart with a 5 control limit generated 18 signals (1 false-positive) for THA and 7 (1 false-positive) for TKA. The sensitivity was 85% and 71% for THA and TKA, respectively; the specificity was 99% for both; and the accuracy was 97% for both. The Shewhart p-chart with a 3-sigma control limit generated the first signal for negative outliers after a median of 10 months (interquartile range [IQR] = 2 to 18) for THA and 13 months (IQR = 5 to 18) for TKA. The CUSUM chart with a 5 control limit generated the first signal after a median of 18 months (IQR = 7 to 22) for THA and 21 months (IQR = 9 to 25) for TKA. Monthly monitoring using CUSUM charts with a 5 control limit enables earlier detection of worsening 1-year revision rates with accuracy so that initiatives to improve care can start earlier.

This research deals with the application of statistical quality control techniques on process control of paracetamol production of Tuyil Pharmaceutical Company with the aim of estimating the control limits and investigate whether the production process is under statistical control. The data used in this research was collected on two measurable quality characteristics of content and weight of paracetamol from 2015-2018. The Mean, Range and EWMA-chart together with process capability analysis were employed for measuring the production process. The result obtained from the analysis on the content of paracetamol, it was observed that for X-bar and R-Chart one point from the observations is out of the control limits while for the EWMA chart all the observations are within control and capable of improving the production process. The analysis also reveals that on the weight of paracetamol, the Mean, Range and EWMA-chart together with process capability analysis are within control and capable of improving the process of production. it was concluded that for the weight of paracetamol is statistically stable and under control while that of the content is partially stable as one point from the observations fall outside the control limits and this could be due to a small shift in the production process which is caused by chance causes of variation such as temperature, room vibration and so on. It is recommended that the company needs to take proper caution so as to improve their production process in order to meet up with consumer’s requirement.

Automated contouring and statistical process control for plan quality in a breast clinical trial

Improving product quality is one of the important factors in facing industrial competition, especially in the White Crystal Sugar (GKP) production sector. PT XYZ, as one of the GKP producers, faces challenges in maintaining the consistency of its product quality according to applicable standards. This study aims to assess the quality of the production process at PT.XYZ by utilizing the Statistical Quality Control (SQC) method through the use of a p-control map (P-Chart) to measure the proportion of defective products. The analysis was conducted on 29 production samples. The results show that the production process is in statistical control, with an average defect proportion of 0.006984, where all points are between the upper control limit (UCL = 0.008769) and the lower control limit (LCL = 0.005199). The defect rate found is very small, less than 0.1%. The total defects reached 3279 kg GKP, consisting of color defects of 1155 kg (35.22%), moisture content of 1091 kg (33%), and cleanliness of 1033 kg (32%). Based on the fishbone diagram, the defects were caused by workers, machines, operational methods, and production materials. In conclusion, the application of the SQC method is effective in identifying and controlling factors that affect the quality of GKP at PT XYZ.

The aim of this study is to evaluate the behaviour of global and local gamma analyses with isodose levels. Global and local gamma evaluation were performed on patient-specific quality assurance (PSQA) data from 100 volumetric modulated arc therapy (VMAT) arcs and 100 helical tomotherapy (HT) plans, using an in-house gamma code. Gamma pass rates versus isodose levels were plotted and evaluated. Other than a slightly increased skew towards higher pass rates for the global gamma evaluation, minimal differences were observed between the results of evaluating all VMAT arcs separately and the results of evaluating over VMAT treatment plans by combining arcs from each plan. Generally, the VMAT results showed average pass rates that increase with decreasing isodose level, for both global and local gamma evaluations. The HT results differed systematically from the VMAT results, with the results of performing global and local gamma evaluations agreeing more closely at all isodose levels and with the highest gamma pass rates being achieved at intermediate dose levels, between the 40 and 70% isodose levels. These results demonstrate the complex of relationships between global and local gamma evaluation results that can arise when clinical PSQA data are analysed and exemplify how the local gamma evaluation does not necessarily produce disproportionately reduced gamma pass rates in low dose regions. Performing gamma evaluation with different isodose levels is suggested as a useful method to improve understanding of specific PSQA data and as well as the broader features of gamma evaluation results.

The aim of this research is to analyze the production quality control policy at PT Glory Industrial in Sragen, to identify the factors that cause damage and loss to products produced by PT Glory Industrial in Sragen. This research is included in the type of case study research at PT Glory Industrial in Sragen by conducting a survey to the company. This research only covers production quality control at PT Glory Industrial in Sragen. Product quality control uses the Statistical Process Control method. Starting with determining quality standards then production results, then damaged production is analyzed through quality control using Statistical Process Control, so that you can receive analysis results that can be useful for PT Glory Industrial in Sragen. The results of the research show that the implementation of product quality control at PT Glory Industrial has not been under control, this is proven by the production process that there were 4 points that exceeded the upper control limit (UCL) and lower control limit (LCL). Occurred on the months February (0,023), June (0,084), September (0,022) and November (0,075). From this data, it can be seen that there are still those who exceed the control limits, thus indicating that quality control has not been implemented properly and improvements are needed to overcome these deviations. Thus H1 is proven correct, there are 6 types of damage that occur at PT Glory Industrial, namely looseness, broken fabric, embossed threads, skipped threads, damaged stitching and wrong labels. Based on the causes and effects, the factors that cause damage are human factors, machines, materials and work methods. Thus H2 is proven correct.

The problem faced by the Bmrsign Wallgallery company is that products are found that do not match the specifications set by the company. The types of product damage are wrong size frames, broken frames, and uneven corners. To solve this problem, the SQC method is used to reduce the causes of defects in the product. The purpose of this method is to reduce the risk of damage to the product. The object studied was Bmrsign Wallgallery with a sample of 29 days. The use of the SQC method is accompanied by analysis tools in the form of control charts (p-charts), Pareto diagrams, cause and effect diagrams (fishbone). Based on the results of data analysis implementing the Statistical Quality Control (SQC) method using the P-Chart control map, the largest value of the Upper Control Limit (UCL) can be shown as 0.272, while the Lower Control Limit (LCL) is -0.013. The results show that the number of defective products during the frame making process is still within normal control limits. The analysis using the Pareto diagram shows that the types of frame defects are dominated by broken with a percentage of 35% or a total of 66 pcs, inappropriate size shapes as much as 33% or a total of 62 pcs, and uneven corners as much as 32% or a total of 60 pcs. Based on the results of the fishbone diagram analysis, defective products are caused by workers who are not focused, work standards are not adhered to, machine maintenance is not given attention, and there is a decrease in the quality of raw materials.