Multileaf collimator characteristics and reliability requirements for IMRT Elekta system

Machine learning models to predict the delivered positions of Elekta multileaf collimator leaves for volumetric modulated arc therapy

PURPOSE

Accurate positioning of multileaf collimator (MLC) leaves during volumetric modulated arc therapy (VMAT) is essential for accurate treatment delivery. We developed a linear regression, support vector machine, random forest, extreme gradient boosting (XGBoost), and an artificial neural network (ANN) for predicting the delivered leaf positions for VMAT plans.

METHODS

For this study, 160 MLC log files from 80 VMAT plans were obtained from a single institution treated on 3 Elekta Versa HD linear accelerators. The gravity vector, X1 and X2 jaw positions, leaf gap, leaf position, leaf velocity, and leaf acceleration were extracted and used as model inputs. The models were trained using 70% of the log files and tested on the remaining 30%. Mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination R² , and fitted line plots showing the relationship between delivered and predicted leaf positions were used to evaluate model performance.

RESULTS

The models achieved the following errors: linear regression (MAE = 0.158 mm, RMSE = 0.225 mm), support vector machine (MAE = 0.141 mm, RMSE = 0.199 mm), random forest (MAE = 0.161 mm, RMSE = 0.229 mm), XGBoost (MAE = 0.185 mm, RMSE = 0.273 mm), and ANN (MAE = 0.361 mm, RMSE = 0.521 mm). A significant correlation between a plan's gamma passing rate (GPR) and the prediction errors of linear regression, support vector machine, and random forest is seen (p < 0.045).

CONCLUSIONS

We examined various models to predict the delivered MLC positions for VMAT plans treated with Elekta linacs. Linear regression, support vector machine, random forest, and XGBoost achieved lower errors than ANN. Models that can accurately predict the individual leaf positions during treatment can help identify leaves that are deviating from the planned position, which can improve a plan's GPR.

Highly efficient and sensitive patient-specific quality assurance for spot-scanned proton therapy

The purpose of this work was to develop an end-to-end patient-specific quality assurance (QA) technique for spot-scanned proton therapy that is more sensitive and efficient than traditional approaches. The patient-specific methodology relies on independently verifying the accuracy of the delivered proton fluence and the dose calculation in the heterogeneous patient volume. A Monte Carlo dose calculation engine, which was developed in-house, recalculates a planned dose distribution on the patient CT data set to verify the dose distribution represented by the treatment planning system. The plan is then delivered in a pre-treatment setting and logs of spot position and dose monitors, which are integrated into the treatment nozzle, are recorded. A computational routine compares the delivery log to the DICOM spot map used by the Monte Carlo calculation to ensure that the delivered parameters at the machine match the calculated plan. Measurements of dose planes using independent detector arrays, which historically are the standard approach to patient-specific QA, are not performed for every patient. The nozzle-integrated detectors are rigorously validated using independent detectors in regular QA intervals. The measured data are compared to the expected delivery patterns. The dose monitor reading deviations are reported in a histogram, while the spot position discrepancies are plotted vs. spot number to facilitate independent analysis of both random and systematic deviations. Action thresholds are linked to accuracy of the commissioned delivery system. Even when plan delivery is acceptable, the Monte Carlo second check system has identified dose calculation issues which would not have been illuminated using traditional, phantom-based measurement techniques. The efficiency and sensitivity of our patient-specific QA program has been improved by implementing a procedure which independently verifies patient dose calculation accuracy and plan delivery fidelity. Such an approach to QA requires holistic integration and maintenance of patient-specific and patient-independent QA.

The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for "intensity modulated radiation therapy (IMRT)" as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

On the sensitivity of patient-specific IMRT QA to MLC positioning errors

Accurate multileaf collimator (MLC) leaf positioning plays an essential role in the effective implementation of intensity modulated radiation therapy (IMRT). This work evaluates the sensitivity of current patient-specific IMRT quality assurance (QA) procedures to minor MLC leaf positioning errors. Random errors of up to 2 mm and systematic errors of +/-1 mm and +/-2 mm in MLC leaf positions were introduced into 8 clinical IMRT patient plans (totaling 53 fields). Planar dose distributions calculated with modified plans were compared to dose distributions measured with both radiochromic films and a diode matrix. The agreement between calculation and measurement was evaluated using both absolute distance-to-agreement (DTA) analysis and gamma index with 2%/2 mm and 3%/3 mm criteria. It was found that both the radiochromic film and the diode matrix could only detect systematic errors on the order of 2 mm or above. The diode array had larger sensitivity than film due to its excellent detector response (such as small variation, linear response, etc.). No difference was found between DTA analysis and gamma index in terms of the sensitivity to MLC positioning errors. Higher sensitivity was observed with 2%/2 mm than with 3%/3 mm in general. When using the diode array and 2%/2 mm criterion, the IMRT QA procedure showed strongest sensitivity to MLC position errors and, at the same time, achieved clinically acceptable passing rates. More accurate dose calculation and measurement would further enhance the sensitivity of patient-specific IMRT QA to MLC positioning errors. However, considering the significant dosimetric effect such MLC errors could cause, patient-specific IMRT QA should be combined with a periodic MLC QA program in order to guarantee the accuracy of IMRT delivery.

Quality assurance needs for modern image-based radiotherapy: recommendations from 2007 interorganizational symposium on "quality assurance of radiation therapy: challenges of advanced technology"

This report summarizes the consensus findings and recommendations emerging from 2007 Symposium, "Quality Assurance of Radiation Therapy: Challenges of Advanced Technology." The Symposium was held in Dallas February 20-22, 2007. The 3-day program, which was sponsored jointly by the American Society for Therapeutic Radiology and Oncology (ASTRO), American Association of Physicists in Medicine (AAPM), and National Cancer Institute (NCI), included >40 invited speakers from the radiation oncology and industrial engineering/human factor communities and attracted nearly 350 attendees, mostly medical physicists. A summary of the major findings follows. The current process of developing consensus recommendations for prescriptive quality assurance (QA) tests remains valid for many of the devices and software systems used in modern radiotherapy (RT), although for some technologies, QA guidance is incomplete or out of date. The current approach to QA does not seem feasible for image-based planning, image-guided therapies, or computer-controlled therapy. In these areas, additional scientific investigation and innovative approaches are needed to manage risk and mitigate errors, including a better balance between mitigating the risk of catastrophic error and maintaining treatment quality, complimenting the current device-centered QA perspective by a more process-centered approach, and broadening community participation in QA guidance formulation and implementation. Industrial engineers and human factor experts can make significant contributions toward advancing a broader, more process-oriented, risk-based formulation of RT QA. Healthcare administrators need to appropriately increase personnel and ancillary equipment resources, as well as capital resources, when new advanced technology RT modalities are implemented. The pace of formalizing clinical physics training must rapidly increase to provide an adequately trained physics workforce for advanced technology RT. The specific recommendations of the Symposium included the following. First, the AAPM, in cooperation with other advisory bodies, should undertake a systematic program to update conventional QA guidance using available risk-assessment methods. Second, the AAPM advanced technology RT Task Groups should better balance clinical process vs. device operation aspects--encouraging greater levels of multidisciplinary participation such as industrial engineering consultants and use-risk assessment and process-flow techniques. Third, ASTRO should form a multidisciplinary subcommittee, consisting of physician, physicist, vendor, and industrial engineering representatives, to better address modern RT quality management and QA needs. Finally, government and private entities committed to improved healthcare quality and safety should support research directed toward addressing QA problems in image-guided therapies.