Comparison of complexity metrics for multi-institutional evaluations of treatment plans in radiotherapy

Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans

BACKGROUND

Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction.

PURPOSE

This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments.

METHODS

UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy.

RESULTS

The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in-house systems available at other centers.

CONCLUSION

UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab-based and stand-alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user-friendly tool empowering researchers to compute the complexity of treatment plans efficiently.

Multi-institution single geometry plan complexity characteristics based on IROC phantoms

BACKGROUND

Clinical intensity modulated radiation therapy plans have been described using various complexity metrics to help identify problematic radiotherapy plans. Most previous studies related to the quantification of plan complexity and their utility have relied on institution-specific plans which can be highly variable depending on the machines, planning techniques, delivery modalities, and measurement devices used. In this work, 1723 plans treating one of only four standardized geometries were simultaneously analyzed to investigate how radiation plan complexity metrics vary across four different sets of common objectives.

PURPOSE

To assess the treatment plan complexity characteristics of plans developed for Imaging and Radiation Oncology Core (IROC) phantoms. Specifically, to understand the variability in plan complexity between institutions for a common plan objective, and to evaluate how various complexity metrics differentiate relevant groups of plans.

METHODS

1723 plans treating one of four standardized IROC phantom geometries representing four different anatomical sites of treatment were analyzed. For each plan, 22 MLC-descriptive plan complexity metrics were calculated, and principal component analysis (PCA) was applied to the 22 metrics in order to evaluate differences in plan complexity between groups. Across all metrics, pairwise comparisons of the IROC phantom data were made for the following classifications of the data: anatomical phantom treated, treatment planning system (TPS), and the combination of MLC model and treatment planning system. An objective k-means clustering algorithm was also applied to the data to determine if any meaningful distinctions could be made between different subgroups. The IROC phantom database was also compared to a clinical database from the University of Wisconsin-Madison (UW) which included plans treating the same four anatomical sites as the IROC phantoms using a TrueBeam™ STx and Pinnacle3 TPS.

RESULTS

The IROC head and neck and spine plans were distinct from the prostate and lung plans based on comparison of the 22 metrics. All IROC phantom plan group complexity metric distributions were highly variable despite all plans being designed for identical geometries and plan objectives. The clusters determined by the k-means algorithm further supported that the IROC head and neck and spine plans involved similar amounts of complexity and were largely distinct from the prostate and lung plans, but no further distinctions could be made. Plan complexity in the head and neck and spine IROC phantom plans were similar to the complexity encountered in the UW clinical plans.

CONCLUSIONS

There is substantial variability in plan complexity between institutions when planning for the same objective. For each IROC anatomical phantom treated, the magnitude of variability in plan complexity between institutions is similar to the variability in plan complexity encountered within a single institution database containing several hundred unique clinical plans treating corresponding anatomies in actual patients.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to patient setup errors

PURPOSE

This study aimed to analyse correlations between planning factors including plan geometry and plan complexity with robustness to patient setup errors.

METHODS

Multiple-target brain stereotactic radiosurgery (SRS) plans were obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets with a 20 Gy prescription. Setup error was simulated using an in-house tool. Dose to targets was assessed via dose covering 99 % (D99 %) of gross tumour volume (GTV) and 98 % of planning target volume (PTV). Dose to organs at risk was assessed using volume of normal brain receiving 12 Gy and maximum dose covering 0.03 cc of brainstem. Plan complexity was assessed via edge metric, modulation complexity score, mean multi-leaf collimator (MLC) gap, mean MLC speed and plan modulation.

RESULTS

Even for small (0.5 mm/°) errors, GTV D99 % was reduced by up to 20 %. The strongest correlation was found between lower complexity plans (larger mean MLC gap and lower edge metric) and higher robustness to setup error. Lower complexity plans had 1 %-20 % fewer targets/scenarios with GTV D99 % falling below the specified tolerance threshold. These complexity metrics correlated with 100 % isodose volume sphericity and dose conformity, though similar conformity was achievable with a range of complexities.

CONCLUSIONS

A higher level of importance should be directed towards plan complexity when considering plan robustness. It is recommended when planning multi-target SRS, larger MLC gaps and lower MLC aperture irregularity be considered during plan optimisation due to higher robustness should patient positioning errors occur.

Forecasting model for qualitative prediction of the results of patient-specific quality assurance based on planning and complexity metrics and their interrelations. Pilot study

Background

The purpose was to analyse the interrelations between planning and complexity metrics and gamma passing rates (GPRs) obtained from VMAT treatments and build the forecasting models for qualitative prediction (QD) of GPRs results.

Materials and method

802 treatment arcs from the plans prepared for the head and neck, thorax, abdomen, and pelvic cancers were analysed. The plans were verified by portal dosimetry and analysed twice using the gamma method with 3%|2mm and 2%|2mm acceptance criteria. The tolerance limit of GPR was 95%. Red, yellow, and green QDs were established for GPR examination. The interrelations were examined, as well as the analysis of effective differentiation of QD. Three models for QD forecasting based on discriminant analysis (DA), random decision forest (RDF) methods, and the hybrid model (HM) were built and evaluated.

Results

Most of the interrelations were small or moderate. The exception is correlations of the join function with the average number of monitor units per control point (R = 0.893) and the beam aperture with planning target volume (R = 0.897). While many metrics allow for the effective separation of the QDs from each other, the study shows that predicting the values of the QD is possible only through multi-component forecasting models, of which the HM is the most accurate (0.894).

Conclusion

Of the three models explored in this study, the HM, which uses DA methods to predict red QD and RDF methods to predict green and yellow QDs, is the most promising one.

Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans

Background and purpose

Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity.

Materials and methods

Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013-2021). Outlier complexities were defined as >95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR <90 %, were identified. The tool's impact was assessed after nine months of clinical use.

Results

Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p < 0.05) due to plan re-optimization.

Conclusions

ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.

The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy

Background and purpose

In sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses.

Materials and methods

A total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR).

Results

The correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA  ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR's determination coefficients (R2 ) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42).

Conclusions

CQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

Efficacious patient-specific QA for Vertebra SBRT using a high-resolution detector array SRS MapCHECK: AAPM TG-218 analysis

PURPOSE

Patient-specific quality assurance (PSQA) for vertebra stereotactic body radiation therapy (SBRT) presents challenges due to highly modulated small fields with high-dose gradients between the target and spinal cord. This study aims to explore the use of the SRS MapCHECK® (SRSMC) for vertebra SBRT PSQA.

METHODS

Twenty vertebra SBRT treatment plans including prescriptions 20 Gy/1 fraction and 24 Gy/2 fractions were selected for each of Millennium (M)-Multileaf Collimator (MLC), and high-definition (HD)-MLC. All 40 plans were measured using Gafchromic EBT3 film (film) and SRSMC, using the StereoPHAN phantom. Plan complexity was assessed using modulation complexity score (MCS), edge metric (EM) (mm-1), modulation factor (MU/cGy), and average leaf pair opening (ALPO) (mm) and its correlation with gamma-pass rate was investigated. The high dose gradient between the target and the spinal cord was analyzed for film and SRSMC and compared against the treatment planning system (TPS). Applying the methodology proposed by AAPM TG-218, action and tolerance values specific to the SRSMC for vertebra SBRT were determined for β values ranging from 5 to 8.

RESULTS

Film and SRSMC gamma-pass rates showed no correlation (p > 0.05). A moderate negative correlation (R = -0.57, p = 0.01) is present between EM and SRSMC 3%/1 mm gamma-pass rate for HD-MLC plans. Both film and SRSMC accurately measured high dose gradients between the target and the spinal cord (R2 > 0.86, p ≤ 0.05). Notably, dose-gradient of HD-MLC plans is 22% steeper and has a smaller standard deviation to M-MLC plans (p ≤ 0.05). Applying TG-218, the film tolerance limit was 96% with action limit 95% for 5%/1 mm (β = 6) and for the SRSMC tolerance limit was 97% with an action limit of 96% for 4%/1 mm (β = 6).

CONCLUSION

Our findings suggest that universal TG-218 limits may not be suitable for vertebra SBRT PSQA. This study demonstrates that SRSMC is a viable tool for vertebra SBRT PSQA, supported by TG-218 implementation of process-based tolerance and action limits.

Incorporating plan complexity into the statistical process control of volumetric modulated arc therapy pre-treatment verifications

BACKGROUND

Statistical process control (SPC) is a powerful statistical tool for process monitoring that has been highly recommended in healthcare applications, including radiation therapy quality assurance (QA). The AAPM TG-218 report described the clinical implementation of SPC for Volumetric Modulated Arc Therapy (VMAT) pre-treatment verifications, pointing out the need to adjust tolerance limits based on plan complexity. However, the quantification of plan complexity and its integration into SPC remains an unresolved challenge.

PURPOSE

The primary aim of this study is to investigate the incorporation of plan complexity into the SPC framework for VMAT pre-treatment verifications. The study explores and evaluates various strategies for this incorporation, discussing their merits and limitations, and provides recommendations for clinical application.

METHODS

A retrospective analysis was conducted on 309 VMAT plans from diverse anatomical sites using the PTW OCTAVIUS 4D device for QA measurements. Gamma Passing Rates (GPR) were obtained, and lower control limits were computed using both the conventional Shewhart method and three heuristic methods (scaled weighted variance, weighted standard deviations, and skewness correction) to accommodate non-normal data distributions. The 'Identify-Eliminate-Recalculate' method was employed for robust analysis. Eight complexity metrics were analyzed and two distinct strategies for incorporating plan complexity into SPC were assessed. The first strategy focused on establishing control limits for different treatment sites, while the second was based on the determination of control limits as a function of individual plan complexity. The study extensively examines the correlation between control limits and plan complexity and assesses the impact of complexity metrics on the control process.

RESULTS

The control limits established using SPC were strongly influenced by the complexity of treatment plans. In the first strategy, a clear correlation was found between control limits and average plan complexity for each site. The second approach derived control limits based on individual plan complexity metrics, enabling tailored tolerance limits. In both strategies, tolerance limits inversely correlated with plan complexity, resulting in all highly complex plans being classified as in control. In contrast, when plans were collectively analyzed without considering complexity, all the out-of-control plans were highly complex.

CONCLUSIONS

Incorporating plan complexity into SPC for VMAT verifications requires meticulous and comprehensive analysis. To ensure overall process control, we advocate for stringent control and minimization of plan complexity during treatment planning, especially when control limits are adjusted based on plan complexity.

A radiotherapy community data-driven approach to determine which complexity metrics best predict the impact of atypical TPS beam modeling on clinical dose calculation accuracy

PURPOSE

To quantify the impact of treatment planning system beam model parameters, based on the actual spread in radiotherapy community data, on clinical treatment plans and determine which complexity metrics best describe the impact beam modeling errors have on dose accuracy.

METHODS

Ten beam modeling parameters for a Varian accelerator were modified in RayStation to match radiotherapy community data at the 2.5, 25, 50, 75, and 97.5 percentile levels. These modifications were evaluated on 25 patient cases, including prostate, non-small cell lung, H&N, brain, and mesothelioma, generating 1,000 plan perturbations. Differences in the mean planned dose to clinical target volumes (CTV) and organs at risk (OAR) were evaluated with respect to the planned dose using the reference (50th-percentile) parameter values. Correlation between CTV dose differences, and 18 different complexity metrics were evaluated using linear regression; R-squared values were used to determine the best metric.

RESULTS

Perturbations to MLC offset and transmission parameters demonstrated the greatest changes in dose: up to 5.7% in CTVs and 16.7% for OARs. More complex clinical plans showed greater dose perturbation with atypical beam model parameters. The mean MLC Gap and Tongue & Groove index (TGi) complexity metrics best described the impact of TPS beam modeling variations on clinical dose delivery across all anatomical sites; similar, though not identical, trends between complexity and dose perturbation were observed among all sites.

CONCLUSION

Extreme values for MLC offset and MLC transmission beam modeling parameters were found to most substantially impact the dose distribution of clinical plans and careful attention should be given to these beam modeling parameters. The mean MLC Gap and TGi complexity metrics were best suited to identifying clinical plans most sensitive to beam modeling errors; this could help provide focus for clinical QA in identifying unacceptable plans.

Modeling of Gamma Index for Prediction of Pretreatment Quality Assurance in Stereotactic Body Radiation Therapy of the Liver

Purpose

The purpose of this study was to develop a predictive model to evaluate pretreatment patient-specific quality assurance (QA) based on treatment planning parameters for stereotactic body radiation therapy (SBRT) for liver carcinoma.

Materials and Methods

We retrospectively selected 180 cases of liver SBRT treated using the volumetric modulated arc therapy technique. Numerous parameters defining the plan complexity were calculated from the DICOM-RP (Radiotherapy Plan) file using an in-house program developed in MATLAB. Patient-specific QA was performed with global gamma evaluation criteria of 2%/2 mm and 3%/3 mm in a relative mode using the Octavius two-dimensional detector array. Various statistical tests and multivariate predictive models were evaluated.

Results

The leaf speed (MILS) and planning target volume size showed the highest correlation with the gamma criteria of 2%/2 mm and 3%/3 mm (P < 0.05). Degree of modulation (DoM), MCSSPORT, leaf speed (MILS), and gantry speed (MIGS) were predictors of global gamma pass rate (GPR) for 2%/2 mm (G22), whereas DoM, MCSSPORT, leaf speed (MILS) and robust decision making were predictors of the global GPR criterion of 3%/3 mm (G33). The variance inflation factor values of all predictors were <2, indicating that the data were not associated with each other. For the G22 prediction, the sensitivity and specificity of the model were 75.0% and 75.0%, respectively, whereas, for G33 prediction, the sensitivity and specificity of the model were 74.9% and 85.7%%, respectively.

Conclusions

The model was potentially beneficial as an easy alternative to pretreatment QA in predicting the uncertainty in plan deliverability at the planning stage and could help reduce resources in busy clinics.

Plan complexity metrics for head and neck VMAT competition plans

Previous plan competitions have largely focused on dose metric assessments. However, whether the submitted plans were realistic and reasonable from a quality assurance (QA) perspective remains unclear. This study aimed to investigate the relationship between aperture-based plan complexity metrics (PCM) in volumetric modulated arc therapy (VMAT) competition plans and clinical treatment plans verified through patient-specific QA (PSQA). In addition, the association of PCMs with plan quality was examined. A head and neck (HN) plan competition was held for Japanese institutions from June 2019 to July 2019, in which 210 competition plans were submitted. Dose distribution quality was quantified based on dose-volume histogram (DVH) metrics by calculating the dose distribution plan score (DDPS). Differences in PCMs between the two VMAT treatment plan groups (HN plan competitions held in Japan and clinically accepted HN VMAT plans through PSQA) were investigated. The mean (± standard deviation) DDPS for the 98 HN competition plans was 158.5 ± 20.6 (maximum DDPS: 200). DDPS showed a weak correlation with PCMs with a maximum r of 0.45 for monitor unit (MU); its correlation with some PCMs was "very weak." Significant differences were found in some PCMs between plans with the highest 20% DDPSs and the remaining plans. The clinical VMAT and competition plans revealed similar distributions for some PCMs. Deviations in PCMs for the two groups were comparable, indicating considerable variability among planners regarding planning skills. The plan complexity for HN VMAT competition plans increased for high-quality plans, as shown by the dose distribution. Direct comparison of PCMs between competition plans and clinically accepted plans showed that the submitted HN VMAT competition plans were realistic and reasonable from the QA perspective. This evaluation may provide a set of criteria for evaluating plan quality in plan competitions.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to delivery errors

BACKGROUND

The use of modulated techniques for intra-cranial stereotactic radiosurgery (SRS) results in highly modulated fields with small apertures, which may be susceptible to uncertainties in the delivery device.

PURPOSE

This study aimed to quantify the impact of simulated delivery errors on treatment plan dosimetry and how this is affected by treatment planning system (TPS), plan geometry, delivery technique, and plan complexity. A beam modelling error was also included as context to the dose uncertainties due to treatment delivery errors.

METHODS

Delivery errors were assessed for multiple-target brain SRS plans obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets, each with a prescription of 20 Gy. Of the final dataset of 54 plans, 51 were created using the volumetric modulated arc therapy (VMAT) technique and three used intensity modulated arc therapy (IMRT). Thirty-five plans were from the Varian Eclipse TPS, 17 from Elekta Monaco TPS, and one plan each from RayStation and Philips Pinnacle TPS. The errors introduced included: monitor unit calibration errors, multi-leaf collimator (MLC) bank offset, single MLC leaf offset, couch rotations, and collimator rotations. Dosimetric leaf gap (DLG) error was also included as a beam modelling error. Dose to targets was assessed via dose covering 98% of planning target volume (PTV) (D98%), dose covering 2% of PTV (D2%), and dose covering 99% of gross tumor volume (GTV) (D99%). Dose to organs at risk (OARs) was assessed using the volume of normal brain receiving 12 Gy (V12Gy), mean dose to normal brain, and maximum dose covering 0.03cc brainstem (D0.03cc). Plan complexity was also assessed via edge metric, modulation complexity score (MCS), mean MLC gap, mean MLC speed, and plan modulation (PM).

RESULTS

PTV D98% showed high robustness on average to most errors with the exception of a bank shift of 1.0 mm and large rotational errors ≥1.0° for either the couch or collimator. However, in some cases, errors close to or within generally accepted machine tolerances resulted in clinically relevant impacts. The greatest impact upon normal brain V12Gy, mean dose to normal brain, and D0.03cc brainstem was found for DLG error in alignment with other recent studies. All delivery errors had on average a minimal impact across these parameters. Comparing plans from the Monaco TPS and the Eclipse TPS, showed a lesser increase to V12Gy, mean dose to normal brain, and D0.03cc brainstem for Monaco plans (p < 0.01) when DLG error was simulated. Monaco plans also correlated to lower plan complexity. Using Spearman's correlation coefficient (r) a strong negative correlation (r ≤ -0.8) was found between the mean MLC gap and dose to OARs for DLG errors.

CONCLUSIONS

Reducing MLC complexity and using larger mean MLC gaps is recommended to improve plan robustness and reduce sensitivity to delivery and modelling errors. For cases in which the calculated dose distribution or dose indices are close to the clinically acceptable limits, this is especially important.

Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy

Background and purpose

Treatment plans in radiotherapy are subject to measurement-based pre-treatment verifications. In this study, plan complexity metrics (PCMs) were calculated per beam and used as input features to develop a predictive model. The aim of this study was to determine the robustness against differences in machine type and institutional-specific quality assurance (QA).

Material and methods

A number of 567 beams were collected, where 477 passed and 90 failed the pre-treatment QA. Treatment plans of different anatomical regions were included. One type of linear accelerator was represented. For all beams, 16 PCMs were calculated. A random forest classifier was trained to distinct between acceptable and non-acceptable beams. The model was validated on other datasets to investigate its robustness. Firstly, plans for another machine type from the same institution were evaluated. Secondly, an inter-institutional validation was conducted on three datasets from different centres with their associated QA.

Results

Intra-institutionally, the PCMs beam modulation, mean MLC gap, Q1 gap, and Modulation Complexity Score were the most informative to detect failing beams. Eighty-tree percent of the failed beams (15/18) were detected correctly. The model could not detect over-modulated beams of another machine type. Inter-institutionally, the model performance reached higher accuracy for centres with comparable equipment both for treatment and QA as the local institute.

Conclusions

The study demonstrates that the robustness decreases when major differences appear in the QA platform or in planning strategies, but that it is feasible to extrapolate institutional-specific trained models between centres with similar clinical practice. Predictive models should be developed for each machine type.

Plan quality and treatment efficiency assurance of two VMAT optimization for cervical cancer radiotherapy

To investigate the difference of the fluence map optimization (FMO) and Stochastic platform optimization (SPO) algorithm in a newly-introduced treatment planning system (TPS).

METHODS

34 cervical cancer patients with definitive radiation were retrospectively analyzed. Each patient has four plans: FMO with fixed jaw plans (FMO-FJ) and no fixed jaw plans (FMO-NFJ); SPO with fixed jaw plans (SPO-FJ) and no fixed jaw plans (SPO-NFJ). Dosimetric parameters, Modulation Complexity Score (MCS), Gamma Pass Rate (GPR) and delivery time were analyzed among the four plans.

RESULTS

For target coverage, SPO-FJ plans are the best ones (P ≤ 0.00). FMO plans are better than SPO-NFJ plans (P ≤ 0.00). For OARs sparing, SPO-FJ plans are better than FMO plans for mostly OARs (P ≤ 0.04). Additionally, SPO-FJ plans are better than SPO-NFJ plans (P ≤ 0.02), except for rectum V45Gy. Compared to SPO-NFJ plans, the FMO plans delivered less dose to bladder, rectum, colon V40Gy and pelvic bone V40Gy (P ≤ 0.04). Meanwhile, the SPO-NFJ plans showed superiority in MU, delivery time, MCS and GPR in all plans. In terms of delivery time and MCS, the SPO-FJ plans are better than FMO plans. FMO-FJ plans are better than FMO-NFJ plans in delivery efficiency. MCSs are strongly correlated with PCTV length, which are negatively with PCTV length (P ≤ 0.03). The delivery time and MUs of the four plans are strongly correlated (P ≤ 0.02). Comparing plans with fixed or no fixed jaw in two algorithms, no difference was found in FMO plans in target coverage and minor difference in Kidney_L Dmean, Mu and delivery time between PCTV width≤15.5 cm group and >15.5 cm group. For SPO plans, SPO-FJ plans showed more superiority in target coverage and OARs sparing than the SPO-NFJ plans in the two groups.

CONCLUSIONS

SPO-FJ plans showed superiority in target coverage and OARs sparing, as well as higher delivery efficiency in the four plans.

Automated evaluation for rapid implementation of knowledge-based radiotherapy planning models

PURPOSE

Knowledge-based planning (KBP) offers the ability to predict dose-volume metrics based on information extracted from previous plans, reducing plan variability and improving plan quality. As clinical integration of KBP is increasing there is a growing need for quantitative evaluation of KBP models. A .NET-based application, RapidCompare, was created for automated plan creation and analysis of Varian RapidPlan models.

METHODS

RapidCompare was designed to read calculation parameters and a list of reference plans. The tool copies the reference plan field geometry and structure set, applies the RapidPlan model, optimizes the KBP plan, and generates data for quantitative evaluation of dose-volume metrics. A cohort of 85 patients, divided into training (50), testing (10), and validation (25) groups, was used to demonstrate the utility of RapidCompare. After training and tuning, the KBP model was paired with three different optimization templates to compare various planning strategies in the validation cohort. All templates used the same set of constraints for the planning target volume (PTV). For organs-at-risk, the optimization template provided constraints using the whole dose-volume histogram (DVH), fixed-dose/volume points, or generalized equivalent uniform dose (gEUD). The resulting plans from each optimization approach were compared using DVH metrics.

RESULTS

RapidCompare allowed for the automated generation of 75 total plans for comparison with limited manual intervention. In comparing optimization techniques, the Dose/Volume and Lines optimization templates generated plans with similar DVH metrics, with a slight preference for the Lines technique with reductions in heart V30Gy and spinal cord max dose. The gEUD model produced high target heterogeneity.

CONCLUSION

Automated evaluation allowed for the exploration of multiple optimization templates in a larger validation cohort than would have been feasible using a manual approach. A final KBP model using line optimization objectives produced the highest quality plans without human intervention.

Dosimetric performance evaluation of the Halcyon treatment platform for stereotactic radiotherapy: A pooled study

With the advancement of radiotherapy equipment, stereotactic radiotherapy (SRT) has been increasingly used. Among the many radiotherapy devices, Halcyon shows promising applications. This article reviews the dosimetric performance such as plan quality, plan complexity, and gamma passing rates of SRT plans with Halcyon to determine the effectiveness and safety of Halcyon SRT plans. This article retrieved the last 5 years of PubMed studies on the effectiveness and safety of the Halcyon SRT plans. Two authors independently reviewed the titles and abstracts to decide whether to include the studies. A search was conducted to identify publications relevant to evaluating the dosimetric performance of SRT plans on Halcyon using the key strings Halcyon, stereotactic radiosurgery, SRT, stereotactic body radiotherapy, and stereotactic ablative radiotherapy. A total of 18 eligible publications were retrieved. Compared to SRT plans on the TrueBeam, the Halcyon has advantages in terms of plan quality, plan complexity, and gamma passing rates. The high treatment speed of SRT plans on the Halcyon is impressive, while the results of its plan evaluation are also encouraging. As a result, Halcyon offers a new option for busy radiotherapy units while significantly improving patient comfort in treatment. For more accurate results, additional relevant publications will need to be followed up in subsequent studies.

Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods

OBJECTIVE

Gamma passing rate (GPR) predictions using machine learning methods have been explored for treatment verification of radiotherapy plans. However, these methods presented datasets with unbalanced number of plans having different treatment conditions (heterogeneous datasets), such as anatomical sites or dose per fractions, leading to lower model interpretability and prediction performance.

METHODS

We investigated the impact of the dataset composition on GPR binary classification (pass/fail) using random forest (RF), XG-boost, and neural network (NN) models. 945 plans were used to create one reference dataset (randomly assembled) and 24 customized datasets that considered four heterogeneity factors independently (anatomical region, number of arcs, dose per fraction, and treatment unit). 309 predictor features were extracted and calculated from plan parameters, modulation complexity metrics, and radiomic analysis (leave-trajectory maps, 3D dose distributions, and portal dosimetry images). The models' performances were measured using the area under the curve from the receiver operating characteristic (ROC-AUC).

RESULTS

Radiomics features for reference models increased ROC-AUC values up to 13%, 15%, and 5% for RF, XG-Boost, and NN, respectively. The datasets with higher heterogeneous conditions presented the lower ROC-AUC values (RF: 0.72 ± 0.11, XG-Boost: 0.67 ± 0.1, NN: 0.89 ± 0.05) compared to models with less heterogeneous treatment conditions (RF: 0.88 ± 0.06, XG-Boost: 0.89 ± 0.07, NN: 0.98 ± 0.01). The ten most important features for each heterogeneity dataset group demonstrated their correlation with the treatments' physical aspects and GPR prediction.

CONCLUSION

Improvements in data generalization and model performances can be associated with datasets having similar treatment conditions. This analysis might be implemented to evaluate the dataset quality and model consistency of further ML applications in radiotherapy.

ADVANCES IN KNOWLEDGE

Dataset heterogeneities decrease ML model performance and reliability.

Characterizing the interplay of treatment parameters and complexity and their impact on performance on an IROC IMRT phantom using machine learning

AIM OF THE STUDY

To elucidate the important factors and their interplay that drive performance on IMRT phantoms from the Imaging and Radiation Oncology Core (IROC).

METHODS

IROC's IMRT head and neck phantom contains two targets and an organ at risk. Point and 2D dose are measured by TLDs and film, respectively. 1,542 irradiations between 2012-2020 were retrospectively analyzed based on output parameters, complexity metrics, and treatment parameters. Univariate analysis compared parameters based on pass/fail, and random forest modeling was used to predict output parameters and determine the underlying importance of the variables.

RESULTS

The average phantom pass rate was 92% and has not significantly improved over time. The step-and-shoot irradiation technique had significantly lower pass rates that significantly affected other treatment parameters' pass rates. The complexity of plans has significantly increased with time, and all aperture-based complexity metrics (except MCS) were associated with the probability of failure. Random forest-based prediction of failure had an accuracy of 98% on held-out test data not used in model training. While complexity metrics were the most important contributors, the specific metric depended on the set of treatment parameters used during the irradiation.

CONCLUSION

With the prevalence of errors in radiotherapy, understanding which parameters affect treatment delivery is vital to improve patient treatment. Complexity metrics were strongly predictive of irradiation failure; however, they are dependent on the specific treatment parameters. In addition, the use of one complexity metric is insufficient to monitor all aspects of the treatment plan.

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy

BACKGROUND AND PURPOSE

Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis.

MATERIALS AND METHODS

A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (M_t), while the other one at achieving the highest mean sample quality (R_t). Four subsequent updates were accomplished. The goodness, robustness and quality of the outcomes were measured and compared to those of the common ancestor. Plan quality was assessed through the Plan Quality Metric (PQM) and plan complexity was monitored.

RESULTS

Both update procedures allowed for an increase in the OARs sparing between +3.9 % and +19.2 % compared to plans generated by a human planner. Target coverage and homogeneity slightly reduced [-0.2 %;-14.7 %] while plan complexity showed only minor changes. Increasing the sample size resulted in more reliable predictions and improved goodness-of-fit, while increasing the mean sample quality improved the outcomes but slightly reduced the models reliability.

CONCLUSIONS

Repeated updates of clinical KBP models can enhance their robustness, reliability and the overall quality of automatically generated plans. The periodical expansion of the model sample accompanied by the removal of the unacceptable low quality plans should maximize the benefits of the updates while limiting the associated workload.

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans

Background and purpose

Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans.

Materials and methods

A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected. Sixty-five indicators including 12 delivery parameters (DP) and 53 complexity metrics (CM) were extracted using a dedicated software library. Additionally, 174 radiomics features (RF) were extracted from the plans' sinograms. Three groups of variables were formed: A (DP), B (DP + CM) and C (DP + CM + RF). Regression models were trained to predict the gamma index passing rate P R γ (3%G, 2mm) and the impact of each group of variables was investigated. ROC-AUC analysis measured the ability of the models to accurately discriminate between 'deliverable' and 'non-deliverable' plans.

Results

The best performance was achieved by model C which allowed detecting around 16% and 63% of the 'deliverable' plans with 100% sensitivity for the two TPSs, respectively. In a real clinical scenario, this would have decreased the whole PSQA workload by approximately 35%.

Conclusions

The combination of delivery parameters, complexity metrics and sinogram radiomics features allows for robust and reliable PSQA gamma passing rate predictions and high-sensitivity detection of a fraction of deliverable plans for one of the two TPSs. Promising yet improvable results were obtained for the other one. The results foster a future adoption of vPSQA programs for HT.

Evaluation of measures related to dosimetric uncertainty of VMAT plans

Dosimetric uncertainty is most often not included in the process of creating and selecting plans for treatment. Treatment planning and the physician's choice of treatment plan is instead often based only on evaluation of clinical goals of the calculated absorbed dose distribution. Estimation of the dosimetric uncertainty could potentially have impact in the process of comparing and selecting volumetric modulated arc therapy (VMAT) plans. In this study, different measures for estimation of dosimetric uncertainty based on treatment plan parameters for plans with similar dose distributions were evaluated. VMAT plans with similar dose distributions but with different treatment plan designs were created using three different optimization methods for each of ten patient cases (tonsil and prostate cancer). Two plans were optimized in Eclipse, one with and one without the use of aperture shape controller, and one plan was optimized in RayStation. The studied measures related to dosimetric uncertainty of treatment plans were aperture-based complexity metric analysis, investigation of modulation level of multi leaf collimator leaves, gantry speed and dose rate, quasi-3D measurements and evaluation of simulations of realistic delivery variations. The results showed that there can be variations in dosimetric uncertainty for treatment plans with similar dose distributions. Dosimetric uncertainty assessment could therefore have impact on the choice of plan to be used for treatment and lead to a decrease in the uncertainty level of the delivered absorbed dose distribution. This study showed that aperture shape complexity had a larger impact on dosimetric uncertainty compared to modulation level of MLC, gantry speed and dose rate.

Using machine learning to predict gamma passing rate in volumetric-modulated arc therapy treatment plans

PURPOSE

This study aims to develop an algorithm to predict gamma passing rate (GPR) in the volumetric-modulated arc therapy (VMAT) technique.

MATERIALS AND METHODS

A total of 118 clinical VMAT plans, including 28 mediastina, 25 head and neck, 40 brains intensity-modulated radiosurgery, and 25 prostate cases, were created in RayStation treatment planning system for Edge and TrueBeam linacs. In-house scripts were developed to compute Modulation indices such as plan-averaged beam area (PA), plan-averaged beam irregularity (PI), total monitor unit (MU), leaf travel/arc length, mean dose rate variation, and mean gantry speed variation. Pretreatment verifications were performed on ArcCHECK phantom with SNC software. GPR was calculated with 3%/2 mm and 10% threshold. The dataset was randomly split into a training (70%) and a test (30%) dataset. A random forest regression (RFR) model and support vector regression (SVR) with linear kernel were trained to predict GPR using the complexity metrics as input. The prediction performance was evaluated by calculating the mean absolute error (MAE), R² , and root mean square error (RMSE).

RESULTS

RMSEs at γ 3%/2 mm for RFR and SVR were 1.407 ± 0.103 and 1.447 ± 0.121, respectively. MAE was 1.14 ± 0.084 for RFR and 1.101 ± 0.09 for SVR. R² was equal to 0.703 ± 0.027 and 0.689 ± 0.053 for RFR and SVR, respectively. GPR of 3%/2 mm with a 10% threshold can be predicted with an error smaller than 3% for 94% of plans using RFR and SVR models. The most important metrics that had the greatest impact on how accurately GPR can be predicted were determined to be the PA, PI, and total MU.

CONCLUSION

In terms of its prediction values and errors, SVR (linear) appeared to be comparable with RFR for this dataset. Based on our results, the PA, PI, and total MU calculations may be useful in guiding VMAT plan evaluation and ultimately reducing uncertainties in planning and radiation delivery.

Evaluating the effect of high-density measurement mode on patient-specific quality assurance for head and neck cancer with ArcCHECK

The high-density measurement (HDm) mode of the ArcCHECK device can achieve a twofold resolution enhancement compared to the standard measurement (Sm) mode. The aim of this study was to evaluate the effect of HDm on the gamma passing rate (GPR) for the patient-specific quality assurance (PSQA) in head and neck cancer. We retrospectively evaluated 30 patients who underwent volumetric modulated arc therapy (VMAT) for head and neck cancer. Absolute gamma analysis was performed on Sm and HDm data. We also investigated correlations between the modulation complexity score for VMAT (MCSv) and differences in the GPR between the two measurement modes. The global GPR of Sm and HDm was 81.0% ± 8.4% and 82.6% ± 7.6% for the 2%/2 mm criterion, 94.0% ± 4.1% and 94.9% ± 3.6% for the 3%/2 mm criterion, and 96.6% ± 2.4% and 97.0% ± 2.4% for the 3%/3 mm criterion, respectively. HDm slightly improved GPR (p < 0.01) for the 2%/2 mm criterion. Differences in GPR between Sm and HDm for the 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria were 1.6% ± 3.0%, 0.8% ± 2.0%, and 0.4% ± 1.2%, respectively. No correlation was identified between the MCSv and the difference in GPR between Sm and HDm. Despite an improvement in GPR with HDm, the difference in GPR between Sm and HDm was approximately 2% even when the tighter criteria were used. Moreover, the change in the GPR between Sm and HDm did not depend on plan complexity. Thus, the effect of HDm on GPR is limited for the PSQA in VMAT for head and neck cancer.

Prediction of VMAT delivery accuracy using plan modulation complexity score and log-files analysis

The purpose of this study was to develop a predictive model based on plan complexity metrics and linac log-files analysis to classify the dosimetric accuracy of VMAT plans. A total of 612 VMAT plans, corresponding to 1224 arcs, were analyzed. All VMAT arcs underwent pre-treatment verification that was performed by means of the dynamic log-files generated by the linac. The comparison of predicted (by TPS) and measured (by log-files) integral fluences was performed using γ-analysis in terms of the percentage of points with γ-value smaller than one (γ%) and using a stringent 2%(local)/2mm criteria. This γ-analysis was performed by a commercial software LinacWatch. The action limits (AL) were derived from the mean values, standard deviations and the confidence limit (CL) of the γ% distribution. A plan complexity metric, the modulation complexity score (MCS), based on the aperture beam area variability and leaf sequence variability was used as input variable of the model. A binary logistic regression (LR) model was developed to classify QA results as "pass" (γ%≥AL) or "fail" (γ%<AL). Receiver operator characteristics (ROC) curves were used to determine the optimal MCS threshold to flag "failed" plans that need to be re-optimized. The model reliability was evaluated stratifying the plans in training, validation and testing groups. The confidence and action limits for γ% were found 20.1% and 79.9%, respectively. The accuracy of the model for the training and testing dataset was 97.4% and 98.0%, respectively. The optimal MCS threshold value for the identification of failed plans was 0.142, providing a true positive rate able to flag the plans failing QA of 91%. In clinical routine, the use of this MCS threshold may allow the prompt identification of overly modulated plans, then reducing the number of QA failures and improving the quality of VMAT plans used for treatment.

Investigation on the impact of the leaf trailing effect using the Halcyon integrated platform system

PURPOSE

The double-stacked design of the Halcyon multileaf collimator (MLC) presents new challenges for treatment planning systems (TPSs). The leaf trailing effect has recently been described as the result of the interplay between the fluence transmitted through the leaf tip ends of each MLC layer. This effect makes the dosimetric leaf gap (DLG) dependent on the distance between the leaves of different layers (trailing distance) and is not adequately modeled by the Eclipse TPS. The purpose of our study was to investigate and report the dose discrepancies produced by these limitations in clinical plans and to explore how these discrepancies can be mitigated and avoided.

METHODS

The integrated platform with the Halcyon v2 system, Eclipse and Aria v15.6, was used. The dose discrepancies were obtained with EPID images and the portal dosimetry software and validated using radiochromic film dosimetry. The results for the AIDA commissioning test and for nine selected clinical beams with the sliding window intensity modulated radiotherapy (dIMRT) technique were thoroughly analyzed and presented. First, the DICOM RT plans were exported and the fluences were computed using different leaf tip models, and then were compared. Second, the detailed characteristics of the corresponding leaf sequences were investigated. Finally, modified DICOM RT plans were created in which the non-collimating (backup) leaves were retracted 2 mm to increase the leaf trailing distance, the modified plans were imported back into the TPS and the measurements were repeated. Dedicated in-house tools were developed in Python to carry out all analyses.

RESULTS

Dose discrepancies greater than 10% and regions of gamma failure were found in both the AIDA test and clinical beams using static-gantry dIMRT. Fluence analysis highlighted that the discrepancies were due to limitations in the MLC model implemented in the TPS. Analysis of leaf sequences indicated that regions of failure were associated with very low leaf speeds and virtually motionless leaves within the beam aperture. Some of these discrepancies were mitigated by increasing the trailing distance of the non-collimating leaves without affecting the beam aperture, but this strategy was not possible in regions where the leaves from both layers actively defined the beam aperture.

CONCLUSIONS

Current limitations of the MLC model in Eclipse produced discrepancies between calculated and delivered doses in clinical beams that caused plan-specific quality assurance failures and interruptions in the clinical workflow. Careful evaluation of the clinical plans produced by Eclipse for the Halcyon is recommended, especially for static gantry dIMRT treatments. Some characteristics of leaf sequences are problematic and should be avoided in clinical plans and, in general, a better leaf tip model is needed. This is particularly important in adaptive radiotherapy treatments, where the accuracy and reliability of TPS dose calculations are of the utmost importance.

Plan quality assessment in clinical practice: Results of the 2020 ESTRO survey on plan complexity and robustness

PURPOSE

Plan complexity and robustness are two essential aspects of treatment plan quality but there is a great variability in their management in clinical practice. This study reports the results of the 2020 ESTRO survey on plan complexity and robustness to identify needs and guide future discussions and consensus.

METHODS

A survey was distributed online to ESTRO members. Plan complexity was defined as the modulation of machine parameters and increased uncertainty in dose calculation and delivery. Robustness was defined as a dose distribution's sensitivity towards errors stemming from treatment uncertainties, patient setup, or anatomical changes.

RESULTS

A total of 126 radiotherapy centres from 33 countries participated, 95 of them (75%) from Europe and Central Asia. The majority controlled and evaluated plan complexity using monitor units (56 centres) and aperture shapes (38 centres). To control robustness, 98 (97% of question responses) photon and 5 (50%) proton centres used PTV margins for plan optimization while 75 (94%) and 5 (50%), respectively, used margins for plan evaluation. Seventeen (21%) photon and 8 (80%) proton centres used robust optimisation, while 10 (13%) and 8 (80%), respectively, used robust evaluation. Primary uncertainties considered were patient setup (photons and protons) and range calculation uncertainties (protons). Participants expressed the need for improved commercial tools to control and evaluate plan complexity and robustness.

CONCLUSION

Clinical implementation of methods to control and evaluate plan complexity and robustness is very heterogeneous. Better tools are needed to manage complexity and robustness in treatment planning systems. International guidelines may promote harmonization.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

Machine learning generated decision boundaries for prediction and exploration of patient-specific quality assurance failures in Stereotactic Radiosurgery plans

INTRODUCTION

Stereotactic Radiosurgery (SRS) is a form of radiotherapy treatment during which high radiation dose is delivered in a single or few fractions. These treatments require highly conformal plans with steep dose gradients which can result in an increase in plan complexity prompting the need for stringent pre-treatment patient specific quality assurance (QA) measurements to ensure the planned and measured dose distributions agree within clinical standards. Complexity scores and machine learning (ML) techniques may help with prediction of QA outcomes however interpretability and usability of those results continues to be an area of study. This study investigates the use of plan complexity metrics as input for an ML model to allow for prediction of QA outcomes for SRS plans as measured via 3D phantom dose verification. Explorations into interpretability and predictive performance changes as model dimensionality increases, as well as a prospective in-clinic implementation using the resulting model were also performed.

METHODS

498 plans (1571 VMAT arcs) were processed via in-house script to generate several complexity scores. 3D phantom dose verification measurement results were extracted and classified as pass or failure (with failures defined as below 95% voxel agreement passing 3%/1mm gamma criteria with 10% threshold,) and 1472 of the arcs were split into training and testing sets, with 99 arcs as a sequential holdout set. A z-score scaler was trained on the training set and used to scale all other sets. Variations of MLC leaf movement variability, aperture complexity, and leaf size and MU at control point weighted target area scores were used as input to a Support Vector Classifier to generate a series of 1-D, 2-D, and 5-D decision boundaries. The best performing 5D model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements for 38 patient plans (112 arcs). The decision to order 3D phantom dose verification measurements was recorded before and after prediction.

RESULTS

Best performing 1-D threshold, and 2-D prediction models with best performance produced a QA failure recall and QA passing recall of 1.00 and 0.55, and 0.82 and 0.82 respectively. Best performing 5-D prediction model produced a QA failure recall (sensitivity) of 1.00, and QA passing recall (specificity) of 0.72. This model was then used within a prospective in-clinic study providing predictions to physicists prior to ordering 3D phantom dose verification measurements and achieved a QA failure recall of 1.00 and QA passing recall of 0.58. The decision to order 3D phantom dose verification measurements was recorded before and after measurement. A single initially unidentified failing plan of the prospective cohort was successfully predicted to fail by the model.

CONCLUSION

Implementation of complexity score based prediction models for SRS would allow for support of a clinician's decision to reduce time spent performing QA measurements, and avoid patient treatment delays (i.e. in case of QA failure). This article is protected by copyright. All rights reserved.

Management and work-up procedures of patients with head and neck malignancies treated by radiation

Radiotherapy alone or in association with systemic treatment plays a major role in the treatment of head and neck tumours, either as a primary treatment or as a postoperative modality. The management of these tumours is multidisciplinary, requiring particular care at every treatment step. We present the update of the recommendations of the French Society of Radiation Oncology on the radiotherapy of head and neck tumours from the imaging work-up needed for optimal selection of treatment volume, to optimization of the dose distribution and delivery.

Impact of the dose quantity used in MV photon optimization on dose distribution, robustness, and complexity

PURPOSE

Convolution/superposition algorithms used in megavoltage (MV) photon radiotherapy model radiation transport in water, yielding dose to water-in-water (D_w,w ). Advanced algorithms constitute a step forward, but their dose distributions in terms of dose to medium-in-medium (D_m,m ) or dose to water-in-medium (D_w,m ) can be problematic when used in plan optimization due to their different dose responses to some atomic composition heterogeneities. Failure to take this into account can lead to undesired overcorrections and thus to unnoticed suboptimal and unrobust plans. Dose to reference-like medium (D_ref,m* ) was recently introduced to overcome these limitations while ensuring accurate transport. This work evaluates and compares the performance of these four dose quantities in planning target volume (PTV)-based optimization.

METHODS

We considered three cases with heterogeneities inside the PTV: virtual phantom with water surrounded by bone; head and neck; and lung. These cases were planned with volumetric modulated arc therapy (VMAT) technique, optimizing with the same setup and objectives for each dose quantity. We used different algorithms of the Varian Eclipse treatment planning system (TPS): Acuros XB (AXB) for D_m,m and D_w,m , and Analytical Anisotropic Algorithm (AAA) for D_w,w . D_ref,m* was obtained from D_m,m distributions using an in-house software considering water as the reference medium (D_w,m* ). The optimization process consisted of: (1) common first optimization, (2) dose distribution computed for each quantity, (3) re-optimization, and (4) final calculation for each dose quantity. The dose distribution, robustness to patient setup errors, and complexity of the plans were analyzed and compared.

RESULTS

The quantities showed similar dose distributions after the optimization but differed in terms of plan robustness. The cases with soft tissue and high-density heterogeneities followed the same pattern. For AXB D_m,m , cold regions appeared in the heterogeneities after the first optimization. They were compensated in the second optimization through local fluence increases, but any positional mismatch impacted robustness, with clinical target volume (CTV) variations from the nominal scenario around +3% for bone and up to +7% for metal. For AXB D_w,m the pattern was inverse (hot regions compensated by fluence decreases) and more pronounced, with CTV dose variations around -7% for bone and up to -17% for metal. Neither AXB D_w,m* nor AAA D_w,w presented these dose inhomogeneities, which resulted in more robust plans. However, D_w,w differed markedly from the other quantities in the lung case because of its lower radiation transport accuracy. AXB D_m,m was the most complex of the four dose quantities and AXB D_w,m* the least complex, though we observed no major differences in this regard.

CONCLUSIONS

The dose quantity used in MV photon optimization can affect plan robustness. D_w,w distributions from convolution/superposition algorithms are robust but may not provide sufficient radiation transport accuracy in some cases. D_m,m and D_w,m from advanced algorithms can compromise robustness because their different responses to some composition heterogeneities introduce additional fluence compensations. D_ref,m* offers advantages in plan optimization and evaluation, producing accurate and robust plans without increasing complexity. D_ref,m* can be easily implemented as a built-in feature of the TPS and can facilitate and simplify the treatment planning process when using advanced algorithms. Final reporting can be kept in D_m,m or D_w,m for clinical correlations.

Verification of an optimizer algorithm by the beam delivery evaluation of intensity-modulated arc therapy plans

Background

In the case of dynamic radiotherapy plans, the fractionation schemes can have dosimetric effects. Our goal was to define the effect of the fraction dose on the plan quality and the beam delivery.

Materials and methods

Treatment plans were created for 5 early-stage lung cancer patients with different dose schedules. The planned total dose was 60 Gy, fraction dose was 2 Gy, 3 Gy, 5 Gy, 12 Gy and 20 Gy. Additionally renormalized plans were created by changing the prescribed fraction dose after optimization. The dosimetric parameters and the beam delivery parameters were collected to define the plan quality and the complexity of the treatment plans. The accuracy of dose delivery was verified with dose measurements using electronic portal imaging device (EPID).

Results

The plan quality was independent from the used fractionation scheme. The fraction dose could be changed safely after the optimization, the delivery accuracy of the treatment plans with changed prescribed dose was not lower. According to EPID based measurements, the high fraction dose and dose rate caused the saturation of the detector, which lowered the gamma passing rate. The aperture complexity score, the gantry speed and the dose rate changes were not predicting factors for the gamma passing rate values.

Conclusions

The plan quality and the delivery accuracy are independent from the fraction dose, moreover the fraction dose can be changed safely after the dose optimization. The saturation effect of the EPID has to be considered when the action limits of the quality assurance system are defined.

The impact of different optimization strategies on the agreement between planned and delivered doses during volumetric modulated arc therapy for total marrow irradiation

Aim of the study

To evaluate the agreement between planned and delivered doses and its potential correlation with the plans' complexity subjected to dosimetric verification.

Material and methods

Four isocentre volumetric modulated arc therapy for total marrow irradiation plans optimized simultaneously with (P1) and without (P2) MU reduction were evaluated dosimetrically by γ method performed in a global mode for 4 combinations of γ-index criteria (2%/2 mm, 2%/3 mm, 3%/2 mm, and 3%/3 mm). The evaluation was conducted for 4 regions (head and neck, chest, abdomen and upper pelvis, and lower pelvis and thighs) that were determined geometrically by the isocentres. The Wilcoxon test was used to detect significant differences between γ passing rate (GPR) analysis results for the P1 and P2 plans. The Pearson correlation was used to check the relationship between GPR and the plans' complexity.

Results

Except for the head and neck region, the P2 plans had better GPRs than the P1 plans. Only for hard combinations of γ-index criteria (i.e. 2%/3 mm, 2%/2 mm) were the GPRs differences between P1 and P2 clinically meaningful, and they were detected in the chest, abdomen and upper pelvis, and lower pelvis and thighs regions. The highest correlations between GPR and the indices describing the plans' complexity were found for the chest region. No correlation was found for the head and neck region.

Conclusions

The P2 plans showed better agreement between planned and delivered doses compared to the P1 plans. The GPR and the plans' complexity depend on the anatomy region and are most important for the chest region.

Head and neck radiotherapy on the MR linac: a multicenter planning challenge amongst MRIdian platform users

Purpose

Purpose of this study is to evaluate plan quality on the MRIdian (Viewray Inc., Oakwood Village, OH, USA) system for head and neck cancer (HNC) through comparison of planning approaches of several centers.

Methods

A total of 14 planners using the MRIdian planning system participated in this treatment challenge, centrally organized by ViewRay, for one contoured case of oropharyngeal carcinoma with standard constraints for organs at risk (OAR). Homogeneity, conformity, sparing of OARs, and other parameters were evaluated according to The International Commission on Radiation Units and Measurements (ICRU) recommendations anonymously, and then compared between centers. Differences amongst centers were assessed by means of Wilcoxon test. Each plan had to fulfil hard constraints based on dose–volume histogram (DVH) parameters and delivery time. A plan quality metric (PQM) was evaluated. The PQM was defined as the sum of 16 submetrics characterizing different DVH goals.

Results

For most dose parameters the median score of all centers was higher than the threshold that results in an ideal score. Six participants achieved the maximum number of points for the OAR dose parameters, and none had an unacceptable performance on any of the metrics. Each planner was able to achieve all the requirements except for one which exceeded delivery time. The number of segments correlated to improved PQM and inversely correlated to brainstem D_0.1cc and to Planning Target Volume1 (PTV) D_0.1cc. Total planning experience inversely correlated to spinal canal dose.

Conclusion

Magnetic Resonance Image (MRI) linac-based planning for HNC is already feasible with good quality. Generally, an increased number of segments and increasing planning experience are able to provide better results regarding planning quality without significantly prolonging overall treatment time.

Supplementary Information

The online version of this article (10.1007/s00066-021-01771-8) contains supplementary material, which is available to authorized users.

Evaluation of treatment plan quality for head and neck IMRT: a multicenter study

Intensity-modulated radiotherapy (IMRT) treatment planning for head and neck cancer is challenging and complex due to many organs at risk (OAR) in this region. The experience and skills of planners may result in substantial variability of treatment plan quality. This study assessed the performance of IMRT planning in Malaysia and observed plan quality variation among participating centers. The computed tomography dataset containing contoured target volumes and OAR was provided to participating centers. This is to control variations in contouring the target volumes and OARs by oncologists. The planner at each center was instructed to complete the treatment plan based on clinical practice with a given prescription, and the plan was analyzed against the planning goals provided. The quality of completed treatment plans was analyzed using the plan quality index (PQI), in which a score of 0 indicated that all dose objectives and constraints were achieved. A total of 23 plans were received from all participating centers comprising 14 VMAT, 7 IMRT, and 2 tomotherapy plans. The PQI indexes of these plans ranged from 0 to 0.65, indicating a wide variation of plan quality nationwide. Results also reported 5 out of 21 plans achieved all dose objectives and constraints showing more professional training is needed for planners in Malaysia. Understanding of treatment planning system and computational physics could also help in improving the quality of treatment plans for IMRT delivery.

Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning

INTRODUCTION

Previous literature has shown general trade-offs between plan complexity and resulting quality assurance (QA) outcomes. However, existing solutions for controlling this trade-off do not guarantee corresponding improvements in deliverability. Therefore, this work explored the feasibility of an optimization framework for directly maximizing predicted QA outcomes of plans without compromising the dosimetric quality of plans designed with an established knowledge-based planning (KBP) technique.

MATERIALS AND METHODS

A support vector machine (SVM) was developed - using a database of 500 previous VMAT plans - to predict gamma passing rates (GPRs; 3%/3mm percent dose-difference/distance-to-agreement with local normalization) based on selected complexity features. A heuristic, QA-based optimization (QAO) framework was devised by utilizing the SVM model to iteratively modify mechanical treatment features most commonly associated with suboptimal GPRs. Specifically, leaf gaps (LGs) <50 mm were widened by random amounts, which impacts all aperture-based complexity features. 13 prostate KBP-guided VMAT plans were optimized via QAO using user-specified maximum LG displacements before corresponding changes in predicted GPRs and dose were assessed.

RESULTS

Predicted GPRs increased by an average of 1.14 ± 1.25% (p = 0.006) with QAO using a 3 mm maximum random LG displacement. There were small differences in dose, resulting in similarly small changes in tumor control probability (maximum increase = 0.05%) and normal tissue complication probabilities in the bladder, rectum, and femoral heads (maximum decrease = 0.2% in the rectum).

CONCLUSION

This study explored the feasibility of QAO and warrants future investigations of further incorporating QA endpoints into plan optimization.

On the reduction of aperture complexity in kidney SABR

Background

Stereotactic ablative body radiotherapy (SABR) of primary kidney cancers is confounded by motion. There is a risk of interplay effect if the dose is delivered using volumetric modulated arc therapy (VMAT) and flattening filter‐free (FFF) dose rates due to target and linac motion. This study aims to provide an efficient way to generate plans with minimal aperture complexity.

Methods

In this retrospective study, 62 patients who received kidney SABR were reviewed. For each patient, two plans were created using internal target volume based motion management, on the average intensity projection of a four‐dimensional CT. In the first plan, optimization was performed using a knowledge‐based planning model based on delivered clinical plans in our institution. In the second plan, the optimization was repeated, with a maximum monitor unit (MU) objective applied in the optimization. Dose‐volume, conformity, and complexity metric (with the field edge metric and the modulation complexity score) were compared between the two plans. Results are shown in terms of median (first quartile — third quartile).

Results

Similar dosimetry was obtained with and without the utilization of an objective on the MU. However, complexity was reduced by using the objective on the MUs (modulation complexity score = 0.55 (0.50–0.61) / 0.33 (0.29–0.36), P‐value < 10⁻¹⁰, with/without the MU objective). Reduction of complexity was driven by a larger aperture area (area aperture variability = 0.68 (0.64–0.73) / 0.42 (0.37–0.45), P‐value < 10⁻¹⁰, with/without the MU objective). Using the objective on the MUs resulted in a more spherical dose distribution (sphericity 50% isodose = 0.73 (0.69–0.75) / 0.64 (0.60–0.68), P‐value < 10⁻⁸, with/without the MU objective) reducing dose to organs at risk given respiratory motion.

Conclusions

Aperture complexity is reduced in kidney SABR by using an objective on the MU delivery with VMAT and FFF dose rate.

Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance

PURPOSE

Our purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019.

METHODS AND MATERIALS

In this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements.

RESULTS

In the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement.

CONCLUSIONS

It is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.

Robotic MLC-based plans: A study of plan complexity

PURPOSE

The utility of complexity metrics has been assessed for IMRT and VMAT treatment plans, but this analysis has never been performed for CyberKnife (CK) plans. The purpose of this study is to perform a complexity analysis of CK MLC plans, adapting and computing complexity indices previously defined for IMRT plans. Metrics were used to compare the complexity of plans created by two optimization systems and to study correlations between plan complexity and patient-specific quality assurance (PSQA) results. Relationships between pairs of metrics were also analyzed to get insight into possible interdependencies.

METHODS

Two independent in-house software platforms were developed to compute six complexity metrics: modulation complexity score (MCS), edge metric (EM), plan irregularity (PI), plan modulation (PM), leaf gap (LG), and small aperture score (SAS10). MCS and PM definitions were adapted to account for CK plans characteristics. The computed metrics were used to compare the existing optimization algorithms (sequential and VOLO) in terms of plan complexity over 24 selected cases. Metrics were then computed over a large number (103) of VOLO SBRT clinical plans from different treatment sites, mainly liver, prostate, pancreas, and spine. Pearson's r was used to study relationships between each pair of metrics. Correlation between complexity indices and PSQA results expressed as gamma index passing rates (GPR) at (3%, 1 mm) and (2%, 1 mm) was finally analyzed. Correlation was regarded as weak for absolute Pearson's r values in the range 0.2-0.39, moderate 0.4-0.59, strong 0.6-0.79, and very strong 0.8-1.

RESULTS

When compared to VOLO, sequential plans exhibited a higher complexity degree, showing lower MCS and LG values and higher EM, PM and PI values. Differences were significant for 5/6 metrics (Wilcoxon P < 0.05). The analysis of VOLO clinical plans highlighted different degrees of complexity among plans from different treatment sites, increasing from liver to prostate, pancreas, and finally, spine. Analysis of dependencies between pairs of metrics showed a very strong significant negative correlation (P < 0.01), respectively, between MCS and PM (r = -0.97), and EM and LG (-0.82). Most of the remaining pairs showed moderate to strong correlations with the exception of PI, which showed weaker correlations with the other metrics. A moderate significant correlation was observed with GPR values both at (3%, 1 mm) and (2%, 1 mm) for all metrics except PI, which showed no correlation.

CONCLUSIONS

Modulation complexity metrics were computed for CK MLC-based plans for the first time and some metrics' definitions were adapted to CK plans peculiarities. The computed metrics proved a useful tool for comparing optimization algorithms and for characterizing CK clinical plans. Strong and very strong correlations were found between some pairs of metrics. Some significant correlations were found with PSQA GPR, indicating that some indices are promising for rationalizing and reducing PSQA workload. Our results set the basis for evaluating new optimization algorithms and TPS versions in the future, as well as for comparing the complexity of CK MLC-based plans in multicenter and multiplatform comparisons.

Description and evaluation of a new volumetric-modulated arc therapy plan complexity metric

This study describes a new plan complexity metric for volumetric-modulated arc therapy (VMAT) and evaluates the relationship of this metric with the VMAT dosimetric accuracy. The new modulation complexity score for VMAT (NMCSv) that is based on the aperture shape and multi-leaf collimator (MLC) leaf travel is described. Its performance is evaluated through correlation and receiver operating characteristic (ROC) analyses with patient-specific gamma passing rates using 2 3-dimensional diode arrays. For comparison, the following metrics are evaluated using the same correlation analyses: average field width, average leaf travel, modulation complexity score, and leaf travel modulation complexity score. Spearman's rank correlation analysis is performed to examine any relationships between the complexity metrics and the patient-specific gamma passing rates. ROC curves are used to assess the performance of the plan metrics using a gamma passing rate of 3%/3 mm criterion with a 95% tolerance level. In both the diode arrays, the gamma passing rates (3%/3 mm and 2%/2 mm) for patient-specific dosimetric verification of VMAT plans are moderately or weakly correlated to all the complexity metrics. NMCSv demonstrates the highest correlation with the passing rates (r = 0.652, p < 0.001 for Delta4 and r = 0.499, p < 0.001 for ArcCheck) and the highest area under the curve value (0.809, p < 0.01 for Delta4 and 0.734, p < 0.01 for ArcCheck). While using the Delta4 system, NMCSv exhibits an excellent classification performance with area under the curves of 0.926 (sensitivity: 0.913; specificity: 0.860; p < 0.01) and 0.918 (sensitivity: 0.943; specificity: 0.720; p < 0.01) for rectal and cervical cancer plans, respectively. NMCSv as a novel potential clinical plan complexity metric is moderately correlated with the gamma passing rate. It demonstrates the best performance with respect to distinguishing the dosimetric accuracy of VMAT plans among the evaluated metrics. The classification performance of complexity metrics can be affected by various dosimetry verification devices and treatment sites.

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

Limiting treatment plan complexity by applying a novel commercial tool

PURPOSE

A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality.

METHODS

Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination.

RESULTS

The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths.

CONCLUSIONS

This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality.

Evaluation of three VMAT-TMI planning methods to find an appropriate balance between plan complexity and the resulting dose distribution

PURPOSE

Evaluation of different planning methods of treatment plan preparation for volumetric modulated arc therapy during total marrow irradiation (VMAT-TMI).

METHOD

Three different planning methods were evaluated to establish the most appropriate VMAT-TMI technique, based on organ at risk (OAR) dose reduction, conformity and plan simplicity. The methods were: (M1) the sub-plan method, (M2) use of eight arcs optimised simultaneously and (M3) M2 with monitor unit reduction. Friedman ANOVA comparison, with Nemenyi's procedures, was used in the statistical analysis of the results.

RESULTS

The dosimetric results obtained for the planning target volume and for most OARs do not differ statistically between methods. The M3 method was characterized by the lowest numbers of monitor units (3259 MU vs. 4450 MU for M1 and 4216 MU for M2) and, in general, the lowest complexity. The variability of the monitor units from control points was almost half for M3 than M1 and M2 (i.e. 0.33 MU vs. 0.61 MU for M1 and 0.58 for M2). Analysing the relationship between the dose distributions obtained for the plans and their complexity, the best result was observed for the M3 method.

CONCLUSION

The use of eight simultaneously optimised arcs with MU reduction allows to obtain VMAT-TMI plans that are characterized by the lowest complexity, with dose distributions comparable to the plans generated by other methods.

Plan evaluation indices: A journey of evolution

AIM

A systemic review and analysis of evolution journey of indices, such as conformity index (CI), homogeneity index (HI) and gradient index (GI), described in the literature.

BACKGROUND

Modern radiotherapy techniques like VMAT, SRS and SBRT produce highly conformal plans and provide better critical structure and normal tissue sparing. These treatment techniques can generate a number of competitive plans for the same patients with different dose distributions. Therefore, indices like CI, HI and GI serve as complementary tools in addition to visual slice by slice isodose verification while plan evaluation. Reliability and accuracy of these indices have been tested in the past and found shortcomings and benefits when compared to one another.

MATERIAL AND METHODS

Potentially relevant studies published after 1993 were identified through a pubmed and web of science search using words "conformity index", "Homogeneity index", "Gradient index"," Stereotactic radiosurgery"," stereotactic Body radiotherapy" "complexity metrics" and "plan evaluation index". Combinations of words "plan evaluation index conformity index" were also searched as were bibliographies of downloaded papers.

RESULTS AND CONCLUSIONS

Mathematical definitions of plan evaluation indices modified with time. CI definitions presented by various authors tested at their own and could not be generalized. Those mathematical definitions of CI which take into account OAR sparing grant more confidence in plan evaluation. Gradient index emerged as a significant plan evaluation index in addition to CI whereas homogeneity index losing its credibility. Biological index base plan evaluation is becoming popular and may replace or alter the role of dosimetrical indices.

Impact of different optimization strategies on the compatibility between planned and delivered doses during radiation therapy of cervical cancer

PURPOSE

To analyse the impact of different optimization strategies on the compatibility between planned and delivered doses during radiotherapy of cervical cancer.

MATERIAL/METHODS

Four treatment plans differing in optimisation strategies were prepared for ten cervical cancer cases. These were: volumetric modulated arc therapy with (_OPT) and without optimization of the doses in the bone marrow and for two sets of margins applied to the clinical target volume that arose from image guidance based on the bones (IG(B)) and soft tissues (IG(ST)). The plans were subjected to dosimetric verification by using the ArcCHECK system and 3DVH software. The planned dose distributions were compared with the corresponding measured dose distributions in the light of complexity of the plans and its deliverability.

RESULTS

The clinically significant impact of the plans complexity on their deliverability is visible only for the gamma passing rates analysis performed in a local mode and directly in the organs. While more general analyses show statistically significant differences, the clinical relevance of them has not been confirmed. The analysis showed that IG(ST)_OPT and IG(B)_OPT significantly differ from IG(ST) and IG(B). The clinical acceptance of IG(ST)_OPT obtained for hard combinations of gamma acceptance criteria (2%/2 mm) confirm its satisfactory deliverability. In turn, for IG(B)_OPT in the case of the rectum, the combination of 2%/2 mm did not meet the criteria of acceptance.

CONCLUSION

Despite the complexity of the IG(ST)_OPT, the results of analysis confirm the acceptance of its deliverability when 2%/2 mm gamma acceptance criteria are used during the analysis.

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique

PURPOSE

Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided by an established in-house KBP system. The purpose of this work was to assess dosimetric, mechanical, and delivery properties of plans designed with a common KBP method for prostate cases treated via volumetric modulated arc therapy (VMAT).

METHODS

Thirty-one prostate patients previously treated with VMAT were replanned with an in-house KBP method based on the overlap volume histogram. VMAT plan complexities of the KBP plans and the reference clinical plans were quantified via monitor units, modulation complexity scores, the edge metric, and average leaf motion per degree of gantry rotation. Each set of plans was delivered to the same diode array and agreement between computed and measured dose distributions was evaluated using the gamma index. Varying percent dose-difference (1-3%) and distance-to-agreement (1 mm to 3 mm) thresholds were assessed for gamma analyses.

RESULTS

Knowledge-based planning (KBP) plans achieved average reductions of 6.4 Gy (P < 0.001) and 8.2 Gy (P < 0.001) in mean bladder and rectum dose compared to reference plans, while maintaining clinically acceptable target dose. However, KBP plans were significantly more complex than reference plans in each evaluated metric (P < 0.001). KBP plans also showed significant reductions (P < 0.05) in gamma passing rates at each evaluated criterion compared to reference plans.

CONCLUSIONS

While KBP plans had significantly reduced bladder and rectum dose, they were significantly more complex and had significantly worse quality assurance outcomes than reference plans. These results suggest caution should be taken when implementing an in-house KBP technique.