Complexity in Radiation Therapy: It's Complicated

Advanced prediction of multi-leaf collimator leaf position using artificial neural network

BACKGROUND

Multi-leaf collimators (MLCs) are crucial for modern radiotherapy as they ensure precise target irradiation through accurate leaf positioning. Accurate prediction of MLC leaf positions is vital for the effectiveness and safety of treatments.

PURPOSE

This study aims to establish three neural network models for predicting the delivered positions of MLCs in radiotherapy.

METHODS

Fifty plans with sliding window dynamic intensity-modulated radiation therapy delivery were selected from an Elekta linear accelerator, which features a 160-leaf MLC system. The dose fraction, gantry angle, collimator angle, X1 and X2 jaw positions, Y1 and Y2 carriage positions, planned leaf positions, adjacent leaf positions, leaf gap, leaf velocity, and leaf acceleration were extracted from the planning data in the machine's log files and used as model inputs, with the delivered leaf positional serving as the target response. This establishes the input-output relationship for the neural network, and the predicted MLC positions are obtained through training. Particle Swarm Optimization Back Propagation Neural Network (PSOBPNN), Back Propagation Neural Network (BPNN), and Radial Basis Function Neural Network (RBFNN) architectures were developed to predict MLC leaf positional deviations during treatment. The training was conducted on 70% of the sample data, with the remaining 30% used for validation and testing. Model performance was assessed using metrics such as mean absolute error (MAE), mean squared error (MSE), regression plots, and error histograms.

RESULTS

The proposed neural network models demonstrated high accuracy in predicting MLC leaf positions. The PSOBPNN model demonstrated superior performance with an MAE of 0.0043 mm and an MSE of 0.00003 mm2. In comparison, the BPNN model achieved an MAE of 0.0241 mm and an MSE of 0.001 mm2, while the RBFNN model exhibited an MAE of 0.0331 mm and an MSE of 0.0019 mm2. The correlation coefficient (R = 0.9999) of models indicates a close match between predicted and delivered leaf positions for all MLC leaves.

CONCLUSION

Three models were evaluated for predicting the delivered MLC positions using data from an Elekta accelerator. The PSOBPNN model exhibited superior performance by achieving markedly lower MAE and MSE values while also demonstrating robust generalizability in predicting positions across various leaf indices, outperforming the conventional BPNN and RBFNN models.

A deep learning-based peer review method for radiotherapy planning

BACKGROUND

Quality control (QC) in radiotherapy planning is crucial for ensuring treatment efficacy and patient safety. Traditionally, QC relies on standard indicators and subjective assessments, which may lead to inconsistencies.

PURPOSE

This study aims to develop a novel peer review method for personalized QC in radiotherapy planning, which is based on patient anatomical information, and utilizes deep learning dose prediction and a statistical model.

METHODS

A UNet model was trained on 139 nasopharyngeal carcinoma patients to predict 3D dose distribution, with plans divided into 95 for training, 20 for validation, and 24 for testing. For the clinical evaluation (24 items in total) of organs at risk (OAR), the QC interval (qualified, acceptable, or unqualified) for these items was set according to the model accuracy. Peer review was performed on another 29 clinical treatment plans, the items identified by the model as requiring optimization and improvement were optimized, and the effectiveness of the peer review method was tested.

RESULTS

The predicted mean voxel-based dose difference was 0.29 ± 0.13 Gy. For most evaluation items, the model prediction results were comparable to the planned results. Peer review results suggested that 66% of the plans were acceptable or unqualified. After optimization, 100% of the acceptable plans and 47% of the unqualified plans became qualified, and 20% of the unqualified plans became acceptable.

CONCLUSIONS

A deep learning dose prediction model based on patient information can be used to develop personalized QC in radiotherapy planning and can help improve the quality of radiotherapy plans.

Fluence smoothing evaluation for whole-breast automatically generated treatment plans

PURPOSE

This study aimed to identify the fluence smoothing threshold that preserves the dosimetric quality of planning for breast cancer intensity-modulated radiation therapy (IMRT).

MATERIAL AND METHODS

We conducted automated treatment planning for 60 breast cancer patients using the Eclipse Scripting Application Programming Interface. The plans included four-field IMRT, emphasizing smoothing weight combinations while maintaining a 4:3 aspect ratio between the X and Y directions. Four weight sets (40 × 30, 100 × 75, 150 × 115.2, and 200 × 150) were tested, resulting in four plans per patient. A total dose of 40.05 Gy over 15 fractions was prescribed. Optimization weigths were dynamically adjusted based on dosimetric evaluations, with the maximum coverage priority set at 200. Statistical analyses were used to assess the dosimetric data.

RESULTS

The median planning target volume (PTV) coverage varied across smoothing levels, with default smoothing (40 × 30) providing superior median PTV coverage. Lung constraints showed significant differences mainly at higher smoothing levels. Heart constraints exhibited less variation between smoothing levels, with significant differences primarily in the maximum and mean doses for right-sided patients and between default and higher smoothing levels for left-sided patients. No significant differences were observed in contralateral breast constraints among all smoothing levels, except at the maximum level for right-sided patients. Monitor units decreased with increasing smoothing weight, showing significant differences between default and other settings. For right-sided patients, the median number of monitor units varied from 1346 (40 × 30) to 754 (200 × 150), and for left-sided patients, from 1333 (40 × 30) to 804 (200 × 150). Chi-square tests revealed differences in dose constraint adherence between default and maximum smoothing levels, particularly in target coverage.

CONCLUSION

Our findings suggest that using a ratio of smoothing weights to target priorities between 1:1.5 and 1:1.6 leads to a favorable balance between complexity and dosimetric plan quality, with no significant impacts on dose constraint adherence.

Validating knowledge-based volumetric modulated arc therapy plans with a multi-institution model (broad model) using a complete open-loop dataset for prostate cancer

This study examined the characteristics of the broad model (KBPbroad) through a complete open-loop evaluation of volumetric modulated arc therapy (VMAT) plans for prostate cancer in 30 patients at two institutions. KBPbroad, trained using 561 prostate cancer VMAT plans from five institutions with different treatment protocols, was shared with two institutions. The institutions were not involved in the creation of KBPbroad. Plan created with KBPbroad were compared with clinical plans (CPs) and plans created using a single-institution model at each institution (KBPonsite). KBPbroad maintained the target coverage of CPs while meeting dose limits across varied settings at each institution. At institution X, KBPbroad provided 40, 60, and 70 Gy (V40Gy, V60Gy, and V70Gy, respectively) to 30.8% ± 9.9%, 15.3% ± 8.5%, and 9.0% ± 6.4% of the volume at the rectal wall, respectively, which were significantly smaller than those provided by KBPonsite and CPs. At institution Y, compared with CPs, KBPbroad provided significantly greater V50Gy, V70Gy, dose to 2% of the volume (D2%) at the rectum, and D2% at the bladder but significantly lower V50Gy and V70Gy at the bladder, in addition to superior dose homogeneity and conformality at the planning target volume. Our complete open-loop evaluation of VMAT plans for prostate cancer at two institutions demonstrated the clinical effectiveness of KBPbroad at institutions producing plans with insufficient reductions in OAR doses. Thus, the quality of KBPbroad plans is likely greater than that of KBPonsite plans and CPs.

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.

Definition of a framework for volumetric modulated arc therapy plan quality assessment with integration of dose-, complexity-, and robustness metrics

Background and purpose

Conventionally, the quality of radiotherapy treatment plans is assessed through visual inspection of dose distributions and dose-volume histograms. This study developed a framework to evaluate plan quality using dose, complexity, and robustness metrics. Additionally, a method for predicting plan robustness metrics using dose and complexity metrics was introduced for cases where plan robustness evaluation is unavailable or impractical.

Materials and methods

The framework and prediction models were developed and validated using 103-bronchial Volumetric Modulated Arc Therapy (VMAT)-plans. The application of the framework was demonstrated using 25-VMAT-plans. To identify significant metrics for plan evaluation, 122-metrics were analysed and narrowed down using multivariate Spearman correlation. Metric limits were set with Statistical process control (SPC). Robustness metrics were predicted using multivariable or single linear regression models based on dose-and complexity-metrics.

Results

Twenty-five-metrics were selected based on the amount and strength of correlations. R95(dose coverage) and HI95/5(homogeneity index) stood out among the dose-metrics, while the complexity-metrics showed similar correlations. Average scenarios dose at 95 % Clinical Target Volume D95mean(CTV) and Errorbar-based Volume-Histograms (EVH) were notable for robustness metrics. Approximately 99 % of evaluated metrics fell within established SPC limits. The prediction model for D95mean(CTV) showed good performance (adjusted R2 = 0.88, mean squared error (MSE) = 3.84 × 10-6), while the model for EVH demonstrated moderate reliability (adjusted R2 = 0.52, MSE = 0.2). No statistically significant differences were found between the predicted (using dose-and complexity-metrics) and calculated robustness metrics (EVH (p-value = 0.9) and D95mean(CTV) (p-value = 1)).

Conclusions

The developed framework enables early detection of sub-optimal, complex and non-robust treatment plans. The predictive model can be used when robustness evaluations are impractical.

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.

Impact of patient information format on the experience of cancer patients treated with radiotherapy

Introduction

Radiotherapy (RT) stands as one of the main cancer treatments. The impact of RT and cancer treatment can have a physical and psychological impact on patients and their carers. To gain patient's trust, and ensure they feel valued, information should be provided before, during, and after RT. Patient and public involvement (PPI) has been lacking, and increased engagement with PPI groups could improve this. This rapid review aims to analyse the literature, and describe and report patient perception, experience, and satisfaction regarding the information received concerning their course of RT.

Methods

To allow the synthesis of results, a pragmatic decision was made to use a rapid review approach to analyse the literature, providing more timely information to inform future work. This rapid review utilised systematic review methods and was conducted according to a pre-defined protocol including clear inclusion criteria (PROSPERO registration: CRD42023415916).Electronic databases CINAHL, AMED, Pubmed/MEDLINE, EMBASE, and PsycINFO were searched using a comprehensive search for published studies from January 2012 to November 2023. Two independent reviewers applied the eligibility criteria. Evidence from literature was extracted and transcribed into qualitative data and Braun and Clarke's six-step thematic analysis (TA) was employed to determine themes by one reviewer and checked by a second [26]. Due to the heterogeneity of the included literature, the analysis of this review is presented primarily through narrative synthesis.

Results

Sixty eight articles met the inclusion criteria for this review. Emerging themes included; a desire for information based on patient characteristics, information format, patient preparedness, timing e.g. timing of information and changing priorities over time, health care professional (HCP) involvement, barriers to information, and motivators for better information delivery.

Conclusions

Several factors can influence a patient's desire for information, from whom and when they receive it, to what format they would prefer to receive it. There is benefit to be gained in employing PPI and patient advocacy to inform future studies that aim to further understand the themes that emerged from this review. Such studies can therefore inform HCPs in providing patient-specific information and support by utilising multiple teaching strategies available to them.

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.

Online Adaptive MRI-Guided Stereotactic Body Radiotherapy for Pancreatic and Other Intra-Abdominal Cancers

A 1.5T MRI combined with a linear accelerator (Unity®, Elekta; Stockholm, Sweden) is a device that shows promise in MRI-guided stereotactic body radiation treatment (SBRT). Previous studies utilized the manufacturer's pre-set MRI sequences (i.e., T2 Weighted (T2W)), which limited the visualization of pancreatic and intra-abdominal tumors and organs at risk (OAR). Here, a T1 Weighted (T1W) sequence was utilized to improve the visualization of tumors and OAR for online adapted-to-position (ATP) and adapted-to-shape (ATS) during MRI-guided SBRT. Twenty-six patients, 19 with pancreatic and 7 with intra-abdominal cancers, underwent CT and MRI simulations for SBRT planning before being treated with multi-fractionated MRI-guided SBRT. The boundary of tumors and OAR was more clearly seen on T1W image sets, resulting in fast and accurate contouring during online ATP/ATS planning. Plan quality in 26 patients was dependent on OAR proximity to the target tumor and achieved 96 ± 5% and 92 ± 9% in gross tumor volume D90% and planning target volume D90%. We utilized T1W imaging (about 120 s) to shorten imaging time by 67% compared to T2W imaging (about 360 s) and improve tumor visualization, minimizing target/OAR delineation uncertainty and the treatment margin for sparing OAR. The average time-consumption of MRI-guided SBRT for the first 21 patients was 55 ± 15 min for ATP and 79 ± 20 min for ATS.

The NCS code of practice for the quality assurance of treatment planning systems (NCS-35)

A subcommittee of the Netherlands Commission on Radiation Dosimetry (NCS) was initiated in 2018 with the task to update and extend a previous publication (NCS-15) on the quality assurance of treatment planning systems (TPS) (Bruinviset al2005). The field of treatment planning has changed considerably since 2005. Whereas the focus of the previous report was more on the technical aspects of the TPS, the scope of this report is broader with a focus on a department wide implementation of the TPS. New sections about education, automated planning, information technology (IT) and updates are therefore added. Although the scope is photon therapy, large parts of this report will also apply to all other treatment modalities. This paper is a condensed version of these guidelines; the full version of the report in English is freely available from the NCS website (http://radiationdosimetry.org/ncs/publications). The paper starts with the scope of this report in relation to earlier reports on this subject. Next, general aspects of the commissioning process are addressed, like e.g. project management, education, and safety. It then focusses more on technical aspects such as beam commissioning and patient modeling, dose representation, dose calculation and (automated) plan optimisation. The final chapters deal with IT-related subjects and scripting, and the process of updating or upgrading the TPS.

Overcoming Problems Caused by Offset Distance of Multiple Targets in Single-isocenter Volumetric Modulated Arc Therapy Planning for Stereotactic Radiosurgery

Purpose

The purpose of the study is to investigate the impact of large target offset distances on the dose distribution and gamma passing rate (GPR) in single-isocenter multiple-target stereotactic radiosurgery (SIMT SRS) using volumetric modulated arc therapy (VMAT) with a flattening filter-free (FFF) beam from a linear accelerator.

Methods

Two targets with a diameter of 1 cm were offset by "±2, ±4, and ±6 cm from the isocenter in a verification phantom for head SRS (20 Gy/fr). The VMAT plans were created using collimator angles that ensured the two targets did not share a leaf pair from the multi-leaf collimator. To evaluate the low-dose spread intermediate dose spill (R50%), GPRs were measured with a criterion of 3%/2 mm using an electronic portal imaging device and evaluated using monitor unit (MU), modulation complexity score for VMAT (MCSv), and leaf travel (LT) parameters.

Results

For offsets of 2, 4, and 6 cm, the respective parameters were: R50%, 4.75 ± 0.36, 5.13 ± 0.36, and 5.11 ± 0.33; GPR, 95.01%, 93.82%, and 90.67%; MU, 5893 ± 186, 5825 ± 286, and 5810 ± 396; MCSv, 0.24, 0.16, and 0.13; and LT, 189.21 ± 36.04, 327.69 ± 67.01, and 430.39 ± 114.34 mm. There was a spread in the low-dose region from offsets of ≥4 cm and the GPR negatively correlated with LT (r = -0.762). There was minimal correlation between GPR and MU or MCSv.

Conclusions

In SIMT SRS VMAT plans with an FFF beam from a linear accelerator, target offsets of <4 cm from the isocenter can minimize the volume of the low-dose region receiving 10 Gy or more. During treatment planning, it is important to choose gantry, couch, and collimator angles that minimize LT and thereby improve the GPR.

Built-in wavelet-induced smoothness to reduce plan complexity in intensity modulated radiation therapy (IMRT)

Objective.Reducing plan complexity in intensity modulated radiation therapy (IMRT) to ensure dosimetric accuracy and delivery efficiency of the radiation treatment plans. We propose a novel approach by representing the beamlet intensities using an incomplete wavelet basis that explicitly excludes fluctuating intensity maps from the decision space (explicit hard constraint). This technique provides a built-in wavelet-induced smoothness that improves both dosimetric plan quality and delivery efficiency.Approach.The beamlet intensity maps need to be especially smooth in the leaf travel direction (referred to as theX-direction). We treat the intensity map of each beam as a 2D image and represent it using the wavelets corresponding to low-frequency changes in theX-direction (i.e. approximation and horizontal). The absence of wavelets corresponding to high-frequency changes (i.e. vertical and diagonal) induces built-in smoothness. We still utilize a regularization term in the objective function to promote smoothness in theY-direction (perpendicular to theX-direction) and further possible smoothness in theX-direction. This technique has been tested on three patient cases of different disease sites (paraspinal, lung, prostate) and all final evaluations and comparisons have been performed on an FDA-approved commercial treatment planning system (Varian Eclipse^TM).Main results.Wavelet-induced smoothness reduced monitor units by about 10%, 45%, and 14% for paraspinal, lung, and prostate cases, respectively. It also improved organ at risk sparing, especially on the complex paraspinal case where it resulted in about 7%, 13%, and 14% less mean dose to esophagus, lung, and cord, respectively. Moreover, built-in wavelet-induced smoothness desensitizes the results to changing the weight associated to the regularization term, and thereby mitigates the weight fine-tuning difficulty.Significance.Fluence smoothness is often achieved by smoothing the beamlet intensity maps using a proper regularization term in the objective function aiming at disincentivizing fluctuation in the beamlet intensities (implicit soft constraint). This work reports a novel application of wavelets in imposing an explicit smoothness hard constraint in the search space using an incomplete wavelet basis. This idea has been successfully applied to exclude complex and clinically irrelevant radiation plans from the search space. The code and pertained models along with a sample dataset are released on our LowDimRT GitHub (https://github.com/PortPy-Project/LowDimRT).

One Year of Clinic-Wide Cherenkov Imaging for Discovery of Quality Improvement Opportunities in Radiation Therapy

PURPOSE

Cherenkov imaging is clinically available as a radiation therapy treatment verification tool. The aim of this work was to discover the benefits of always-on Cherenkov imaging as a novel incident detection and quality improvement system through review of all imaging at our center.

METHODS AND MATERIALS

Multicamera Cherenkov imaging systems were permanently installed in 3 treatment bunkers, imaging continuously over a year. Images were acquired as part of normal treatment procedures and reviewed for potential treatment delivery anomalies.

RESULTS

In total, 622 unique patients were evaluated for this study. We identified 9 patients with treatment anomalies occurring over their course of treatment, which were only detected with Cherenkov imaging. Categorizing each event indicated issues arising in simulation, planning, pretreatment review, and treatment delivery, and none of the incidents were detected before this review by conventional measures. The incidents identified in this study included dose to unintended areas in planning, dose to unintended areas due to positioning at treatment, and nonideal bolus placement during setup.

CONCLUSIONS

Cherenkov imaging was shown to provide a unique method of detecting radiation therapy incidents that would have otherwise gone undetected. Although none of the events detected in this study reached the threshold of reporting, they identified opportunities for practice improvement and demonstrated added value of Cherenkov imaging in quality assurance programs.

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.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

[Impact of Aperture Shape Controller on Knowledge-based VMAT Planning of Prostate Cancer]

PURPOSE

Knowledge-based planning (KBP) has disadvantages of high monitor unit (MU) and complex multi-leaf collimator (MLC) motion. We investigated the optimal aperture shape controller (ASC) level for the KBP to reduce these factors in volumetric modulated arc therapy (VMAT) for prostate cancer.

METHODS

The KBP model was created based on 51 clinical plans (CPs) of patients who underwent the VMAT for prostate cancer. Another 10 CPs were selected randomly, and the KBPs with/without ASC, changed stepwise from very low (KBP-VL) to very high (KBP-VH), were performed with a single auto-optimization. The parameters of dose-volume histograms (DVHs) and MLC performance metrics were evaluated. We obtained the modulation complexity score for VMAT (MCSv), closed leaf score (CLS), small aperture score (SAS), leaf travel (LT), and total MU.

RESULTS

The ASC did not affect the DVH parameters negatively. The following comparisons of MLC performance were obtained (KBP vs. KBP-VL vs. KBP-VH, respectively): 0.25 vs. 0.27 vs. 0.30 (MCSv), 0.19 vs. 0.18 vs. 0.16 (CLS), 0.50 vs. 0.45 vs. 0.40 (SAS_{10 mm}), 0.73 vs. 0.68 vs. 0.63 (SAS_{20 mm}), 768.35 mm vs. 671.50 mm vs. 551.32 mm (LT), and 672.87 vs. 642.36 vs. 607.59 (MU). There were significant differences between KBP and KBP-VH for MCSv and LT (p<0.05).

CONCLUSIONS

The KBP using an ASC set to the very high level could reduce the complexity of MLC motion significantly more without deterioration of the DVH parameters compared with the KBP in VMAT for prostate cancer.

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.

The development and testing of a novel spherical radiotherapy phantom system for the commissioning and patient-specific quality assurance of mono-isocentric multiple mets SRS plans

PURPOSE

To develop a single radiotherapy phantom system capable of performing both patient-specific quality assurance (QA) measurements and commissioning measurements for mono-isocentric LinAc-based stereotactic radiosurgery ("mLSRS") treatment plans.

METHODS

Design A 20-cm diameter spherical phantom was designed which contained within it a film cartridge. The surface of the sphere was machined to display a set of angular markings at both the equator and the meridian representing a spherical coordinate system. A stand was designed which allows for free rotation about any vector passing through the center of the sphere. A program was created using Python 3 to: (a) Compute the measurement setup necessary to intersect exactly one film plane with three user specified dicom points contained within the QA plan; (b) Extract the intersection dose plane from the three dimensional DICOM dose file and; (c) Generate a synthetic computed tomography (CT) in the exact measurement geometry which is subsequently used for phantom positioning during the QA measurement.

TESTING

To assess the functionality of the phantom system dynamic conformal arc mLSRS plans that were generated by a clinically commissioned multiple metastasis treatment planning system (BrainLab Elements version 2.0) using patient-specific data. A total of seven patient plans were created that contained a total of 31 targets {<Volume> = (0.382 ± 0.534) cc: Range [0.051, 2.05] cc, <Off-Axis Distance> = (30 ± 16) mm: Range[0, 55] mm} 27 of which were directly measured with film and analyzed. Each planned isocenter was mapped to the phantom's center and the dose was recomputed. From the phantom dose distribution dicom points of interest were selected in sets of three and input into the provided software. The software computed the plane that intersects with the entered three points and instructed the user on the setup geometry to place the film in the intersecting plane. The software then generated a synthetic CT scan with embedded fiducial markers rotated into the setup orientation. This CT was then used as the setup reference image in ExacTrac image guidance system (tolerance 0.7 mm & 0.5deg). All plans were delivered on a Varian Truebeam linear accelerator with HDMLC, Exactrac and a 6 degree of freedom couch. After delivery of each test plan a 10 × 10 reference field was delivered to a known dose approximately equal to the maximum dose contained within the plan for film calibration. The test film was scanned simultaneously with the 10 × 10 reference film and a film that received zero dose using an Epson 10000XL flatbed scanner after waiting 24 hours. The test film was scaled according to the reference film and analyzed via the gamma analysis (3%, 1 mm, 10%) implemented in Ashland Film QA Pro software.

RESULTS

The spherical phantom system was able to perform validation measurements on a variety of patient-specific plan geometries. The average gamma pass-rate γ(3%, 1 mm,10%) for all measurements was 96.7% (σ = 3.6%).

CONCLUSIONS

A novel spherical radiotherapy phantom system has been designed and tested on clinically relevant test plans.

Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study

Dosimetric evaluation and variation assessment were performed with two knowledge-based planning (KBP) models created at different periods for volumetric-modulated arc therapy (VMAT) for prostate cancer at five institutes. The first and second models (F- and S-models) for KBP were created before April 2017 and April 2019, respectively. The S-model was created using feedback plans from the F-model. Dose evaluation was compared between the two models using the same two computed tomography (CT) datasets and structures. The evaluation metrics were the dose received by 95.0% and 2.0% of the planning target volume (PTV); dose-volume parameters to the rectum and bladder as V90, V80, and V50; and monitor unit (MU). Dosimetric variation was compared by exporting estimated dose-volume histograms for each model to the Model Analytics website and assessing the organ at risk volume. There were no dosimetric differences between the two models for PTV. The V50 of the rectum in the S-model had improved compared to that of the F-model (case I: 49.3 ± 15.6 and 43.5 ± 15.2 [p = 0.08]; case II: 42.5 ± 16.9 and 36.0 ± 15.6 [p = 0.138]). The differences in other parameters were within ± 1.8% between the rectum and the bladder. The MU was slightly higher in the S-model than in the F-model, and dosimetric variation was reduced to the rectum and bladder among all the institutes. The polished S-model for KBP could be used for standardization of the plan quality and sharing of KBP models in VMAT for prostate cancer.

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.

Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies

Radiotherapy treatment planning studies contribute significantly  to advances and improvements in radiation treatment of cancer patients. They are a pivotal step to support and facilitate the introduction of novel techniques into clinical practice, or as a first step before clinical trials can be carried out. There have been numerous examples published in the literature that demonstrated the feasibility of such techniques as IMRT, VMAT, IMPT, or that compared different treatment methods (e.g. non-coplanar vs coplanar treatment), or investigated planning approaches (e.g. automated planning). However, for a planning study to generate trustworthy new knowledge and give confidence in applying its findings, then its design, execution and reporting all need to meet high scientific standards. This paper provides a 'quality framework' of recommendations and guidelines that can contribute to the quality of planning studies and resulting publications. Throughout the text, questions are posed and, if applicable to a specific study and if met, they can be answered positively in the provided 'RATING' score sheet. A normalised weighted-sum score can then be calculated from the answers as a quality indicator. The score sheet can also be used to suggest how the quality might be improved, e.g. by focussing on questions with high weight, or by encouraging consideration of aspects given insufficient attention. Whilst the overall aim of this framework and scoring system is to improve the scientific quality of treatment planning studies and papers, it might also be used by reviewers and journal editors to help to evaluate scientific manuscripts reporting planning studies.

Radioresistant tumours: From identification to targeting

From surviving fraction to tumour curability, definitions of tumour radioresistance may vary depending on the view angle. Yet, mechanisms of radioresistance have been identified and involve tumour-specific oncogenic signalling pathways, tumour metabolism and proliferation, tumour microenvironment/hypoxia, genomics. Correlations between tumour biology (histology) and imaging allow theragnostic approaches that use non-invasive biological imaging using tracer functionalization of tumour pathway biomarkers, imaging of hypoxia, etc. Modelling dose prescription function based on their tumour radio-resistant factor enhancement ratio, related to metabolism, proliferation, hypoxia is an area of investigation. Yet, the delivery of dose painting by numbers/voxel-based radiotherapy with low lineal energy transfer particles may be limited by the degree of modulation complexity needed to achieve the doses needed to counteract radioresistance. Higher lineal energy transfer particles or combinations of different particles, or combinations with drugs and devices such as done with radioenhancing nanoparticles may be promising.

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.