Treatment Plan Complexity Research Articles

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

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. 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. 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. 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. 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.

Impact of delivery variations on 3D dose distributions for volumetric modulated arc therapy plans of various complexity.

Delivery variations during radiotherapy can cause discrepancies between planned and delivered dose distribution. These variations could arise from random and systematic offsets in certain machine parameters or systematic offsets related to the calibration process of the treatment unit. The aim of this study was to present a novel simulation-based methodology to evaluate realistic delivery variations in three dimensions (3D). Additionally, we investigated the dosimetric impact of delivery variations for volumetric modulated arc therapy (VMAT) plans for different treatment sites and complexities. Twelve VMAT plans for different treatment sites (prostate-, head & neck-, lung-, and gynecological cancer) were selected. The clinical plan used for the treatment of each patient was reoptimized to create one plan with reduced complexity (i.e., simple plan) and one of higher complexity (i.e., complex plan). This resulted in a total of 36 plans. Delivery variations were simulated by randomly introducing offsets in multi-leaf collimator position, jaw position, gantry angle and collimator angle simultaneously. Twenty simulations were carried out for each of the 36 plans, yielding 720 simulated deliveries. To explore the impact of individual offsets, additional simulations were conducted for each type of offset separately. A 3D dose calculation was performed for each simulation using the same calculation engine as for the clinical plan. Two standard deviations (2SD) of dose were determined for every voxel for 3D-spatial evaluations. The dose variation in certain DVH metrics, that is, D2% and D98% for the clinical target volume and five different DVH metrics for selected organs at risk, was calculated for the twenty simulated deliveries of each plan. For comparison, the effect of delivery variations was assessed by conducting measurements with the Delta4 phantom. The volume of voxels with 2SD above 1% of the prescribed dose was consistently larger for the complex plans in comparison to their corresponding simple and clinical plans. 2SDs larger than 1% were in many cases, found to accumulate outside the planning target volume. For complex plans, regions with 2SDs larger than 1% were detected also inside the high dose region, exhibiting, on average, a size six times larger volume, than those observed in simple plans. Similar results were found for all treatment sites. Variation in the selected DVH metrics for the simulated deliveries was generally largest for the complex plans with few exceptions. When comparing the 2SD distribution of the measurements with the 2SD distribution from the simulations, the spatial information showed deviations outside the PTV in both simulations and measurements. However, the measured values were, on average, 35% higher for the prostate plans and 10% higher for the head & neck plans compared to the simulated values. The presented methodology effectively quantified and localized dose deviations due to delivery offsets. The 3D analysis provided information that was undetectable using the analysis based on DVH metrics. Dosimetric uncertainties due to delivery variations were prominent at the edge of the high-dose region irrespective of treatment site and plan complexity. Dosimetric uncertainties inside the high-dose region was more profound for plans of higher complexity.

Treatment plan complexity quantification for predicting gamma passing rates in patient-specific quality assurance for stereotactic volumetric modulated arc therapy.

To investigate the beam complexity of stereotactic Volumetric Modulated Arc Therapy (VMAT) plans quantitively and predict gamma passing rates (GPRs) using machine learning. The entire dataset is exclusively made of stereotactic VMAT plans (301 plans with 594 beams) from Varian Edge LINAC. The GPRs were analyzed using Varian's portal dosimetry with 2%/2mm criteria. A total of 27 metrics were calculated to investigate the correlation between metrics and GPRs. Random forest and gradient boosting models were developed and trained to predict the GPRs based on the extracted complexity features. The threshold values of complexity metric were obtained to predict a given beam to pass or fail from ROC curve analysis. The three moderately significant values of Spearman's rank correlation to GPRs were 0.508 (p < 0.001), 0.445 (p < 0.001), and -0.416 (p < 0.001) for proposed metric LAAM, the ratio of the average aperture area over jaw area (AAJA) and index of modulation, respectively. The random forest method achieved 98.74% prediction accuracy with mean absolute error of 1.23% using five-fold cross-validation, and 98.71% with 1.25% for gradient boosting regressor method, respectively. LAAM, leaf travelling distance (LT), AAJA, LT modulation complexity score (LTMCS) and index of modulation, were the top five most important complexity features. The LAAM metric showed the best performance with AUC value of 0.801, and threshold value of 0.365. The calculated metrics were effective in quantifying the complexity of stereotactic VMAT plans. We have demonstrated that the GPRs could be accurately predicted using machine learning methods based on extracted complexity metrics. The quantification of complexity and machine learning methods have the potential to improve stereotactic treatment planning and identify the failure of QA results promptly.

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

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. 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. 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. 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. 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.

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

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 unresolvedchallenge. 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 clinicalapplication. 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 controlprocess. 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 highlycomplex. 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 plancomplexity.

Concordance in the healthcare context: A concept analysis.

The concept of concordance and its usage are indicative of patient-centered healthcare, which encourages an equitable collaboration between patients and clinicians. However, there is a lack of clarity in understanding concordance, and an analysis of this concept is warranted. This paper seeks to explore the essence of the concordance concept in the healthcare context. Walker and Avant's eight-step method of concept analysis was applied. A literature search was conducted using concordance AND concord as keywords on CINAHL, MEDLINE, PubMed, ProQuest, Cochrane database, and ScienceDirect databases, published between 2006 and 2022. The attributes of concordance include communication process, therapeutic partnership, and agreement on treatment regimens. Antecedents of concordance comprise provider-patient relationship, patients' beliefs, healthcare providers' characteristics, and complexity of treatment plans. Consequences are improved adherence and compliance, improved clinical outcomes, and better quality of life. This paper offers clarification of concordance by presenting its antecedents, attributes, and consequences. The findings serve as a basis for developing assessments and nursing interventions to promote patient concordance. Nurses can use the findings to encourage concordance by establishing a trusty relationship during health encounters that respects patients' beliefs and effectively communicating to improve patients' understanding in dealing with complex treatment plans.

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

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. 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. 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 20Gy. 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 12Gy (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). PTV D98% showed high robustness on average to most errors with the exception of a bank shift of 1.0mm 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. 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.

Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer

This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman’s rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = − 0.608, p < 0.01; TrueBeam Rs = − 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment.

The effects of flattening filter-free beams and aperture shape controller on the complexity of conventional large-field treatment plans.

The purpose of this study was to investigate the impact of using flattening filter-free (FFF) beams and the aperture shape controller (ASC) on the complexity of conventional large-field treatment plans. A total of 24 head and neck (H&N) and 24 prostate with pelvic nodes treatment plans were used in this study. Each plan was reoptimized using the original clinical objectives with both flattened and FFF beams, as well as six different ASC settings. The dosimetric qualities of each plan cohort were evaluated using commonly used dose-volume histogram values, and plan complexities were assessed through metrics including monitor unit (MU)/Dose, change in gantry speed, multileaf collimator (MLC) speed, the edge area ratio metric (EM), and the equivalent square length. No significant differences in dosimetric qualities were found between plans with flattened and FFF beams. The ASC settings did not have significant effects on dosimetric qualities in the H&N plan cohort, but the "very high" ASC setting resulted in poorer dosimetric results for the prostate plans. Plans with FFF beams had significantly higher MU/Dose compared to plans with flattened beams. The use of flattening filter (FF) had significant effects on the change in gantry speed, with flattened beams producing plans that required higher change in gantry speed. However, the FF did not have significant effects on MLC speed, EM, or equivalent square length. In contrast, ASC settings had significant effects on these three metrics; increasing the ASC level resulted in plans with decreasing MLC speed, lower edge area ratio, and higher equivalent square length. This study demonstrated that using FFF beams with various ASC settings, except for the "very high" level, can produce plans with reduced complexities without compromising dosimetric qualities in conventional large-field treatment plans.

Predicting the complexity of head-and-neck volumetric-modulated arc therapy planning using a radiation therapy planning quality assurance software.

The more complex the treatment plan, the higher the possibility of errors in dose verification. Recently, a treatment planning quality assurance (QA) software (PlanIQ) with a function to objectively evaluate the quality of volumetric-modulated arc therapy (VMAT) treatment plans by scoring and calculating the ideal dose-volume histogram has been marketed. This study aimed to assess the association between the scores of ideal treatment plans identified using PlanIQ and the results of dose verification and to investigate whether the results of dose verification can be predicted based on the complexity of treatment plans. Dose verification was performed using an ionization chamber dosimeter, a radiochromic film, and a three-dimensional dose verification system, Delta4 PT. Correlations between the ideal treatment plan scores obtained by PlanIQ and the results of the absolute dose verification and dose distribution verification were obtained, and it was examined whether dose verifications could be predicted from the complexity of the treatment plans. Even when the score from the ideal treatment plan was high, the results of absolute dose verification and dose distribution verification were sometimes poor. However, even when the score from the ideal treatment plan was low, the absolute volume verification and dose distribution verification sometimes yielded good results. Treatment plan complexity can be determined in advance from the ideal treatment plan score calculated by PlanIQ. However, it is difficult to predict the results of dose verification using an ideal treatment plan.

Caregiver burden, treatment complexity, and the veterinarian-client relationship in owners of dog with skin disease.

BackgroundIncreasing complexity of treatment plans is associated with higher levels of caregiver burden in owners of dogs with skin disease. It is possible that elevated caregiver burden resulting from treatment complexity could, in turn, affect the veterinarian–client relationship.Hypotheses/ObjectivesWe expected that treatment complexity, caregiver burden, and the client’s perception of the veterinarian–client relationship would be related to each other. We also expected an indirect effect of caregiver burden on the cross‐sectional association between treatment complexity and the veterinarian–client relationship, and that this effect would be robust to adjustment for the dog’s skin disease course and severity.ParticipantsParticipants were 349 owners of dogs with skin disease recruited through online consumer panels.Materials and methodsCross‐sectional online assessments were completed for caregiver burden, treatment plan complexity, veterinarian–client relationship, and skin disease course and severity. Demographic information also was collected.ResultsThe indirect effect of caregiver burden on the relationship between treatment complexity and veterinarian–client relationship was statistically significant, accounting for 42.76% of the variance in the model. After controlling for disease severity and course, that effect remained statistically significant, accounting for 37.76% of the variance.Conclusions and clinical importanceFindings support the notion that greater treatment complexity is related to the owner’s perception of the veterinarian–client relationship via caregiver burden. Efforts to reduce caregiver burden by using the simplest effective treatment may benefit the veterinarian–client relationship.

Clinical experiment on quality control comparison of complex treatment plans of the VMAT technique using a diode-based cylindrical phantom (ArcCHECK) and an amorphous silicon-based planar detector (A-Si1000)

BackgroundWith increasing application of Volumetric Modulated Arc Therapy (VMAT) and the complexity of treatment plans, necessity of patient-specific quality assurance procedures is raised. Therefore, this study aimed to perform VMAT plan control using the ArcCHECK® (Sun Nuclear) and the EPID (Electronic Portal Imaging Device) A-Si1000 and make a comparison. MethodsThe study was carried out at AL KAWTAR Oncology Center (Fez-Meknes/Morocco) with the TrueBeamTM accelerator and EclipseTM Treatment Planning System (TPS). GeoGebra 6.0 and Origin 2019 software were applied to statistical analysis and to reconstruct the dose distribution maps for ArcCHECK® Phantom, respectively. Gamma index test with the 3%/3 and 2%/2 mm criteria applied to compare two pretreatment quality control instrumentation. ResultsAlthough the EPID A-Si1000 and the ArcCHECK® phantom both were capable of performing VMAT plan control, results of gamma index analysis revealed some differences in the quality of the reviewed plans with the two devices. Conclusionbased on the results, a single device to implement patient QA for complex VMAT plans may not be reliable. Therefore, to ensure patient protection against machine or human errors, applying the EPID A-Si1000 and the ArcCHECK® phantom both are proposed to evaluate the quality of complex VMAT plans for different cancer types.

Predicting personalised optimal arc parameter using knowledge-based planning model for inoperable locally advanced lung cancer patients to reduce organ at risk doses

Objectives. Volumetric modulated arc therapy (VMAT) allows for reduction of organs at risk (OAR) volumes receiving higher doses, but increases OAR volumes receiving lower radiation doses and can subsequently increasing associated toxicity. Therefore, reduction of this low-dose-bath is crucial. This study investigates personalizing the optimization of VMAT arc parameters (gantry start and stop angles) to decrease OAR doses. Materials and Methods. Twenty previously treated locally advanced non-small cell lung cancer (NSCLC) patients treated with half-arcs were randomly selected from our database. These plans were re-optimized with seven different arcs parameters; optimization objectives were kept constant for all plans. All resulting plans were reviewed by two clinicians and the optimal plan (lowest OAR doses and adequate target coverage) was selected. Furthermore, knowledge-based planning (KBP) model was developed using these plans as ‘training data’ to predict optimal arc parameters for individual patients based on their anatomy. Treatment plan complexity scores and deliverability measurements were performed for both optimal and original clinical plans. Results. The results show that different arc geometries resulted in different dose distributions to the OAR but target coverage was mostly similar. Different arc geometries were required for different patients to minimize OAR doses. Comparison of the personalized against the standard (2 half-arcs) plans showed a significant reduction in lung V5 (lung volume receiving 5 Gy), mean lung dose and mean heart doses. Reduction in lung V20 and heart V30 were statistically insignificant. Plan complexity and deliverability measurements show the test plans can be delivered as planned. Conclusions. Our study demonstrated that personalizing arc parameters based on an individual patient’s anatomy significantly reduces both lung and heart doses. Dose reduction is expected to reduce toxicity and improve the quality of life for these patients.

Treatment complexity and caregiver burden are linked in owners of dogs with allergic/atopic dermatitis.

BackgroundSkin disease severity and treatment plan complexity are both related to owner experience of caregiver burden; however, to date they have not been examined simultaneously in owners of dogs with atopic or other chronic allergic dermatitis.Hypotheses/ObjectivesWe expected to replicate and extend previous work by showing that caregiver burden is linked to treatment complexity and disease severity in this population. Moreover, we hypothesized that a relationship between burden and treatment complexity would be present in both subjective (owner report of experience) and objective (number of weekly treatments) measures, and that these relationships would exist independent of skin disease severity.ParticipantsEighty‐six owners of dogs with atopic or other chronic allergic dermatitis recruited through social media dog allergy groups.Methods and materialsCross‐sectional online assessments were completed for caregiver burden, treatment plan complexity and skin disease severity.ResultsCaregiver burden was correlated with skin disease severity (r = 0.40) and both subjective (r = 0.37) and objective (r =0.30) measures of treatment complexity (P < 0.01 for all). Relationships between caregiver burden and treatment complexity remained significant after accounting for skin disease severity.Conclusions and clinical importanceGreater treatment plan complexity is associated with higher caregiver burden in owners of dogs with atopic or other chronic allergic dermatitis. The independence of this relationship highlights the importance of simplicity in effective treatment planning.

Incorporating historical sub-optimal deep neural networks for dose prediction in radiotherapy.

As the main treatment for cancer patients, radiotherapy has achieved enormous advancement over recent decades. However, these achievements have come at the cost of increased treatment plan complexity, necessitating high levels of expertise experience and effort. The accurate prediction of dose distribution would alleviate the above issues. Deep convolutional neural networks are known to be effective models for such prediction tasks. Most studies on dose prediction have attempted to modify the network architecture to accommodate the requirement of different diseases. In this paper, we focus on the input and output of dose prediction model, rather than the network architecture. Regarding the input, the non-modulated dose distribution, which is the initial quantity in the inverse optimization of the treatment plan, is used to provide auxiliary information for the prediction task. Regarding the output, a historical sub-optimal ensemble (HSE) method is proposed, which leverages the sub-optimal models during the training phase to improve the prediction results. The proposed HSE is a general method that does not require any modification of the learning algorithm and does not incur additional computational cost during the training phase. Multiple experiments, including the dose prediction, segmentation, and classification tasks, demonstrate the effectiveness of the strategies applied to the input and output parts.

Monte Carlo calculations of radiotherapy dose in "homogeneous" anatomy.

Given the substantial literature on the use of Monte Carlo (MC) simulations to verify treatment planning system (TPS) calculations of radiotherapy dose in heterogeneous regions, such as head and neck and lung, this study investigated the potential value of running MC simulations of radiotherapy treatments of nominally homogeneous pelvic anatomy. A pre-existing in-house MC job submission and analysis system, built around BEAMnrc and DOSXYZnrc, was used to evaluate the dosimetric accuracy of a sample of 12 pelvic volumetric arc therapy (VMAT) treatments, planned using the Varian Eclipse TPS, where dose was calculated with both the Analytical Anisotropic Algorithm (AAA) and the Acuros (AXB) algorithm. In-house TADA (Treatment And Dose Assessor) software was used to evaluate treatment plan complexity, in terms of the small aperture score (SAS), modulation index (MI) and a novel exposed leaf score (ELS/ELA). Results showed that the TPS generally achieved closer agreement with the MC dose distribution when treatments were planned for smaller (single-organ) targets rather than larger targets that included nodes or metastases. Analysis of these MC results with reference to the complexity metrics indicated that while AXB was useful for reducing dosimetric uncertainties associated with density heterogeneity, the residual TPS dose calculation uncertainties resulted from treatment plan complexity and TPS model simplicity. The results of this study demonstrate the value of using MC methods to recalculate and check the dose calculations provided by commercial radiotherapy TPSs, even when the treated anatomy is assumed to be comparatively homogeneous, such as in the pelvic region.

Clinical implementation of a Monte Carlo based independent TPS dose checking system

The increase in complexity of treatment plans over time through modalities such as intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) has often not been met with an increase in capability of the secondary dose calculation checking systems typically used to verify the treatment planning system. Monte Carlo (MC) codes such as EGSnrc have become easily available and are capable of performing calculations of highly complex radiotherapy treatments. This educational note demonstrates a method for implementing and using a fully automated system for performing and analysing full MC calculations of conformal, IMRT and VMAT radiotherapy plans. Example calculations were based on BEAMnrc/DOSXYZnrc and are performed automatically after either uploading exported plan DICOM data through a Python-based web interface, or exporting DICOM data to a monitored network location. This note demonstrates how completed MC calculations can then be analysed using an automatically generated dose point comparison report, or easily re-imported back into the treatment planning system. Agreement between the TPS and MC calculation was an improvement on agreement between RadCalc and the TPS, with differences ranging from 1.2 to 5.5% between RadCalc and the treatment planning system (TPS), and 0.1-1.7% between MC and TPS. Comparison of the dose-volume histogram (DVH) parameters [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for the example VMAT plans showed agreement for the mean planning target volume dose within [Formula: see text], [Formula: see text] and [Formula: see text] generally within [Formula: see text] with the exception of a brain case, and [Formula: see text] within [Formula: see text]. Overall, this note provides a demonstration of a system that has been integrated well into existing clinical workflow, and has been shown to be a valuable additional tool in the secondary checking of treatment plan calculations.

Limiting treatment plan complexity by applying a novel commercial tool.

PurposeA 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.MethodsTen 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.ResultsThe 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.ConclusionsThis 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.

On the complexity of helical tomotherapy treatment plans

PurposeMultiple metrics are proposed to characterize and compare the complexity of helical tomotherapy (HT) plans created for different treatment sites.MethodsA cohort composed of 208 HT plans from head and neck (105), prostate (51) and brain (52) tumor sites was considered. For each plan, 14 complexity metrics were calculated. Those metrics evaluate the percentage of leaves with small opening times or approaching the projection duration, the percentage of closed leaves, the amount of tongue‐and‐groove effect, and the overall modulation of the planned sinogram. To enable data visualization, an approach based on principal component analysis was followed to reduce the dataset dimensionality. This allowed the calculation of a global plan complexity score. The correlation between plan complexity and pretreatment verification results using the Spearman’s rank correlation coefficients was investigated.ResultsAccording to the global score, the most complex plans were the head and neck tumor cases, followed by the prostate and brain lesions irradiated with stereotactic technique. For almost all individual metrics, head and neck plans confirmed to be the plans with the highest complexity. Nevertheless, prostate cases had the highest percentage of leaves with an opening time approaching the projection duration, whereas the stereotactic brain plans had the highest percentage of closed leaves per projection. Significant correlations between some of the metrics and the pretreatment verification results were identified for the stereotactic brain group.ConclusionsThe proposed metrics and the global score demonstrated to be useful to characterize and quantify the complexity of HT plans of different treatment sites. The reported differences inter‐ and intra‐group may be valuable to guide the planning process aiming at reducing uncertainties and harmonize planning strategies.

Characterization of robust optimization for VMAT plan for liver cancer.

We investigated the feasibility of robust optimization for volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) for liver cancer in comparison with planning target volume (PTV)-based optimized plans. Treatment plan quality, robustness, complexity, and accuracy of dose delivery were assessed. Ten liver cancer patients were selected for this study. PTV-based optimized plans with an 8-mm PTV margin and robust optimized plans with an 8-mm setup uncertainty were generated. Plan perturbed doses were evaluated using a setup error of 8mm in all directions from the isocenter. The dosimetric comparison parameters were clinical target volume (CTV) doses (D98%, D50%, and D2%), liver doses, and monitor unit (MU). Plan complexity was evaluated using the modulation complexity score for VMAT (MCSv). There was no significant difference between the two optimizations with respect to CTV doses and MUs. Robust optimized plans had a higher liver dose than did PTV-based optimized plans. Plan perturbed dose evaluations showed that doses to the CTV for the robust optimized plans had small variations. Robust optimized plans were less complex than PTV-based optimized plans. Robust optimized plans had statistically significant fewer leaf position errors than did PTV-based optimized plans. Comparison of treatment plan quality, robustness, and plan complexity of both optimizations showed that robust optimization could be feasibile for VMAT of liver cancer.