Dose-volume Histograms Of Organs At Risk Research Articles

Dosimetric effect of collimator rotation on intensity modulated radiotherapy and volumetric modulated arc therapy for rectal cancer radiotherapy.

Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) are the main radiotherapy techniques for treating and managing rectal cancer. Collimator rotation is one of the crucial parameters in radiotherapy planning, and its alteration can cause dosimetric variations. This study assessed the effect of collimator rotation on the dosimetric results of various IMRT and VMAT plans for rectal cancer. Computed tomography (CT) images of 20 male patients with rectal cancer were utilized for IMRT and VMAT treatment planning with various collimator angles. Nine different IMRT techniques (5, 7, and 9 coplanar fields with collimator angles of 0°, 45°, and 90°) and six different VMAT techniques (1 and 2 full coplanar arcs with collimator angles of 0°, 45°, and 90°) were planned for each patient. The dosimetric results of various treatment techniques for target tissue (conformity index [CI] and homogeneity index [HI]) and organs at risk (OARs) sparing (parameters obtained from OARs dose-volume histograms [DVH]) as well as radiobiological findings were analyzed and compared. The 7-fields IMRT technique demonstrated lower bladder doses (V40Gy, V45Gy), unaffected by collimator rotation. The 9-fields IMRT and 2-arcs VMAT (excluding the 90-degree collimator) had the lowest V35Gy and V45Gy. A 90-degree collimator rotation in 2-arcs VMAT significantly increased small bowel and bladder V45Gy, femoral head doses, and HI values. Radiobiologically, the 90-degree rotation had adverse effects on small bowel NTCP (normal tissue complication probability). No superiority was found for a 45-degree collimator rotation over 0 or 30 degrees in VMAT techniques. Collimator rotation had minimal impact on dosimetric parameters in IMRT planning but is significant in VMAT techniques. A 90-degree rotation in VMAT, particularly in a 2-full arc technique, adversely affects PTV homogeneity index, bladder dose, and small bowel NTCP. Other evaluated collimator angles did not significantly affect VMAT dosimetrical or radiobiological outcomes.

Challenges of Treating Lung Cancer Patients at MR-Linac Using MR-Based Synthetic CT Calculation in the Adaptive Workflow

Magnetic Resonance guided adaptive radiotherapy (MRgART) allows plan adaptation according to the new patient anatomy; the contours of the structures are adjusted based on the patient's daily MRI, and in the adapt to shape (ATS) workflow, the adapted plan is recalculated on the MRI-based synthetic CT (sCT) generated by bulk density assignment. For sites where there is a high electronic density (ED) gradient between the target and surrounding tissues, such as in lung cancer treatments, the assignment of an average ED may not be able to reproduce an accurate dose calculation. This study evaluates the accuracy of the sCT adapted plan calculation for lung cancer patients and assesses whether the assignment of an optimized ED can reduce dosimetric differences should they arise MATERIALS/METHODS: Nine lung cancer patients treated at Unity 1.5 MR-Linac were selected for this retrospective study. The patient's target and organs at risk (OARs) were contoured, and a CT reference plan containing the ED bulk assignment information i.e., the contours to use in the ATS workflow, and their corresponding average ED was generated. To assess the accuracy of the dosimetry of the adapted plan calculated on the sCT, the plan was recalculated on an ideal sCT (sCTref) obtained from the reference CT by forcing the drawn contours to the average ED as defined on the CT reference plan. Targets and OARs dose-volume histogram (DVH) of the CT and sCTref plans and the dose distributions using gamma (γ) analysis with 2%-2mm criteria were compared. In the case of a discrepancy between the DVHs, the average Eds used for the recalculation on the sCTref were adjusted by several attempts to obtain a sCT optimized (sCTopt) for which a superposition of DVHs on CT and sCTopt was achieved. For 7 of the 9 patients CT and sCTref target DVHs were not comparable, with a mean dosimetric difference of 5.55% (range 2.35%-7.46%) in the target volume receiving the prescription dose (VDpre), while OARs DVH dose differences remained below 1% for the nine patients. The adjustment of the ED of the homolateral lung in the sCTopt, reduced the mean target VDpre dosimetric difference between CT and sCTopt to 0.66% (range 0.17%-1.64%). In addition, the results of the gamma analysis increased from values ranging between 39.5%-70.3% to 88.5%-93.2%, as shown in the Table. Dosimetric errors in the use of the sCT calculation for targets in high ED gradient areas may arise; the use of optimized ED for sCT calculation may be a promising strand to investigate in order to proceed with MR-based sCT plan adaptation for lung cancer treatment.

Robustness analysis of surface-guided DIBH left breast radiotherapy: personalized dosimetric effect of real intrafractional motion within the beam gating thresholds.

The robustness of surface-guided (SG) deep-inspiration breath-hold (DIBH) radiotherapy (RT) for left breast cancer was evaluated by investigating any potential dosimetric effects due to the residual intrafractional motion allowed by the selected beam gating thresholds. The potential reduction of DIBH benefits in terms of organs at risk (OARs) sparing and target coverage was evaluated for conformational (3DCRT) and intensity-modulated radiation therapy (IMRT) techniques. Atotal of 192 fractions of SGRT DIBH left breast 3DCRT treatment for 12patients were analyzed. For each fraction, the average of the real-time displacement between the isocenter on the daily reference surface and on the live surface ("SGRT shift") during beam-on was evaluated and applied to the original plan isocenter. The dose distribution for the treatment beams with the new isocenter point was then calculated and the total plan dose distribution was obtained by summing the estimated perturbed dose for each fraction. Then, for each patient, the original plan and the perturbed one were compared by means of Wilcoxon test for target coverage and OAR dose-volume histogram (DVH) metrics. Aglobal plan quality score was calculated to assess the overall plan robustness against intrafractional motion of both 3DCRT and IMRT techniques. Target coverage and OAR DVH metrics did not show significant variations between the original and the perturbed plan for the IMRT techniques. 3DCRT plans showed significant variations for the left descending coronary artery (LAD) and the humerus only. However, none of the dose metrics exceeded the mandatory dose constraints for any of the analyzed plans. The global plan quality analysis indicated that both 3DCRT and IMRT techniques were affected by the isocenter shifts in the same way and, generally, the residual isocenter shifts more likely tend to worsen the plan in all cases. The DIBH technique proved to be robust against residual intrafractional isocenter shifts allowed by the selected SGRT beam-hold thresholds. Small-volume OARs located near high dose gradients showed significant marginal deteriorations in the perturbed plans with the 3DCRT technique only. Global plan quality was mainly influenced by patient anatomy and treatment beam geometry rather than the technique adopted.

Evaluating Synthetic CT Calculation Accuracy in a 1.5 T MR-Linac Clinical Workflow

<h3>Purpose/Objective(s)</h3> The adapt to shape (ATS) workflow of 1.5 T MR-Linac 1.5 allows plan adaptation according to the new patient anatomy with a plan optimization on daily magnetic resonance imaging (MRI)-based synthetic computed tomography (syCT). This study evaluates the effectiveness of introducing into the clinical workflow a per/patient assessment of the minimum expected dosimetric errors associated with an MRI-based syCT adapted plan. <h3>Materials/Methods</h3> 55 patients treated at 1.5T MR-Linac in different tumors sites were consecutively included in this study. Each patient underwent CT and MR simulation scans for treatment plan preparation. The patient's target and organs at risk (OARs) were contoured in a treatment planning system, and a reference CT plan was generated using eight to twelve individual beam angles; the plan contains electronic density (ED) bulk assignment information i.e., the contours to use in the ATS workflow, and their corresponding average ED, the priority of each contour respect to density assignment in case of contour overlaps. To assess the dosimetric error made by using ATS procedure, for each patient the plan was recalculated on ideal syCT obtained from the reference CT by forcing the drawn contours to the average ED as indicated on the reference CT plan. Targets and OARs dose-volume histogram (DVH) of the CT and syCT plans and the dose distributions using gamma analysis 3%-3mm and 2%2mm criteria, were compared. <h3>Results</h3> For the dose distribution on syCT and CT, the average pass rate of gamma analysis using the 3%/3 mm and 2%/2 mm criteria were higher than 99% and 98%, for 47 patients out of 55; similarly targets and OARs DVH dose differences remained below 1%. For the remaining 8 patients, the source of discrepancy was investigated, and the CT reference plan was reworked to decrease the dosimetric differences on the syCT plan; the results obtained are shown in Table 1. <h3>Conclusion</h3> The accuracy of the plan adaptation on MR-based syCT may be affected by several factors. The inclusion of a per-patient assessment in the clinical workflow allows monitoring and possible optimization before the start of the radiotherapy course.

Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium

PurposeOutside of randomized clinical trials, it is difficult to develop clinically relevant evidence-based recommendations for radiation therapy (RT) practice guidelines owing to lack of comprehensive real-world data. To address this knowledge gap, we formed the Learning from Analysis of Multicenter Big Data Aggregation consortium to cooperatively implement RT data standardization, develop software solutions for data analysis, and recommend clinical practice change based on real-world data analyzed. The first phase of this “Big Data” study aimed at characterizing variability in clinical practice patterns of dosimetric data for organs at risk (OARs) that would undermine subsequent use of large-scale, electronically aggregated data to characterize associations with outcomes. Evidence from this study was used as the basis for practical recommendations to improve data quality. Methods and MaterialsDosimetric details of patients with head and neck cancer treated with radiation therapy between 2014 and 2019 were analyzed. Institutional patterns of practice were characterized, including structure nomenclature, volumes, and frequency of contouring. Dose volume histogram (DVH) distributions were characterized and compared with institutional constraints and literature values. ResultsPlans for 4664 patients treated to a mean plan dose of 64.4 ± 13.2 Gy in 32 ± 4 fractions were aggregated. Before implementation of TG-263 guidelines in each institution, there was variability in OAR nomenclature across institutions and structures. With evidence from this study, we identified a targeted and practical set of recommendations aimed at improving the quality of real-world data. ConclusionsQuantifying similarities and differences among institutions for OAR structures and DVH metrics is the launching point for next steps to investigate potential relationships between DVH parameters and patient outcomes.

Radiotherapy-induced toxicity in prostate cancer patients with hip prostheses

IntroductionAcute and late toxicity was analysed for prostate cancer patients with bilateral hip prostheses, who received fixed field intensity modulated radiotherapy (IMRT). The aims were (1) to establish whether toxicity rates differed from those of a control group with normal hips, (2) to develop a volumetric modulated arc therapy (VMAT) approach for patients with prostheses and (3) to compare doses to bladder and rectum for the control group, prostheses group and VMAT replans for the prostheses group.MethodsGenitourinary (GU) and gastrointestinal (GI) toxicity was scored using Common Terminology Criteria for Adverse Events version 5.0. The incidence of grade 2 or worse (G2+) toxicity was compared using Fisher’s exact test. Dose volume histograms (DVHs) and mean doses to organs at risk (OARs) were compared using signed rank tests.ResultsThere were 17 patients in the prostheses group and 50 in the control group. Acute and late GU toxicity was similar. G2+ late GI toxicity incidence was 31% for the prostheses group and 14% for the control group (p = 0.14). Significant differences (p < 0.05) were seen between the OAR DVHs of the prostheses group who had IMRT and the control group for a range of intermediate doses. The rectum mean dose was significantly different (p < 0.001), but no difference was seen for the bladder mean dose (p = 0.08).ConclusionsNo significant differences were seen in GU and GI toxicity incidence between patients with bilateral hip prostheses and a control group. The DVHs for bladder and rectum were significantly higher for patients with prostheses planned with IMRT. Replanning using a VMAT technique significantly reduced doses to the OARs, whilst maintaining good planning target volume coverage.

Are gamma passing rate and dose-volume histogram QA metrics correlated?

The quality of a measured distribution of dose delivered against its corresponding radiotherapy plan is routinely assessed by gamma index (GI) and dose-volume histogram (DVH) metrics. Any correlation between error detection rates, as based on either of these approaches, while argued, has never been convincingly demonstrated. The dependence of the strength of correlation between the GI passing rate ( ) and DVH quality assurance (QA) metrics on various elements of the therapy plan has not been systematically investigated. A formal analysis of the relation between and DVH metrics has been undertaken, leading to a relationship which may partly approximate with respect to the DVH. This relationship was further validated by studying examples of simulated clinical radiotherapy plans and by studying the correlation between and the derived relationship using a simple two-dimensional representations of the planning target volume (PTV) and organs at risk (OAR), where penumbra regions, distance-to-agreement tolerances and dose delivery errors were systematically varied. It is shown formally that there cannot be any correlation between and other commonly applied DVH-derived QA measures. However, may be partly approximated given the planned and measured DVH. The derived approximation (the " -slope indicator") may be clinically useful in some practical cases of radiotherapy plan QA. In formal terms, there cannot be any correlation between and any common DVH-calculated patient-specific measures, with respect to PTV or OAR. However, as demonstrated analytically and further confirmed in our simulation studies, the approximation derived in this study (the " -slope indicator") may in some cases offer a degree of correlation between and the PTV and OAR DVH QA metrics in measured and planned patient-specific dose distributions-which may be potentially useful in clinical practice.

Assessing the Need for Adjusted Organ-at-Risk Planning Goals for Patients Undergoing Adjuvant Radiation Therapy for Locally Advanced Breast Cancer with Proton Radiation

PurposeLocally advanced breast cancer requires surgical management via lumpectomy or mastectomy with or without systemic therapy followed by chest wall or breast (CW) and comprehensive nodal irradiation (CNI). Radiation (RT) dose constraints for the heart and ipsilateral lung have been developed based on photon RT. Proton therapy (PBT) can deliver significantly lower doses of RT to these organs-at-risk (OARs) and may warrant adjustments to OAR planning goals. Methods and MaterialsThe RT plans of consecutive patients undergoing adjuvant CW-CNI RT with PBT within a single center were reviewed. A inital treatment volume, comprised of CW/intact breast + CNI (CTV_init) structure, including the CW and CNI but excluding any boost plans was analyzed. Frequency distributions were generated based on doses received by the heart, lungs, and esophagus for validated dosimetric parameters. Frequency distributions were generated and then stratified by laterality and compared using the Kruskal-Wallis H test. The 75th, 85th, and 95th percentiles for each dosimetric parameter were calculated, overall and by laterality. The 75th percentile (Q3), was used as a suggested primary goal, and the 95th percentile was used as a suggested secondary goal. ResultsOne hundred and seventy-two plans were analyzed. Forty-nine plans were right-sided, 107 were left-sided, and 16 were bilateral. The overall Q3 of the mean and V25 of the heart were 1.5 Gy and 1.7%, respectively. The mean and V25 to the heart differed significantly by laterality. Pulmonary values were similar to current recommendations. For all lateralites, the median volume of the esophagus receiving 70% prescription dose was ≤1 cm3. ConclusionsWe present the first dosimetric study providing complete OAR dose-volume histograms data for patients undergoing adjuvant pencil-beam scanning-PBT for locally advanced breast cancer, with detailed information on central tendencies, ranges and distributions of data. We have provided suggested planning goals and metrics for the lungs, heart, and esophagus; the latter 2 differing significantly from current Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) constraints and classical photon goals.

Enhanced optimization of volumetric modulated arc therapy plans using Monte Carlo generated beamlets.

A treatment planning system (TPS) produces volumetric modulated arc therapy (VMAT) plans by applying an optimization process to an objective function, followed by an accurate calculation of the final, deliverable dose. However, during the optimization step, a rapid dose calculation algorithm is required, which reduces its accuracy and its representation of the objective function space. Monte Carlo (MC) routines, considered the gold standard in accuracy, are currently too slow for practical comprehensive VMAT optimization. Therefore, we propose a novel approach called enhanced optimization (EO), which employs the TPS VMAT plan as a starting point, and applies small perturbations to nudge the solution closer to a true objective minimum. The perturbations consist of beamlet dose matrices, calculated using MC routines on a distributed-computing framework. DICOM files for clinical VMAT plans files are exported from the TPS and used to generate input files for the EGSnrc MC toolkit. Beamlet doses are calculated using the MC routines, each corresponding to a single multileaf collimator leaf from a single control point traveling 0.5cm in or out of the field. A typical VMAT plan requires 5000 to 10000 beamlets, which may be calculated overnight. This results in a ternary-valued objective function, which may use the same clinical objectives as the original VMAT plan. A simple greedy search algorithm is applied to minimize this function and determine the optimal set of ternary variables. The resulting modified control point parameters are imported into the TPS to calculate the final, deliverable dose, and to compare the EO plan with the original. EO was evaluated retrospectively on seven VMAT plans (two adult brain, one pediatric brain, two head and neck, and two prostate). Additionally, the use of stricter objectives was investigated for two of the cases: the left cochlea planning organ at risk (OAR) volume objective for the pediatric brain case, and the rectum objective for a prostate case. EO produced improved objective scores (by 6% to 60%) and dose-volume histograms (DVH) for the brain plans and the head and neck plans. For each of these plans, the target dose minimum and homogeneity were preserved, while one or more of the OAR DVH's was reduced. Although EO also reduced the objective scores for the prostate plans (by 46% and 79%), their absolute score and DVH improvements were not substantial. The stricter objective on the pediatric brain case resulted in lower dose to the OAR without compromising the target dose. However, the rectum dose in the prostate case could not be improved without reducing dose homogeneity to the planning target volume, suggesting that VMAT prostate cases may already be highly optimized by the TPS. We have developed a novel approach to improving the dose distribution of VMAT plans, which relies on MC calculations to provide small modifications to the control points. This method may be particularly useful for complex treatments in which a certain OAR is of concern and it is difficult for the treatment planner to obtain an acceptable solution with the TPS. Further development will reduce the beamlet computation time and result in more sophisticated EO treatment planning methods.

A deep learning model to predict dose-volume histograms of organs at risk in radiotherapy treatment plans.

To develop a deep learning-based model to predict achievable dose-volume histograms (DVHs) of organs at risk (OARs) for automation of inverse planning. The model was based on a connected residual deconvolution network (CResDevNet) and compared with UNet as a baseline. The DVHs of OARs are dependent on patient anatomical features of the planning target volumes and OARs, and these spatial relationships can be learned automatically from prior high-quality plans. The contours of planning target volumes and OARs were parsed from the plan database and used as the input to the model, and the dose-area histograms (DAHs) of the OARs were output from the model. The model was trained from scratch by correlating anatomical features with DAHs of OARs, then accumulating these histograms to obtain the final predicted DVH for each OAR. Helical tomotherapy plans for 170 nasopharyngeal cancer patients were used to train and validate the model. An additional 60 patient treatment plans were used to test the predictive accuracy of the model. The DVHs and dose-volume indices (DVIs) of clinical interest for each OAR in the testing dataset were predicted to evaluate the accuracy of the models. The mean absolute errors in the DVIs for each OAR were calculated using each model and statistically compared using a paired-samples t-test. Dice similarity coefficients for areas of the DVHs were also evaluated. Dose-volume histograms of 21 OARs in nasopharyngeal cancer were predicted using the models. For each patient, 63 DVIs for all OARs were calculated. Using the 60 patient treatment plans in the testing dataset, 79% and 73% of the DVIs predicted using the CResDevNet and UNet models, respectively, were within 5% of the clinical values. The median value of the DVIs' mean absolute errors was 3.2±2.5% and 3.7±2.9% for the CResDevNet and UNet models, respectively. The average dice similarity coefficient for all OARs was 0.965 using the CResDevNet model and 0.958 using the UNet model. A deep learning model was developed for predicting achievable DVHs of OARs. The prediction accuracy of the CResDevNet model was evaluated using a planning database of nasopharyngeal cancer cases and shown to be more accurate than the UNet model. Prediction accuracy was also higher for larger-volume OARs. The model can be used for automation of inverse planning and quality assessment of individual treatment plans.

A Risk-Adjusted Control Chart to Evaluate Intensity Modulated Radiation Therapy Plan Quality

PurposeThis study aimed to develop a quality control framework for intensity modulated radiation therapy plan evaluations that can account for variations in patient- and treatment-specific risk factors. Methods and MaterialsPatient-specific risk factors, such as a patient’s anatomy and tumor dose requirements, affect organs-at-risk (OARs) dose-volume histograms (DVHs), which in turn affects plan quality and can potentially cause adverse effects. Treatment-specific risk factors, such as the use of chemotherapy and surgery, are clinically relevant when evaluating radiation therapy planning criteria. A risk-adjusted control chart was developed to identify unusual plan quality after accounting for patient- and treatment-specific risk factors. In this proof of concept, 6 OAR DVH points and average monitor units serve as proxies for plan quality. Eighteen risk factors are considered for modeling quality: planning target volume (PTV) and OAR cross-sectional areas; volumes, spreads, and surface areas; minimum and centroid distances between OARs and the PTV; 6 PTV DVH points; use of chemotherapy; and surgery. A total of 69 head and neck cases were used to demonstrate the application of risk-adjusted control charts, and the results were compared with the application of conventional control charts. ResultsThe risk-adjusted control chart remains robust to interpatient variations in the studied risk factors, unlike the conventional control chart. For the brainstem, the conventional chart signaled 4 patients with unusual (out-of-control) doses to 2% brainstem volume. However, the adjusted chart did not signal any plans after accounting for their risk factors. For the spinal cord doses to 2% brainstem volume, the conventional chart signaled 2 patients, and the adjusted chart signaled a separate patient after accounting for their risk factors. Similar adjustments were observed for the other DVH points when evaluating brainstem, spinal cord, ipsilateral parotid, and average monitor units. The adjustments can be directly attributed to the patient- and treatment-specific risk factors. ConclusionsA risk-adjusted control chart was developed to evaluate plan quality, which is robust to variations in patient- and treatment-specific parameters.

Prediction of dose-volume histograms in nasopharyngeal cancer IMRT using geometric and dosimetric information

A method using both patient geometric and dosimetric information was proposed to predict dose-volume histograms (DVHs) of organs at risk (OARs) for a nasopharyngeal cancer (NPC) intensity-modulated radiation therapy (IMRT) plan.A total of 106 nine-field IMRT NPC plans were used in this study. Twenty-six plans were randomly selected as testing cases, and the remaining plans were used as the training data. A method employing geometric and dosimetric information was developed for OAR DVH prediction. The dosimetric information was derived from an initial dose calculation using a simple unoptimized conformal plan. The DVHs were also predicted using only the geometric information. The DVH prediction model was a generalized regression neural network (GRNN). Mean absolute error (MAE) and R2 values were introduced to evaluate DVH prediction accuracy.Significant differences in the DVH prediction accuracy were found between the method employing the geometric and dosimetric information and the method utilizing the geometric information for the brainstem (R2, 0.98 versus 0.95, p = 0.007; MAE, 3.52% versus 7.19%, p = 0.002), spinal cord (R2, 0.98 versus 0.96, p < 0.001; MAE, 2.80% versus 4.36%, p < 0.001), left optic nerve (R2, 0.90 versus 0.77, p = 0.014; MAE, 3.07% versus 11.29%, p = 0.025) and other organs. On average, the R2 value increased by ~6.7% and the MAE value decreased by ~46.7% after adding the dosimetric information to the DVH prediction.We developed a method for predicting DVHs of OARs in NPC IMRT plans by using geometric and dosimetric information. Adding dosimetric information can help predict the DVHs of OARs in NPC IMRT plans.

Dosimetric features-driven machine learning model for DVH prediction in VMAT treatment planning.

Few features characterizing the dosimetric properties of the patients are included in currently available dose-volume histogram (DVH) prediction models, making it intractable to build a correlative relationship between the input and output parameters. Here, we use planning target volume (PTV)-only treatment plans of the patients (i.e., the achievable dose distribution in the absence of organs-at-risk (OAR) constraints) to estimate the potentially achievable quality of treatment plans and establish a machine learning-based DVH prediction framework with the use of the dosimetric metric as model input parameters. A support vector regression (SVR) approach was used as the backbone of our machine learning model. A database containing volumetric modulated arc therapy (VMAT) plans of 63 prostate cancer patients were used. For each patient, the PTV-only plan was generated first. A correlative relationship between the OAR DVH of the PTV-only plan (model input) and the corresponding DVH of the clinical treatment plan (CTP) (model output) was then established with the 53 training cases. The prediction model was tested by the validation cohort of ten cases. For the training cohort, the checks of dosimetric endpoints (DEs) indicated that 52 of 53 plans (98%) were within the 10% error bound for bladder, and 45 of 53 plans (85%) were within the 10% error bound for rectum. In the validation tests, 92% and 96% of the DEs were within the 10% error bounds for bladder and rectum, respectively, and eight of ten validation plans (80%) were within the 10% error bound for both the bladder and rectum. The sum of absolute residuals (SAR) achieved a mean of 0.034±0.028 and 0.046±0.021 for the bladder and rectum, respectively. A novel dosimetric features-driven machine learning model with the use of PTV-only plan has been established for DVH prediction. The framework is capable of efficiently generating best achievable DVHs for VMAT planning.

Dosimetric comparison of graphical optimization and inverse planning simulated annealing for brachytherapy of cervical cancer.

PurposeGraphical optimization (GO) and inverse planning simulated annealing (IPSA) are the main treatment planning optimization techniques used in patients undergoing 3D brachytherapy treatment. This study aims to compare the dosimetric difference of plans optimized by GO and IPSA in cervical cancer brachytherapy.Material and methods21 cervical cancer patients data sets consisted of computed tomography (CT) and magnetic resonance imaging (MRI), acquired with the Fletcher applicator in situ were transferred to the Oncentra brachytherapy planning system. For each patient, the treatment plan was initially optimized with GO to reach a maximal D90 tumor dose (6 Gy/fraction, 5 fractions), while keeping the dose to organs at risk (OARs) as low as possible. A second plan was then optimized with IPSA on the same CT images and data set (i.e., contours, catheters, and location of dwell points). Targets and OARs dose volume histograms and irradiation time were compared; data were analyzed with paired t-test; p value < 0.05 was considered statistically significant.ResultsThe plans with both optimizations meet the clinical requirements. The mean D90 of the clinical target volume was comparable for GO and IPSA. Similar values (p > 0.05) of target V100, V150, V200, HI, and CI were registered for GO and IPSA optimizations. Bladder and rectum D1cc and D2cc obtained by GO resulted in larger values than those obtained by IPSA (p = 0.002). V75 for bladder and rectum were slightly higher for IPSA, but without statistical difference (p > 0.05). The irradiation time was comparable (p > 0.05).ConclusionsIn 3D brachytherapy of cervical cancer, GO and IPSA optimizations do not present a significant difference in target dose coverage; nevertheless, IPSA may reduce the maximum dose to normal tissue when compared with GO.

Modeling and Prediction of SIB Prostate IMRT Plans

To use retrospective data from simultaneously integrated boost (SIB) prostate intensity modulated radiation therapy (IMRT) plans to model and optimize dose plan outcomes. Modeled outcomes could provide plan guidance for new cases and could also be used as a plan comparison tool. A nonlinear regression based model was developed to map distance to target histograms (DTH) for organs at risk (OARs) to their dose volume histograms (DVH). Contours of patients' planning target volumes (PTVs) and OARs were overlaid on CT image data to compute organ volumes and DTHs. For the training cohort, DVHs were extracted from the SIB PTV (58.8 - 70 Gy) and the OARs. Principle component analysis (PCA) was used to elicit the key feature correlations between the DTH and DVH curves. The initial model cohort consisted of 23 retrospective prostate SIB plans. A leave-one-out training scheme was followed to generate a predicted DVH for each plan. For 85% of the cases, the expert planned OAR DVHs either fell within the 68% confidence interval of the predicted DVH, or the model predicted that a lower dose could be delivered to the OARs. A regression based model was used to map patient anatomical features to OAR DVHs. Cases where the trained model differed significantly from the planned DVH can be further examined to evaluate plan integrity. The results are promising considering the small training set, and model robustness should improve with a larger training cohort. Future work will focus on comparing models trained locally to national planning data. Lung cases, along with head and neck cases will also be considered.

12. Feedback on use of the RapidPlan™ knowledge based planning system for the realization of prostatic treatment planning in volumetric modulated arctherapy

Introduction Inverse planning is an interactive and iterative process that depends on the user experience. This process is time consuming and requires compromises between organ at risk (OAR) sparing and target volume coverage. However, the use of a knowledge based planning system could allow harmonization of practices. Methods RapidPlan™ (Varian) is a mathematical model using existing treatment plans to estimate OARs dose volume histograms and optimization objectives of targets for new plans. The database of the expert system is made with the target volumes and OARs structures, dose prescription and beams arrangement of treatment plan compliant to dosimetric criteria. One year after the introduction of a thesaurus allowing a homogenization in planning practices, a first model composed with 36 patients treated for prostate cancer and seminal vesicles (P + VS) with a dose of 80 Gy in 40 fractions was created. For 125 patients, treatment plans performed with and without the RapidPlan™ system were compared. A second model, extended to 116 patients, was then created on the basis of the first one, including treatment plans with OARs corresponding to a wider range in terms of volumes and also cases with hip prostheses. These prostheses are considered and delineated as normal femoral heads. Optimization objectives and constraints have also been refined in this model. Results With the 36 patient model, 60% of the treatment plans obtained met the current criteria for P + VS localization as early as the first optimization. 45 patients were treated using this model. With the model extended to 116 patients, 119 patients were treated and among them 9 had single or double hip prostheses. 83.2% of treatment plans were acceptable with only one inverse optimization. Among the 20 cases requiring at least one additional optimization, 7 were due to the presence of hip prostheses (2 single and 5 double, or 5.9%), 4 to poor coverage of CTV Prostate (3.4%), 4 to an excess dose delivered to 2% of the rectum (3.4%), 3 to a low bladder filling (2.5%), 1 to an excess dose delivered to 50% of the rectum (0.8%), and 1 to the presence of a femoral cyst (0.8%). Conclusions The RapidPlan™ knowledge based planning system allows homogenization of practices in inverse planning. It increased efficiency with direct access to the result and improved treatment plan quality. RapidPlan™ also opens up an opportunity for knowledge transfer through model transfer.

Artificial neural network based gynaecological image-guided adaptive brachytherapy treatment planning correction of intra-fractional organs at risk dose variation.

PurposeIntra-fractional organs at risk (OARs) deformations can lead to dose variation during image-guided adaptive brachytherapy (IGABT). The aim of this study was to modify the final accepted brachytherapy treatment plan to dosimetrically compensate for these intra-fractional organs-applicators position variations and, at the same time, fulfilling the dosimetric criteria.Material and methodsThirty patients with locally advanced cervical cancer, after external beam radiotherapy (EBRT) of 45-50 Gy over five to six weeks with concomitant weekly chemotherapy, and qualified for intracavitary high-dose-rate (HDR) brachytherapy with tandem-ovoid applicators were selected for this study. Second computed tomography scan was done for each patient after finishing brachytherapy treatment with applicators in situ. Artificial neural networks (ANNs) based models were used to predict intra-fractional OARs dose-volume histogram parameters variations and propose a new final plan.ResultsA model was developed to estimate the intra-fractional organs dose variations during gynaecological intracavitary brachytherapy. Also, ANNs were used to modify the final brachytherapy treatment plan to compensate dosimetrically for changes in ‘organs-applicators’, while maintaining target dose at the original level.ConclusionsThere are semi-automatic and fast responding models that can be used in the routine clinical workflow to reduce individually IGABT uncertainties. These models can be more validated by more patients’ plans to be able to serve as a clinical tool.

O10. Optimism or Greed: Methods of dose escalation in IGABT through total dose constraint optimization

Introduction Image Guided Adaptive Brachytherapy (IGABT) is long overdue in replacing traditional 2D brachytherapy approaches. The key advantage of IGABT is conforming and optimizing dose to the anatomy of the individual patient, considering tumour volume, topography and organs at risk (OARs) on the treatment day. OARs experience inter-fractional variations in volume, geometry and position in relation to tumour volumes. These variations ensure that some of the fractional OAR constraints may be reached in one fraction, but not necessarily in the remaining fractions. Exploitation of these variations through ‘fractionation compensation’ allows tumour dose escalation by optimizing fractional OAR dose constraints during the course of treatment and maintaining total dose constraints. Materials and methods An in-house software module for IGABT treatment planning was developed to test two methods of tumour dose escalation. CT data sets of 10 cervix cancer patients receiving 5 high dose rate brachytherapy treatment fractions were used in the study. Manual optimization based on dose volume histogram (DVH) parameter and equivalent uniform dose (EUD) parameter was performed for all patients. Optimization was performed via fractionation compensation (total dose constraint optimization) in two schemes: ‘Optimistic’ and ‘Greedy’. Optimistic equally distributes the compensation in the remaining undelivered fractions, while Greedy integrates the entire compensation into the current fraction. Results DVH-based optimization resulted in an average tumour D90 of 91.2 Gy and 91.3 Gy for the optimistic and greedy schemes, respectively. EUD-based optimization resulted in 2.8% lower D90 values of 88.7 Gy and 88.8 Gy, respectively, with a reduction in OAR DVH parameters. Conclusion We found no statistically significant difference between the fractionation compensation schemes. However, Greedy resulted in some additional dose escalation for individuals. Both planning approaches reached the tumour dose objectives, while EUD planning resulted in lower OAR DVH parameters.

Treatment planning study of Volumetric Modulated Arc Therapy and three dimensional field-in-field techniques for left chest-wall cancers with regional lymph nodes

AimThis study aims to investigate whether there are dosimetric advantages to using VMAT (Volumetric Modulated Arc Therapy) for left-sided chest-wall patients over the three-dimensional conformal field-in-field (FinF) technique. BackgroundThere is a lack of dosimetric studies dedicated for chest-wall patients. Potential dosimetric advantage could be obtained using VMAT due to complex geometry of PTVs (Planning Target Volumes) and OARs (Organs at Risk) in chest-wall and lymph nodes. Materials and methodsVMAT and FinF plans were generated and evaluated based on DVHs (Dose Volume Histograms) for both PTVs and OARs for 22 left-sided chest-wall patients with involved regional nodes. PTV HIs (Homogeneity Indices) and CIs (Conformity Indices), and EUDs (Equivalent Uniform Doses) for PTVs and OARs were also evaluated for comparisons between VMAT and FinF. ResultsFinF planning met PTV criteria adequately in all cases except two. In these two cases, VMAT was able to meet PTV criteria adequately. VMAT demonstrated significant reduction in left lung V20Gy in chest-wall patients compared to FinF plans. The volumes of the right lung and right breast receiving 5Gy were much higher in VMAT than those in FinF for all patients. ConclusionsCompared to the FinF technique, there is a generally limited benefit using VMAT for left-sided chest-wall patients due to large low-dose-bath to OARs with insignificant improvement in PTV coverage. In case where FinF planning cannot meet dose constrains, VMAT provides a viable option. The use of VMAT planning over the FinF technique in chest-wall cancers should be carefully analyzed on an individual basis.

Predictive value of dose-volume histograms of organs at risk in volumetric modulated arc therapy plans for cervical cancer

Objective To investigate the predictive value of dose-volume histograms (DVHs) of organs at risk (OARs) including the bladder, rectum, and small intestine in volumetric modulated arc therapy (VMAT) plans for cervical cancer. Methods A total of 100 VMAT plans for cervical cancer were assigned into the learning group. The correlation of the anatomical information with the V30, V40, and V50 values of the bladder, rectum, and small intestine was evaluated in the group. The support vector regression (SVR) algorithm was used to establish the correspondence between the anatomical information and the DVHs of OARs. The DVHs of OARs in the verification group containing 20 VMAT plans were predicted based on the anatomical information. Results The DVHs of the bladder, rectum, and small intestine were likely to be influenced mainly by the spatial relationship between these OARs and target volume. In the verification group, the prediction errors of V30, V40 and V50 by SVR algorithm were-2.4%±3.5%, -2.5%±3.8%, and-1.5%±4.9% for the bladder, 0.5%±2.6%, -1.5%±5.1%, and-2.0%±7.4% for the rectum, and-2.9%±5.3%, 2.7%±7.7%, and 5.3%±11.1% for the small intestine, respectively. Conclusions After learning the correlation between the anatomical information and the DVHs of OARs from prior VMAT plans for cervical cancer, SVR algorithm can precisely predict the DVHs of the bladder, rectum, and small intestine based on the anatomical information. Key words: Cervical neoplasms/volumetric modulated arc therapy; Dose volume histograms; Support vector regression algorithm