Dose-volume Histogram Curves Research Articles

Analisis Distribusi Dosis pada Treatment Planning System Pasien Kanker Payudara dengan Sumber Foton 6 MV pada Instalasi Radioterapi Rumah Sakit X

The internship at X Hospital aimed to gain practical experience as a medical physicist over two months, particularly in the treatment planning system activities in the radiotherapy unit. Additionally, this internship aimed to evaluate the Dose Volume Histogram (DVH) and analyze the effectiveness of the isodose lines. The treatment planning system is a simulation of cancer or tumor treatment using radiation with 3D conformal radiotherapy (3D-CRT) techniques. The patient study object was a woman with post-mastectomy breast cancer who was to undergo adjuvant curative therapy. The prescribed dose was 50 Gy, divided into 25 fractions, using a 6 MV photon source. The irradiation was conducted with four fields, gantry angle 129° with collimator angle 270°, 310° with collimator angle 90°, 130° with collimator angle 0°, and 270° with collimator angle 0°, field size 14 x 14 cm, and irradiation distance of 100 cm. The TPS simulation results indicated that the dose received by the Planning Target Volume (PTV) covered more than 90% of the target volume (special treatment), while the dose received by the Organ at Risk (OAR) tended to be below the threshold limit, and the isodose lines ensured optimal radiation dose distribution to the breast cancer target area. This was demonstrated by the DVH curve.

Clinical impact of DVH uncertainties

Dose-volume histograms (DVH), along with dose and volume metrics, are central to radiotherapy planning. As such, errors have the potential to significantly impact the selection of appropriate treatment plans. Dose distributions that pass tests in one TPS may fail the same tests when transferred to another, even if using identical structures and dose grid information. This work shows the design and implementation of methods for assessing the accuracy of dose and volume computations performed by treatment planning systems (TPS), and other analytical tools. We demonstrate examples where differences in calculations between systems can change the assessment of a plan’s clinical acceptability. Our work also provides a more detailed DVH analysis of single targets than earlier published studies. This is relevant for SRS plans and small structure dose assessments. Very small structures are a particular problem because of their coarse digital representation, and the impact of this is thoroughly examined.Reference DVH curves were derived mathematically, based on Gaussian dose distributions centered on spherical structures. The structures and dose distributions were generated synthetically, and imported into RayStation, MasterPlan, and ProKnow. Corresponding DVHs were analytically derived and taken as ground truth references, for comparison with the commercial DVH calculations. Two commonly used dose metrics PCI and MGI were used to determine the limit of calculation accuracy for small structures. In addition, to measure the DVH differences between a larger range of commercial DVH calculators, the D95 metric from a set of real clinical plans was compared across both the 3 DVH calculators under test, and across a further six TPSs from other hospitals.We show that even slight deviations between the results of DVH calculators can lead to plan check failures, and we illustrate this with the commonly used D95 planning metric. We present clinical data across eight planning systems that highlight instances where plan checks would pass in one software and fail in another due to DVH calculation differences. For the smallest volumes tested, errors of up to 20% were observed in the DVHs. RayStation was tested down to a 3 mm radius sphere (≈0.1 cc) and this showed close to 10% error, reducing to 1% for 10 mm radius (≈4.0 cc) and 0.1% for 20 mm radius (≈33 cc). In clinical plans, the variation in D95 was up to 9% for the smallest volumes, and typically around 2% in the range 0.5 cc-20 cc, and 1% in 20 cc-70 cc, falling to <0.1% for large volumes. Paddick Conformity Index (PCI) and Modified Gradient Index (MGI) are commonly used plan quality indicators for very small volumes. For volumes ≈0.1 cc we observed errors of up to 40% in PCI, and up to 75% in MGI.Our study extends the range of tested DVH calculators in published work, and shows their performance over a wider range of volume sizes. We provide quantitative evidence of the critical need to test the accuracy of DVH calculators in the TPS before clinical use. This work is particularly relevant for both stereotactic plan evaluation and for assessment of small volume doses in published dose constraint recommendations. We demonstrate that significant errors can occur in DVHs for volumes less than 1 cc, even if the volumes themselves are calculated accurately. Even for large structures, deviations between the outputs of DVH calculators can lead to indicated or reported plan check failures if they do not include appropriate tolerances. We urge caution in the use of DVH metrics for these very small volumes and recommend that appropriate DVH uncertainty tolerances are set in organ dose constraints when using them to evaluate clinical plans.

Dysphagia in Head and Neck Radiotherapy: The Influence of Pharyngeal Constrictor Anatomy and Dosimetry.

The goal of this study was to identify which anatomical and dosimetric changes correlated with late patient-reported dysphagia throughout the course of head and neck chemo-radiotherapy treatment. The patient cohort (n = 64) considered oropharyngeal and nasopharyngeal patients treated with curative intent, exhibiting no baseline dysphagia with a follow-up time greater than one year. Patients completed the MD Anderson Dysphagia Inventory during a follow-up visit. A composite score was measured ranging from 20 to 100, with a low score indicating a high symptom burden; a score ≤60 indicated patient-reported dysphagia. The pharyngeal (PCM) and cricopharyngeal constrictor muscles (CPM) were contoured on a planning CT image and adapted to weekly cone-beam CT anatomy using deformable image registration and dose was accumulated using weighted dose-volume histogram curves. The PCM and CPM were examined for volume, thickness, and dosimetric changes across treatment with the results correlated to symptom group. Anatomical evaluation indicated the PCM thickness increased more during treatment for patients with dysphagia, with base of C2 vertebrae (p = 0.04) and superior-inferior middle PCM (p = 0.01) thicknesses indicating a 1.0-1.5mm increase. The planned and delivered mean dose and DVH metrics to PCM and CPM were found to be within random error measured for the dose accumulation, indicating delivered and planned dose are equivalent. The PCM and CPM organs were found to lie approximately 5mm closer to high dose gradients in patients exhibiting dysphagia. The volume, thickness, and high dose gradient metrics may be useful metrics to identify patients at risk of late patient-reported dysphagia.

Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy

AimsDelineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer. Materials and methodsEighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison. ResultsVariations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20–11.09 Gy and 0.72–4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%–65.3% and 7.8%–32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient. ConclusionRadiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the ‘dose range’ where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.

Multicenter Multivendor Evaluation of Dose Volume Histogram Creation Consistencies for 8 Commercial Radiation Therapy Dosimetric Systems

PurposeTo evaluate dose volume histogram (DVH) construction differences across eight major commercial treatment planning systems (TPSs) and dose reporting systems (DRSs) for clinically treated plans of various anatomical sites and target sizes. Materials and MethodsDose files from ten selected clinically treated plans with a hypo-fractionation, stereotactic radiotherapy prescription or sharp dose gradients such as head and neck plans ranging from prescription doses of 18 Gy in 1 fraction to 70 Gy in 35 fractions, each calculated at 0.25 cm and 0.125 cm grid size, were created and anonymized in Eclipse TPS, and exported to seven other major TPS (Pinnacle, RayStation, Elements) and DRS (MIM, Mobius, ProKnow, and Velocity) systems for comparison. Dose-volume constraint points of clinical importance for each plan were collected from each evaluated system (D0.03cc[Gy], volume, and the mean dose were used for structures without specified constraints). Each reported constraint type and structure volume was normalized to the value from Eclipse for a pairwise comparison. A Wilcoxon Rank Sum test was used for statistical significance and a multivariable regression model was evaluated adjusting for plan, grid size, and distance to target center. ResultsFor all DVH points relative to Eclipse, all systems reported median values within 1.0% difference of each other, however they were all different from Eclipse. Considering mean values, Pinnacle, RayStation, and Elements averaged at 1.038, 1.046, 1.024 respectively, while MIM, Mobius, ProKnow, and Velocity reported 1.026, 1.050, 1.033 and 1.022 respectively relative to Eclipse. Smaller dose grid size improved agreement between the systems marginally without statistical significance. For structure volumes relative to Eclipse, larger differences are seen across all systems with a range in median values up to 3.0% difference and mean up to 10.1% difference. ConclusionLarge variations were observed between all systems. Eclipse generally reported, at statistically significant levels, lower values than all other evaluated systems. The non-significant change resulting from lowering the dose grid resolution indicates that this resolution may be less important than other aspects of calculating DVH curves, such as the 3D modeling of the structure.

Declarations of Independence: How Embedded Multicollinearity Errors Affect Dosimetric and Other Complex Analyses in Radiation Oncology

The statistical technique of multiple regression, commonly referred to as “multivariable regression,” is often used in clinical research to quantify the relationships between multiple predictor variables and a single outcome variable of interest. The foundational theory underpinning multivariable regression assumes that all predictor variables are independent of one another. In other words, the effect of each independent variable is measured by its contribution to the regression equation while all other variables remain unchanged. In the presence of correlations between two or more variables, however, it is impossible to change one variable without a consequent change in the variable(s) it is linked to. This condition, known as “multicollinearity,” can introduce errors into multivariable regression models by affecting estimates of the regression coefficients that quantify the relationship between individual predictor variables and the outcome variable. Errors that arise due to violations of the multicollinearity assumption are of special interest to radiation oncology researchers. Because of high levels of correlation among variables derived from points along individual organ dose-volume histogram (DVH) curves, as well as strong intercorrelations among dose-volume parameters in neighboring organs, dosimetric analyses are particularly subject to multicollinearity errors. For example, dose-volume parameters for the heart are strongly correlated not only with other points along the heart DVH curve but are likely also correlated with dose-volume parameters in neighboring organs such as the lung. In this paper, we describe the problem of multicollinearity in accessible terms and discuss examples of violations of the multicollinearity assumption within the radiation oncology literature. Finally, we provide recommendations regarding best practices for identifying and managing multicollinearity in complex data sets.

Tracking OAR Volume and DVH Variability in the Initial Cohort of Pelvic Patients Treated with CBCT-Guided, Online Adaptive Therapy

<h3>Purpose/Objective(s)</h3> We have recently commissioned and began treatment using a novel, CBCT-guided, online adaptive radiotherapy (ART) platform. We have evaluated the volumetric and dosimetric variation in our initial cohort of patients. <h3>Materials/Methods</h3> We have tracked our 6 initial online adaptive pelvis patients for 208 delivered fractions. A custom script was developed to extract volume and dosimetry data from our second check software. Target and organ-at-risk (OAR) volumes were gathered for every fraction. Structure volume variations were computed relative to the reference plan for each fraction and averaged per patient. Dose-volume histogram (DVH) data was collected for the primary treatment plan for our four prostate patients, all treated to 45 Gy in 25 fractions. The range of OAR DVH values at 20, 30, and 40 Gy and PTV V45 Gy over the primary course of each patient's treatment was computed for both scheduled and daily adapted plans. <h3>Results</h3> OAR variability was very patient dependent. Bladder volumes showed the largest variation, with most patients unable to replicate their bladder filling from simulation. Mean deviations in bladder volume ranged from -47% to +12%, with standard deviations >20% for two-thirds of patients. Rectal volume was also variable, but to a lesser extent, with mean variations ranging from -31% to +16%. DVH data demonstrates that ART produces a much tighter distribution of DVH curves compared to the scheduled plan for all structures while maintaining PTV coverage to within 1% of the reference value. The variability of V40Gy, V30Gy, and V20Gy showed reductions of 13.9%, 29.8%, 30.9% for bladder and 52.6%, 48.9%, and 24.6% for rectum relative to the scheduled plan, respectively. Using only the scheduled plan would result in reductions of up to 8% of target coverage compared to the reference plan. <h3>Conclusion</h3> We have observed considerable OAR interfraction volume variations during the course of pelvic radiation that can be highly patient dependent. Use of ART maintains adequate target coverage while reducing the variability in dose delivery to these nearby OARs. Combined with the large variability in OAR filling, we have demonstrated that ART enables clinicians to have a higher likelihood of delivering their intended doses to targets and OARs compared to standard-of-care radiotherapy.

Assessment of dose-volume histogram precision for five clinical systems.

To investigate the dependency of dose-volume histogram (DVH) behavior and precision on underlying discretization using shapes and dose distributions with known analytical DVHs for five commercial DVH calculators. DVHs and summary metrics were extracted from all five systems using synthetic cone and cylinder objects for which the true volume and DVH curves were known. Trends in the curves and metrics were explored by varying the underlying voxelization of the CT image, structure set, and dose grid as well by varying the geometry of the structure and direction of a linear dose gradient. Using synthetic structures allowed for comparison with ground truth DVH curves to assess their accuracy while an algorithm was additionally developed to assess the precision of each system. The precision was calculated with a novel algorithm that treats any "stair step" behavior in a DVH curve as an uncertainty band and calculates the width, characterized as a percent difference, of the band for various DVH metrics. The underlying voxelization was additionally changed and DVHs were extracted for two clinical examples. The details of how each system calculated DVHs were also investigated and tendencies in the calculated curves, metrics, and precision were related to choices made in the calculation methodology. Calculation methodology differences that had a noticeable impact on the DVH curves and summary metrics include supersampling beyond the input grids and interpretation of the superior and inferior ends of the structures. Among the systems studied, the median precision ranged from 0.902% to 3.22%, and interquartile ranges varied from 1.09% to 3.91%. Commercial dose-evaluation solutions can calculate different DVH curves, structure volume measures, and dose statistics for the same input data due to differences in their calculation methodologies. This study highlights the importance of understanding and investigating the DVH calculation when considering a new clinical system and when using more than one system for data transfer.

Skin dose distributions between Stanford and rotational techniques in total skin electron therapy (TSET).

Total skin electron therapy (TSET) has proven to be one of the most effective treatments for advanced-stage cutaneous T-cell lymphoma. Two most used techniques are the Stanford six-field and rotational techniques. This study compares patient skin dose distributions as a function of depth between these two techniques. The EGSnrc system was used to simulate electron beams and calculate patient dose distributions. The calculations assumed the same patient standing on a platform, and the patient's different postures were ignored for the Stanford technique in the comparison of dose distributions. The skin doses were analyzed as a function of skin depth-dose coverage and evaluated using dose-volume-histograms (DVH). The comparisons were performed in three realistic clinical settings in which dual-field were used for patients treated at extended distances of 316 and 500cm, and a single field was used at 700 cm. In all cases the realistic patient treatment beam delivery geometry was simulated. Although small dose differences were observed in some local areas, no clinically significant differences were found in the patient 3D dose distributions between the Stanford and rotational techniques. Virtually the same DVH curves between two the techniques were observed for mean dose to skin depth of 0-5, 5-10, and 10-15 mm from the skin surface, respectively. It is found that the skin depth dose coverage is 2 mm shallower for patient treatment at 500cm compared to at 316cm due to the additional air attenuation. However, very similar dose coverage and uniformity can be achieved at these two different extended treatment distances by adjusting the thickness of acrylic scatter plate. Adequate thickness of a scattering plate improves the skin dose uniformity. Both the Stanford and rotational techniques deliver very similar skin dose coverage in DVH plots, and only small differences are seen in local areas. It is worth to emphasize that the DVH is a graphical representation of the distribution of dose within a structure, and it does not contain spatial information. Therefore, comparison of entire skin dose using DVH may mask some variations at different locations of the surface area. In addition, the comparison did not consider different patient postures of the Stanford technique. Including the different patient postures in the calculation may affect the result of doses to the limbs.

Use of CBCT plus plan robustness for reducing QACT frequency in intensity-modulated proton therapy: Head-and-neck cases.

Anatomic variation has a significant dosimetric impact in intensity-modulated proton therapy. Weekly or biweekly computed tomography (CT) scans, called quality assurance CTs (QACTs), are used to monitor anatomic and resultant dose changes to determine whether adaptive plans are needed. Frequent CT scans result in unwanted QACT dose and increased clinical workloads. This study proposed utilizing patient setup cone-beam CTs (CBCTs) and treatment plan robustness to reduce the frequency of QACTs. We retrospectively analyzed data from 27 patients with head-and-neck cancer, including 594 CBCTs, 136 QACTs, and 19 adaptive plans. For each CBCT, water-equivalent thickness (WET) along the pencil-beam path was calculated. For each treatment plan, the WET of the first-day CBCT was used as the reference, and the mean WET changes (ΔWET) in each following CBCT was used as the surrogate of proton range change. Using CBCTs acquired prior to a QACT, we predicted the ΔWET on the QACT day by a linear regression model. The impact of range change on target dose was calculated as the predicted ΔWET weighted by the monitor units of each field. In addition, plan robustness was estimated from the robust dose-volume histograms (DVHs) and combined with ΔWET to reduce QACT frequency. Robustness was estimated from the distance between the DVH curves of the nominal and worst scenarios. When the estimated mean ΔWET was<6.5mm(or<7.5mmif the robustness was>95%),the QACT could be skipped without missing any adaptive planning; otherwise a QACT was required. Overall, 41% of QACTs could be eliminated when ΔWET was<6.5mmand 56% when ΔWET was<7.5mm,and robustness was>95%.At least one QACT could have been omitted in 25 of the 27 cases under skipping thresholds at ΔWETs<7.5mm and R>95%. This study suggests that the number of QACTs can be greatly reduced by calculating range change in patient setup CBCTs and can be further reduced by combining this information with analyses of plan robustness.

Feasibility of Using FDG in the Stereotactic Ablative Setting for Tracked Dose Delivery With BgRT: Results from a Prospective Study of Serial Inter-Fraction PET/CTs

<h3>Purpose/Objective(s)</h3> Biology-guided radiotherapy (BgRT) is a new radiation modality that utilizes real-time limited time sampled FDG PET images of a tumor to deliver a dynamically tracked dose distribution. Since the delivered radiotherapy dose is dependent on the FDG distribution at each fraction, the ability to deliver the planned dose despite variations in daily FDG signal must be established. This study evaluates the predicted dose distribution for BgRT based on serial FDG scans obtained over a course of stereotactic ablative radiotherapy (SABR). <h3>Materials/Methods</h3> Six patients with 1 (n = 5) or 2 (n = 1) lung tumors at least 2 cm in diameter with SUV_max ≥ 6 treated with SABR (50 Gy in 5 fractions) were selected for this investigation. These patients received 3 PET/CT scans as part of an IRB approved prospective clinical trial: within 2 weeks of treatment start (PET1), between fractions 1 and 2 (PET2), and between fractions 4 and 5 (PET3). A simulation tool was developed to convert PET1 into a simulated BgRT planning PET image accounting for differences in system sensitivity, acquisition time, reconstruction method and geometry between the diagnostic PET/CT system and the BgRT machine. This same tool was used to convert PET2 and PET3 into simulated pre-scan PET images, short PET acquisitions made just prior to a BgRT delivery fraction to ensure the current FDG distribution is safe for treatment. BgRT plans designed to deliver 50 Gy in 5 fractions following RTOG 0813 dose constraints were developed for each of the 7 targets using the simulated planning PET images as input. Expected variations in the dynamically calculated dose delivery are presented as bounds on the dose-volume histogram (DVH). For each plan, the predicted DVH for fractions 2 and 5 was calculated from the simulated pre-scan PET images and compared to the bounded DVH of the BgRT plan. The pass %, defined as the percentage of points on the predicted DVH curves falling within the planned DVH bounds, was automatically calculated; the pass % must be ≥ 95% to proceed with BgRT delivery. <h3>Results</h3> All 7 BgRT plans met target coverage objectives and normal tissue constraints. The mean pass % for the 14 pre-scans was 98.6%, with 13 exceeding the 95% threshold required for BgRT. There was no statistically significant difference between pass % at fraction 2 versus fraction 5 (two-tailed paired t-test, <i>P</i> = 0.44). The pass % was 92.2% for 1 pre-scan. In this case, the target SUV_max had decreased by 55.5% of the baseline value. <h3>Conclusion</h3> This investigation provides initial evidence that FDG scans remain stable enough over a course of ablative radiotherapy to enable dynamically tracked dose delivery with BgRT. Significant changes in FDG biodistribution that would prevent dose from being delivered within the pre-approved bounds of the BgRT plan can be identified through analysis of a pre-scan PET image. A prospective clinical trial to confirm these results is in preparation. Clinical trial number NCT03493789.

Evaluating the Effect of the Silicone Prosthesis on the Photon Dose Distribution in Radiation Therapy of Breast Cancer

Purpose: Breast cancer is the most common malignancy among women which in some cases is followed by breast reconstructions. The objective of the experimental study is to investigate the effect of the silicone prosthesis implementation on the dose distribution of radiotherapy. Materials and Methods: Initially CT images of 7 mastectomy breast patients with silicone prosthesis were imported to the Monaco treatment planning system. A treatment plan consisting of two tangential photon fields with a prescription dose of 50Gy was arranged. To study the effect and water equivalency of silicone prosthesis, dose distribution of treatment plan was acquired in two conditions: 1) considering the real electron density of silicone prosthesis; 2) modifying (Relative electron density) RED of silicone prosthesis to 1 to virtually assume it as soft tissue (water). The results were then compared by VeriSoft software to evaluate the gamma index. Results: The obtained results indicated that the RED for the silicon prosthesis varies between 0.7 and 1.14 while the RED for soft tissue is approximately 1. Also, the Dose-volume histogram curves for both conditions indicated that the minimum and maximum differences ranged from 1% to 4%. The significant differences might be due to the presence of the air cavity or bubbles in the silicone prosthesis implementation or air voxels between prostheses and soft tissue. Conclusion: The obtained results showed that if there is no air cavity in silicone prosthesis and the surgery is performed in a way that no volume of air is left between the prosthesis and breast tissue, the effect and presence of silicone prosthesis will be similar to soft tissue (water).

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs. In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients. The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose. In this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.

Evaluation of Dosimetric Parameters for Tumor Therapy with 177Lu and 90Y Radionuclides in Gate Monte Carlo Code.

Background: 90Y and 177Lu are two well-known radionuclides used in radionuclide therapy to treat neuroendocrine tumors.Objective: This current study aims to evaluate, compare and optimize tumor therapy with 90Y and 177Lu for different volumes of the tumor using the criterion of self-absorbed dose, cross-absorbed dose, absorbed dose profile, absorbed dose uniformity, and dose-volume histogram (DVH) curve using Gate Monte Carlo simulation code. Material and Methods: In our analytical study, Gate Monte Carlo simulation code has been used to model tumors and simulate particle transport. Spherical tumors were modeled from radius 0.5 to 20 mm. Tumors were uniformly designed from water (soft tissue reagent). The full energy spectrum of each radionuclide of 177Lu and 90Y was used in the total volume of tumors with isotropic radiation, homogeneously. Self-absorbed dose, cross-absorbed dose, absorbed dose profile, absorbed dose uniformity, and DVH curve parameters were evaluated. Results: The absorbed dose for 90Y is higher than 177Lu in all tumors (p-value <5%). The uniformity of the absorbed dose for 177Lu is much greater than 90Y. As the tumor size increases, the DVH graph improves for 90Y. Conclusion: Based on self-absorbed dose, cross-absorbed dose, absorbed dose uniformity, and DVH diagram, 177Lu and 90Y are appropriate for smaller and larger tumors, respectively. Next, we can evaluate the appropriate cocktail of these radionuclides, in terms of the type of composition, for the treatment of tumors with a specific size.

NTCP Modeling of Late Effects for Head and Neck Cancer: A Systematic Review.

NTCP Modeling of Late Effects for Head and Neck Cancer: A Systematic Review.

Prediction of Pituitary Gland Complications by LKB and Log-Logistic Radiobiological Models in 3D Conformal Radiation Therapy of Head and Neck Tumors

Introduction: The pituitary gland is frequently irradiated during radiation therapy of head and neck tumors which can influence the quality of life of the patients after radiation therapy. This study aimed to estimate the normal tissue complication probability (NTCP) for the pituitary gland in head and neck cancers using two radiobiological models. Material and Methods: 53 patients including 20 cases with nasopharyngeal cancer and 33 cases with brain tumor were studied. The dosimetric properties of each plan including minimum, mean and maximum doses were extracted from the dose-volume histogram curve. For estimation of pituitary gland response for each patient, the BIOPLAN software was used to calculate NTCP by LKB model and Matlab software was applied to calculate NTCP by equivalent uniform dose (EUD) model. Models’ parameters including TD50, , and‘a’ were extracted from a previous study of radiobiological modeling of pituitary gland response to radiation therapy. For statistical analysis, the T-test was used to compare two models. Results: The average mean doses of 30.42 and 51.29 (Gy) of the pituitary gland were obtained for nasopharyngeal and brain tumor patients, respectively. The average NTCPs of the pituitary gland for nasopharyngeal patients estimated by LKB and Log-logistic models were 3.84 and 3.91%, respectively. In brain tumors, the average NTCP was 16.33% for LKB and 16.41% for Log-logistic models. The results showed that the log-logistic and LKB models provided comparable results and no statistically significant difference (P-value< 0.05) was found between two models. Conclusion: The NTCP results indicated that the average NTCP of the pituitary gland for nasopharyngeal patients was approximately four times lower than that of brain tumors. Finally, implementation of follow up studies and modeling investigations are recommended for accurate estimation of pituitary gland complications following radiation therapy.

DVHnet: A deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning.

To develop a deep learning method to predict patient-specific dose volume histograms (DVHs) for radiotherapy planning. Patient data included 180 cases with nasopharyngeal cancer, of which 153 cases were used for training and 27 for testing. A network (named "DVHnet") based on a convolutional neural network (CNN) was designed for directly predicting DVHs of organs at risk (OARs). Two-channel images with contoured structures were generated as the inputs for training the model. A one-dimensional array consisting of 256 continuous volume percentages on a DVH curve for each slice was calculated as the corresponding output. The combined DVH was then calculated. Sixteen OARs were modeled in the study. Prediction accuracy was evaluated against the corresponding DVH curve of ground truth (GT) plans. A global DVH analysis and critical dosimetry metrics for each OAR were calculated for quantitative evaluation. The performance of DVHnet also was evaluated against two baselines: DosemapNet (developed by our research group) and commercial RapidPlan software. The predicted mean difference in average dose of all OARs using DVHnet was 0.30±0.95Gy. And the predicted differences in D2% and D50 can be control within 2.32 and 0.69Gy. For most OARs, there were no obvious differences between the dosimetric metrics of the predicted and GT values for both DVHnet and DosemapNet (P≥0.05). Only the predicted D2% of the optic organs for DVHnet, and of brain stem PRV for DosemapNet displayed statistically significant differences. Except for the optic organs, DVHnet performs better than or comparably with RapidPlan. The mean difference in proportion of points of interest was 3.59%±7.78%. A deep learning network model was developed to automatically extract useful features for accurate prediction of patient-specific DVH curves directly. The performance of DVHnet was comparable to DosemapNet and RapidPlan.

A Pilot Study of Safer Radiation Dosage to the Heart and Its Subregions.

Background and Objectives: The real impact of ionizing radiation on the heart and poorer overall survival for patients with non small cell lung cancer (NSCLC) remains unclear. This study aims to determine the safe dose constraints to the heart’s subregions that could prevent patients’ early non-cancerous death and improve their quality of life. Methods and Materials: A retrospective cohort study was performed containing 51 consecutive patients diagnosed with stage III NSCLC and treated using 3D, Intensity-modulated radiation therapy (IMRT), and Volumetric modulated arc therapy (VMAT) radiotherapy. For a dosimetric analysis, these structures were chosen: heart, heart base (HB), and region of great blood vessels (GBV). Dose–volume histograms (DVH) were recorded for all mentioned structures. Maximum and mean doses to the heart, HB, the muscle mass of the HB, and GBV were obtained. V10–V60 (%) parameters were calculated from the DVH. After performed statistical analysis, logistic regression models were created, and critical doses calculated. Results: The critical dose for developing a fatal endpoint for HB was 30.5 Gy, while for GBV, it was 46.3 Gy. Increasing the average dose to the HB or GBV by 1 Gy from the critical dose further increases the possibility of early death by 22.0% and 15.8%, respectively. Conclusions: We suggest that the non-canonical sub-regions of the heart (HB and GBV) should be considered during the planning stage. Additional constraints of the heart subregions should be chosen accordingly, and we propose that the mean doses to these regions be 30.5 Gy and 46.3 Gy, respectively, or less. Extrapolated DVH curves for both regions may be used during the planning stage with care.

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

PurposeThe purpose of this study was to develop automated planning for whole‐brain radiation therapy (WBRT) using a U‐net‐based deep‐learning model for predicting the multileaf collimator (MLC) shape bypassing the contouring processes.MethodsA dataset of 55 cases, including 40 training sets, five validation sets, and 10 test sets, was used to predict the static MLC shape. The digitally reconstructed radiograph (DRR) reconstructed from planning CT images as an input layer and the MLC shape as an output layer are connected one‐to‐one via the U‐net modeling. The Dice similarity coefficient (DSC) was used as the loss function in the training and ninefold cross‐validation. Dose‐volume‐histogram (DVH) curves were constructed for assessing the automatic MLC shaping performance. Deep‐learning (DL) and manually optimized (MO) approaches were compared based on the DVH curves and dose distributions.ResultsThe ninefold cross‐validation ensemble test results were consistent with DSC values of 94.6 ± 0.4 and 94.7 ± 0.9 in training and validation learnings, respectively. The dose coverages of 95% target volume were (98.0 ± 0.7)% and (98.3 ± 0.8)%, and the maximum doses for the lens as critical organ‐at‐risk were 2.9 Gy and 3.9 Gy for DL and MO, respectively. The DL technique shows the consistent results in terms of the DVH parameter except for MLC shaping prediction for dose saving of small organs such as lens.ConclusionsComparable with the MO plan result, the WBRT plan quality obtained using the DL approach is clinically acceptable. Moreover, the DL approach enables WBRT auto‐planning without the time‐consuming manual MLC shaping and target contouring.

A simple dosimetric approach to spatially fractionated GRID radiation therapy using the multileaf collimator for treatment of breast cancers in the prone position.

The purpose of this study was to explore the treatment planning methods of spatially fractionated radiation therapy (SFRT), commonly referred to as GRID therapy, in the treatment of breast cancer patients using multileaf collimator (MLC) in the prone position. A total of 12 patients with either left or right breast cancer were retrospectively chosen. The computed tomography (CT) images taken for the whole breast external beam radiation therapy (WB‐EBRT) were used for GRID therapy planning. Each GRID plan was made by using two portals and each portal had two fields with 1‐cm aperture size. The dose prescription point was placed at the center of the target volume, and a dose of 20 Gy with 6‐MV beams was prescribed. Dose‐volume histogram (DVH) curves were generated to evaluate dosimetric properties. A modified linear‐quadratic (MLQ) radiobiological response model was used to assess the equivalent uniform doses (EUD) and therapeutic ratios (TRs) of all GRID plans. The DVH curves indicated that these MLC‐based GRID therapy plans can deliver heterogeneous dose distribution in the target volume as seen with the conventional cerrobend GRID block. The plans generated by the MLC technique also demonstrated the advantage for accommodating different target shapes, sparing normal structures, and reporting dose metrics to the targets and the organs at risks. All GRID plans showed to have similar dosimetric parameters, implying the plans can be made in a consistent quality regardless of the shape of the target and the size of volume. The mean dose of lung and heart were respectively below 0.6 and 0.7 Gy. When the size of aperture is increased from 1 to 2 cm, the EUD and TR became smaller, but the peak/valley dose ratio (PVDR) became greater. The dosimetric approach of this study was proven to be simple, practical and easy to be implemented in clinic.