Differential Dose-volume Histograms Research Articles

Treatment planning with high-resolution 3D dose maps in preclinical and translational synchrotron microbeam radiation therapy

Background and PurposeMicrobeam Radiation Therapy (MRT) aims to deliver higher doses to the target while minimizing radiation damage to healthy tissues using synchrotron x-ray microbeams. Translational MRT research has now started, driven by promising results from preclinical studies. This study aimed to propose a first dose-outcome model by analyzing micrometric dose distributions obtained with high-resolution 3D dose calculations, accounting for the inherent physical dose distribution complexity in MRT. The feasibility of integrating penMRT, our full Monte Carlo multiscale dose calculation algorithm based on PENELOPE into translational research on veterinary patients was also investigated. Material and MethodsMicrometric dose distributions were calculated in tumor-bearing rats and for a veterinary patient with penMRT, for conformal multi-directional MRT treatment plans. Absorbed dose maps were obtained with 0.005 × 0.005 × 1 mm3 voxel sizes. High-resolution dose-volume histograms were extracted and analyzed against radiobiology studies. ResultsThe complexity of the MRT dose distribution was properly rendered at a micrometer scale on 3D dose maps, with well separated dose regions observed on the differential dose-volume histograms. The median survival time of glioma-bearing rats varied linearly with the volume fraction of the planning target volume that received doses higher than 50 Gy (R2 = 0.98). The feasibility of using penMRT for treatment planning in large volumes has been shown on a veterinary patient. ConclusionsThis study demonstrated the significant added value of penMRT for planning and prescribing MRT treatments. It also shed light on the correlation between the high-resolution 3D dose distributions and the treatment outcome.

Improved Dosimetry and Plan Quality for Accelerated Partial Breast Irradiation Using Online Adaptive Radiation Therapy: A Single Institutional Study

PurposeAccelerated partial breast irradiation (APBI) is an attractive treatment modality for eligible patients as it has been shown to result in similar local control and improved cosmetic outcomes compared to whole breast radiotherapy. The use of online adaptive radiotherapy (OART) for APBI is promising as it allows for a reduction of PTV margins since breast motion and lumpectomy cavity volume changes are accounted for in daily imaging. Here we present a retrospective, single-institution evaluation on the adequacy of kV-cone beam computed tomography (CBCT) OART for APBI treatments. Methods and Materials19 patients (21 treatment sites) were treated to 30 Gy in 5 fractions between January of 2022 and May of 2023. Time between simulation and treatment, change in gross tumor (i.e. lumpectomy cavity) volume (GTV), and differences in dose volume histogram (DVH) metrics with adaption were analyzed. The Wilcoxon paired, non-parametric test was utilized to test for DVH metric differences between the scheduled plans (initial plans recalculated on daily CBCT anatomy) and delivered plans, either the scheduled or adapted plan, which was re-optimized using daily anatomy. ResultsMedian (inter-quartile range) time from simulation to first treatment was 26 days (21-32 days). During this same time, median GTV volume reduction was 16.0% (7.3-23.9%) relative to simulation volume. Adaptive treatments took 31.3 min (27.4-36.6 min) from start of CBCT to treatment session end. At treatment, the adaptive plan was selected for 86% (89/103) of evaluable fractions. In evaluating plan quality, 78% of delivered plans met all target, OAR, and conformity metrics evaluated, compared to 34% of scheduled plans. ConclusionsUse of OART for stereotactic linac-based APBI allowed for safe, high-quality treatments in this cohort of 21 treatment courses. Although treatment delivery times were longer than traditional stereotactic body treatments, there were notable improvements in plan quality for APBI using OART.

Influence of Dose Distribution in the Target on the Values of Integral Radiobiological Criteria Based on Synthetic Dose-Volume Histograms

Purpose: To investigate the influence of absorbed dose distribution in the tumor volume on the values of
 integral radiobiological criteria, such as the probability of local tumor control (Tumor Control Probability, TCP) and normal tissue complication probability (NTCP), based on synthetic dose-volume histograms (DVH), which enables to assess the treatment effectiveness and probability of complications
 from healthy organs and tissues.
 Material and methods: Synthetic dose-volume histograms were considered theoretically possible to analyze the dependence of equivalent uniform dose (EUD) on values of Nimirko model parameters and absorbed dose distribution for both tumor and critical organs. The calculation was carried out in Wolfram
 Mathematica program based on the algorithm, where an array of absorbed dose values per fraction of
 irradiation volume with step by absorbed dose was calculated for the given values of absorbed dose and
 the parameter determining the width of distribution. The resulting array was differentiated to obtain
 the differential DVH. The EUD values were obtained from the normalized differential DVHs.
 Results: During analysis of EUD value dependence on parameter a values determining degree of nonlinearity of absorbed dose summation in sub-volumes of irradiation and on absorbed dose distribution
 the following was revealed: for critical organs at high values of parameter a the essential role belongs to
 high and average values of absorbed dose, and for tumors at low values of parameter a the main role belongs to the minimum value of absorbed dose. Thus, the value of parameter a should be negative for a
 tumor, and positive for critical organs, and should be specifically defined for specific structures.
 Conclusion: To improve the predictive value (prediction) of the effect of radiotherapy using the
 TCP/NTCP Nimirko model, an adequate definition of the parameter a, the value of which can only be determined from absorbed dose distributions over the volume of anatomical structures within clinical trials, is necessary.

Toxicity Analysis of a Modified Prostate Bed CTV Based on PSMA-PET Patterns of Failure.

After radical prostatectomy (RP), about 20-50% of prostate cancer patients experience biochemical failure, and many can be cured with salvage radiotherapy (sRT). Adding androgen deprivation and pelvic lymph node (PLN) RT to sRT decreases risk of disease progression at the cost of increased toxicity. PSMA PET patterns of local failure after RP show failures outside the RTOG prostate bed CTV (RTOG_CTV) occurring postero-laterally and inferiorly, while in much of the covered bladder local failures are not seen. A modified CTV (m_CTV) encompassing all sites of observed local failures and less of the bladder would potentially reduce dose to the bladder but increase dose to rectum. Herein, we compared sRT plans using RTOG_CTV vs. m_CTV and assessed predicted genitourinary (GU) and gastrointestinal adverse events (AEs). Volumes for RTOG_CTV, m_CTV, PLN CTV, and OARs were retrospectively contoured on planning CT scans of 28 post-RP patients. Plans were generated for each RTOG_CTV and m_CTV. Doses to prostate bed PTV (CTV + 8mm expansion, 6mm posteriorly) and PLN CTV were 68 Gy and 52.7 Gy, respectively, all in in 34 fractions. Dose-volume histogram (DVH) data for bladder, rectum, and bowel of each plan were extracted. Normal Tissue Complication Probability (NTCP) curves were generated using Lyman-Kutcher-Burman model to predict risk of late G≥3 GU AEs, early GU urgency, leakage, frequency, nocturia, late GI bleed, and late G≥3 GI AEs. Data were compared using 2 sample t-test and Wilcox signed rank test. Correlation of bladder volume and differences in DVH and NTCP data between plans was assessed using Pearson coefficient. The m_CTV and RTOG_CTV had a median volume of 70.56cc (IQR 59.72-82.17cc) and 132.14cc (IQR 109.33-155.84cc), respectively. Use of m_CTVs resulted in mean absolute decrease of 20.22% and 17.94% in bladder V65 Gy and V40 Gy, respectively (p<0.01 for both), without differences in rectum V65 Gy (p = 0.44), rectum V40 Gy (p = 0.11), or bowel V45 Gy (p = 0.29). Per NTCP curves, m_CTV plans had mean absolute decrease of 24.89% in predicted late G≥3 GU AEs (p<0.01). For predicted early GU AEs, there were mean absolute decreases ranging from 4.85 to 10.21% (p<0.01 for all). Predicted late GI bleed (p = 0.98) and late G≥3 GI AEs (p = 0.99) were not significantly different between plans. When assessed as a continuous variable, smaller bladder volumes had strong correlation with more benefit from m_CTV in bladder V65 Gy (r = -0.71, p<0.01) and moderate correlation with less early GU AEs (r -0.59 to -0.56, p<0.01 for all). There was no correlation with bladder V40 Gy (p = 0.27) or late G≥3 GU AEs (p = 0.14). The m_CTV plans had significantly less dose to the bladder, no increased dose to the rectum and significantly lower predicted rates of early and late GU toxicity. Using a m_CTV encompassing observed local failures from PSMA PET could improve the therapeutic ratio for patients undergoing sRT. Further clinical validation is warranted.

NTCP Calculations of Five Different Irradiation Techniques for the Treatment of Thymoma.

This study provided normal tissue complication probability (NTCP) calculations from photon radiotherapy techniques in eleven patients with thymoma. Five plans were created for each participant using three-dimensional conformal radiotherapy (3D-CRT), five-field intensity modulated radiotherapy (5F-IMRT), seven-field IMRT (7F-IMRT), and volumetric modulated arc therapy with full arcs (FA-VMAT) and partial arcs (PA-VMAT). The target coverage, homogeneity index and conformation number for the planning target volume (PTV) and dosimetric parameters for the organs-at-risk (OARs) were taken from the fifty-five generated plans. The patient-specific NTCP of the lungs, heart and esophagus was calculated with an in-house software tool using differential dose-volume histograms and the equivalent uniform dose model. The PTV dose metrics from 3D-CRT were inferior to those from IMRT and VMAT plans. The dose constraints for the OARs were met in all treatment plans. The NTCP range of the lungs, heart and esophagus was 0.34-0.49%, 0.03-0.06% and 0.08-0.10%, respectively. The NTCPs of the heart for the incidence of peridarditis from IMRT and VMAT were significantly smaller than those from conformal treatment (p < 0.05). The 7F-IMRT was significantly superior to FA-VMAT in reducing the NTCP of the lungs and the risk of pneumonitis (p = 0.001). Similar superiority of 5F-IMRT over PA-VMAT for lung protection was found (p = 0.009). The presented results may be employed in the selection of the appropriate irradiation technique for restricting the complications in the adjacent OARs.

Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions

The use of adjuvant Immune Checkpoint Inhibitors (ICI) after concurrent chemo-radiation therapy (CCRT) has become the standard of care for locally advanced non-small cell lung cancer (LA-NSCLC). However, prolonged radiotherapy regimens are known to cause radiation-induced lymphopenia (RIL), a long-neglected toxicity that has been shown to correlate with response to ICIs and survival of patients treated with adjuvant ICI after CCRT.In this study, we aim to develop a novel neural network (NN) approach that integrates patient characteristics, treatment related variables, and differential dose volume histograms (dDVH) of lung and heart to predict the incidence of RIL at the end of treatment. Multi-institutional data of 139 LA-NSCLC patients from two hospitals were collected for training and validation of our suggested model. Ensemble learning was combined with a bootstrap strategy to stabilize the model, which was evaluated internally using repeated cross validation.The performance of our proposed model was compared to conventional models using the same input features, such as Logistic Regression (LR) and Random Forests (RF), using the Area Under the Curve (AUC) of Receiver Operating Characteristics (ROC) curves. Our suggested model (AUC=0.77) outperformed the comparison models (AUC=0.72, 0.74) in terms of absolute performance, indicating that the convolutional structure of the network successfully abstracts additional information from the differential DVHs, which we studied using Gradient-weighted Class Activation Map.This study shows that clinical factors combined with dDVHs can be used to predict the risk of RIL for an individual patient and shows a path toward preventing lymphopenia using patient-specific modifications of the radiotherapy plan.

Predictive Model of Liver Toxicity to Aid the Personalized Selection of Proton Versus Photon Therapy in Hepatocellular Carcinoma

Our objective was to develop an externally validated model for predicting liver toxicity after radiation therapy in patients with hepatocellular carcinoma (HCC) that can integrate both photon and proton dose distributions with patient-specific characteristics. Training data consisted of all patients with HCC treated between 2008 and 2019 at our institution (n=117, 60%/40% photon/proton). We developed a shallow convolutional neural network (CNN) to predict posttreatment liver dysfunction from the differential dose-volume histogram (DVH) and baseline liver metrics. To reduce bias and improve robustness, we used ensemble learning (CNNE). After a preregistered study analysis plan, we evaluated stability using internal bootstrap resampling and generalizability using a data set from a different institution (n=88). Finally, we implemented a class activation map method to characterize the critical DVH subregions and benchmarked the model against logistic regression and XGBoost. The models were evaluated using the area under the receiver operating characteristic curve and area under the precision-recall curve. The CNNE model showed similar internal performance and robustness compared with the benchmarks. CNNE exceeded the benchmark models in external validation, with an area under the receiver operating characteristic curve of 0.78 versus 0.55 to 0.70, and an area under the precision-recall curve of 0.6 versus 0.43 to 0.52. The model showed improved predictive power in the photon group, excellent specificity in both modalities, and high sensitivity in the photon high-risk group. Models built solely on DVHs confirm outperformance of the CNNE and indicate that the proposed structure efficiently abstracts features from both proton and photon dose distributions. The activation map method demonstrates the importance of the low-dose bath and its interaction with low liver function at baseline. We developed and externally validated a patient-specific prediction model for hepatic toxicity based on the entire DVH and clinical factors that can integrate both photon and proton therapy cohorts. This model complements the new American Society for Radiation Oncology clinical practice guidelines and could support value-driven integration of proton therapy into the management of HCC.

Risk evaluation of secondary malignancies after radiotherapy of breast cancer in light of the continuous development of planning techniques.

Secondary cancer risk is a significant concern for women treated with breast radiation therapy due to improved long-term survival rates. We evaluated the potential of new advanced automated planning algorithms together with hybrid techniques to minimize the excess absolute risk (EAR) for secondary cancer in various organs after radiation treatment for early staged breast cancer. Using CT data set of 25 patients, we generated 4 different radiation treatment plans of different complexity, including 3-dimensional conformal radiotherapy (3D-CRT), field-in-field (FinF), hybrid-IMRT (HMRT) and automated hybrid-VMAT (HVMAT) techniques. The organ-equivalent dose (OED) was calculated from differential dose-volume histograms on the basis of the "linear-exponential," "plateau," and "full mechanistic" dose-response models and was used to evaluate the EAR for secondary cancer in the contralateral breast (CB), contralateral lung (CL), and ipsilateral lung (IL). Statistical comparisons of data were performed by a Kruskal-Wallis analysis of variance. The planning objectives were fulfilled with all the planning techniques for both target coverage and organs-at-risk sparing. The differences in EAR for CB, CL and IL secondary tumor induction were not significant among the 4 techniques. For the CB and CL, the mean absolute difference did not reach 1 case of 10000 patient-years. For the IL, the mean absolute difference was up to 5 cases of 10,000 patient-years. In conclusion, the automated HVMAT technique allows an EAR reduction at the level of well-consolidated tangential 3D-CRT or FinF techniques, keeping all the HVMAT dosimetric improvements unchanged. On the basis of this analysis, the adoption of the HVMAT technique poses no increase in EAR and could be considered safe also for younger patients.

Clinical Efficiency Gains with Artificial-Intelligence Auto-Segmentation in the Entire Human Body

<h3>Purpose/Objective(s)</h3> To evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis. <h3>Materials/Methods</h3> An FDA approved Artificial Intelligence (AI) segmentation software (Medical Mind, Inc) was clinically introduced for total body contouring in TMI. Our current workflow requires dosimetrist and physician to review and edit AI-segmentations prior to treatment planning. This work compares the original AI-segmentation with the clinically used segmentations including the spatial and dosimetric deviations, and the clinical efficiency gains. The TMI-model creates 27 organs at risk (OARs) and 4 planning target volumes (PTVs). Since introducing the TMI-model, 21 patients were treated with this workflow. The spatial deviations were estimated with conventional volume and surface measures including the Sorensen DICE coefficient and the 95^th% Hausdorff Distance (HD95). The dosimetric deviations were estimated for the entire dose volume histogram (DVH) for all structures using a threshold of 5% difference at any dose level. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. Examples of efficiency gains are: an unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface <1mm from a clinical contour has an efficiency gain = 70%. <h3>Results</h3> A total of 464 contours were evaluated. In PTVs, the percentage of contour surface deviated by >1mm in 44.3%±31.1% of all targets, an average efficiency gain of 55.6%. Deviations >5mm were detected in 20.1%±34.5% of the PTV contour surfaces. In OARs, deviations >1mm were detected in 24.2%±26.9% of the structure surfaces; an average clinical efficiency gain of 75.8%. Deviations > 5mm were detected in 7.1%±17.9% of all OAR contours. DVH differences were minimal, with 17.5% of all curves exceeding 5% deviation at any dose level. The most common 5%-DVH variations were in esophagus (67%), mandible (42.9%), kidney (23.8%), and PTVs (20.2%). In H&N OARs, efficiency gains ranged from 42.0% (optic chiasm) to 100% (eyes were unedited in all cases), with mean and standard deviation 77.3%±18.9%. In thorax OARs efficiency gains were >80% in spinal cord, heart, and both lungs, with esophagus efficiency around 40% indicating significant edits. In abdomen and pelvis OARs, efficiency gains ranged from 61.6-68.6% (stomach, rectum, bowel) and 78.3-84.8% in liver and bladder. The efficiency metric was not well correlated to DICE (r = 0.75) or negatively correlated to HD95 (r=-0.75) indicating the efficiency of AI contouring is not measured by traditional overlap methods. <h3>Conclusion</h3> The Medical Mind AI software is a powerful solution for efficiency in clinical radiation therapy with the ability to segment the entire body. Based on spatial and dosimetric comparisons, structures including esophagus and PTVs require significant human edits.

Prediction of Hepatic Toxicity after Radiotherapy Using a Neural Network Including Blood Biomarkers and Liver Dose Distributions

<h3>Purpose/Objective(s)</h3> ASTRO recently published practical guidelines for treating unresectable hepatocellular carcinoma with radiotherapy, recommending hypofractionated dose escalated regimens when tolerated by the liver. This highlights the need for patient-specific prediction models of radiation-induced hepatic toxicities, which necessitates inclusion not only of radiation dose, but also baseline liver function biomarkers. <h3>Materials/Methods</h3> We developed a shallow neural network to predict declines in Child-Pugh score (CP) of 2 or more, with a focus on robustness, interpretability and ability to include both proton and photon radiation dose distributions. The network uses 1 convolution layer to extract 8 dosimetric features that are representative of the differential DVH (dDVH) and interactions of different dose levels within, which are then combined in a fully connected layer with baseline platelet counts and liver function biomarkers—CP and albumin-bilirubin grade. After registering a study analysis plan, we trained the model on 117 patients from a single institution and independently validated it on 88 patients from another institution, where 47%/36% of the training/validation cohorts received proton radiotherapy. We compared the model to logistic regression to evaluate the importance of feature interactions on predictive power, measured by the area under the receiver operating characteristics (AUC). <h3>Results</h3> The model prediction of CP decline had a cross-validation AUC = 0.86; 95%CI: 0.69-0.97 and independent validation AUC = 0.74 with 89% accuracy in 10% high-risk group. In contrast, logistic regression showed cross-validation AUC 0.73; 95%CI: 0.56-0.88 and validation AUC 0.56, demonstrating a significant importance of feature interactions. Sensitivity analysis of the neural network revealed that patients with low baseline platelet counts were most sensitive to high doses to the normal liver compared to other patient groups, indicating a potential benefit of liver-sparing therapies in these patients. <h3>Conclusion</h3> We developed a patient-specific hepatic toxicity model that can take into account both proton and photon dose distributions and the interaction of radiation dose to normal liver with baseline liver function. Robust model performance across institutions and particularly in high-risk patients could allow stratification of patients to standard, hypofractionated regimens, or proton therapy based on a patient's risk score.

Auto-segmentation for total marrow irradiation.

PurposeTo evaluate the accuracy and efficiency of Artificial-Intelligence (AI) segmentation in Total Marrow Irradiation (TMI) including contours throughout the head and neck (H&N), thorax, abdomen, and pelvis.MethodsAn AI segmentation software was clinically introduced for total body contouring in TMI including 27 organs at risk (OARs) and 4 planning target volumes (PTVs). This work compares the clinically utilized contours to the AI-TMI contours for 21 patients. Structure and image dicom data was used to generate comparisons including volumetric, spatial, and dosimetric variations between the AI- and human-edited contour sets. Conventional volume and surface measures including the Sørensen–Dice coefficient (Dice) and the 95th% Hausdorff Distance (HD95) were used, and novel efficiency metrics were introduced. The clinical efficiency gains were estimated by the percentage of the AI-contour-surface within 1mm of the clinical contour surface. An unedited AI-contour has an efficiency gain=100%, an AI-contour with 70% of its surface<1mm from a clinical contour has an efficiency gain of 70%. The dosimetric deviations were estimated from the clinical dose distribution to compute the dose volume histogram (DVH) for all structures.ResultsA total of 467 contours were compared in the 21 patients. In PTVs, contour surfaces deviated by >1mm in 38.6% ± 23.1% of structures, an average efficiency gain of 61.4%. Deviations >5mm were detected in 12.0% ± 21.3% of the PTV contours. In OARs, deviations >1mm were detected in 24.4% ± 27.1% of the structure surfaces and >5mm in 7.2% ± 18.0%; an average clinical efficiency gain of 75.6%. In H&N OARs, efficiency gains ranged from 42% in optic chiasm to 100% in eyes (unedited in all cases). In thorax, average efficiency gains were >80% in spinal cord, heart, and both lungs. Efficiency gains ranged from 60-70% in spleen, stomach, rectum, and bowel and 75-84% in liver, kidney, and bladder. DVH differences exceeded 0.05 in 109/467 curves at any dose level. The most common 5%-DVH variations were in esophagus (86%), rectum (48%), and PTVs (22%).ConclusionsAI auto-segmentation software offers a powerful solution for enhanced efficiency in TMI treatment planning. Whole body segmentation including PTVs and normal organs was successful based on spatial and dosimetric comparison.

Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT.

Adaptive radiotherapy performed on the daily magnetic resonance imaging (MRI) is an option to improve the treatment quality. In the adapt-to-shape workflow of 1.5-T MR-Linac, the contours of structures are adjusted on the basis of patient daily MRI, and the adapted plan is recalculated on the MRI-based synthetic computed tomography (syCT) generated by bulk density assignment. Because dosimetric accuracy of this strategy is a priority and requires evaluation, this study aims to explore the usefulness of adding an assessment of dosimetric errors associated with recalculation on syCT to the clinical workflow. Sixty-one patients, with various tumor sites, treated using a 1.5-T MR-Linac were included in this study. In Monaco V5.4, the target and organs at risk (OARs) were contoured, and a reference CT plan that contains information about the outlined contours, their average electron density (ED), and the priority of ED assignment was generated. To evaluate the dosimetric error of syCT caused by the inherent approximation within bulk density assignment, the reference CT plan was recalculated on the syCT obtained from the reference CT by forcing all contoured structures to their mean ED defined on the reference plan. The dose–volume histogram (DVH) and dose distribution of the CT and syCT plan were compared. The causes of dosimetric discrepancies were investigated, and the reference plan was reworked to minimize errors if needed. For 54 patients, gamma analysis of the dose distribution on syCT and CT show a median pass rate of 99.7% and 98.5% with the criteria of 3%/3 mm and 2%/2 mm, respectively. DVH difference of targets and OARs remained less than 1.5% or 1 Gy. For the remaining patients, factors (i.e., inappropriate ED assignments) influenced the dosimetric agreement of the syCT vs. CT reference DVH by up to 21%. The causes of the errors were promptly identified, and the DVH dosimetry was realigned except for two lung treatments for which a significant discrepancy remained. The recalculation on the syCT obtained from the planning CT is a powerful tool to assess and decrease the minimal error committed during the adaptive plan on the MRI-based syCT.

Three-dimensional conformal radiotherapy (3D-CRT) vs. volumetric modulated arc therapy (VMAT) in deep inspiration breath-hold (DIBH) technique in left-sided breast cancer patients-comparative analysis of dose distribution and estimation of projected secondary cancer risk.

The purpose of this study was to compare two techniques of irradiation of left-sided breast cancer patients who underwent breast-conserving surgery, three-dimensional conformal radiotherapy technique (3D-CRT) and volumetric modulated arc therapy (VMAT), in terms of dose distribution in the planning target volume (PTV) and organs at risk (OARs). The second aim of the study was estimation of the projected risk of radiation-induced secondary cancer for both radiotherapy techniques. For 25patients who underwent CT simulation in deep inspiration breath-hold (DIBH), three treatment plans were generated: one using athree-dimensional conformal radiotherapy technique and two using volumetric modulated arc therapy. First VMAT-DIBH geometry consisted of three partial arcs (ARC-DIBH 3A) and second consisted of four partial arcs (ARC-DIBH 4A). Cumulative dose-volume histograms (DVHs) were used to compare dose distributions within the PTV and OARs (heart, left anterior descending coronary artery [LAD], ipsilateral and contralateral lung [IL, CL], and contralateral breast [CB]). Normal tissue complication probabilities (NTCPs) and organ equivalent doses (OEDs) were calculated using the differential DVHs. Excess absolute risks (EARs) for second cancers were estimated using Schneider's full mechanistic dose-response model. All plans fulfilled the criterium for PTV V95% ≥ 95%. The PTV coverage, homogeneity, and conformity indices were significantly better for VMAT-DIBH. VMAT showed asignificantly increased mean dose and V5Gy for all OARs, but reduced LAD Dmax by 15 Gy. For IL, CL, and CB, the 3D-CRT DIBH method achieved the lowest values of EAR: 28.38 per 10,000PYs, 2.55 per 10,000PYs, and 4.48 per 10,000PYs (p < 0.001), compared to 40.29 per 10,000PYs, 15.62 per 10,000PYs, and 23.44 per 10,000PYs for ARC-DIBH 3A plans and 41.12 per 10,000PYs, 15.59 per 10,000PYs, and 22.73 per 10,000PYs for ARC-DIBH 4A plans. Both techniques provided negligibly low NTCPs for all OARs. The study shows that VMAT-DIBH provides better OAR sparing against high doses. However, the large low-dose-bath (≤ 5 Gy) is still aconcern due to the fact that alarger volume of normal tissues exposed to lower doses may increase aradiation-induced risk of secondary cancer.

Feasibility of Differential Dose-Volume Histogram Features in Multivariate Prediction Model for Radiation Pneumonitis Occurrence.

: PurposeThe purpose of this study is to introduce differential dose–volume histogram (dDVH) features into machine learning for radiation pneumonitis (RP) prediction and to demonstrate the predictive performance of the developed model based on integrated cumulative dose–volume histogram (cDVH) and dDVH features. Materials and methods: cDVH and dDVH features were calculated for 153 patients treated for non-small-cell lung cancer with 60–66 Gy and dose bins ranging from 2 to 8 Gy in 2 Gy increments. RP prediction models were developed with the least absolute shrinkage and selection operator (LASSO) through fivefold cross-validation. Results: Among the 152 patients in the patient cohort, 41 presented ≥grade 2 RP. The interdependencies between cDVH features evaluated by Spearman’s correlation were significantly resolved by the inclusion of dDVH features. The average area under curve for the RP prediction model using cDVH and dDVH model was 0.73, which was higher than the average area under curve using cDVH model for 0.62 with statistically significance (p < 0.01). An analysis using the entire set of regression coefficients determined by LASSO demonstrated that dDVH features represented four of the top five frequently selected features in the model fitting, regardless of dose bin. Conclusions: We successfully developed an RP prediction model that integrated cDVH and dDVH features. The best RP prediction model was achieved using dDVH (dose bin = 4 Gy) features in the machine learning process.

Dosimetric Accuracy of MR-Guided Online Adaptive Planning for Nasopharyngeal Carcinoma Radiotherapy on 1.5 T MR-Linac.

PurposeThe aim of this study is to evaluate the dose accuracy of bulk relative electron density (rED) approach for application in 1.5 T MR-Linac and assess the reliability of this approach in the case of online adaptive MR-guided radiotherapy for nasopharyngeal carcinoma (NPC) patients.MethodsTen NPC patients formerly treated on conventional linac were included in this study, with their original planning CT and MRI collected. For each patient, structures such as the targets, organs at risk, bone, and air regions were delineated on the original CT in the Monaco system (v5.40.02). To simulate the online adaptive workflow, firstly all contours were transferred to MRI from the original CT using rigid registration in the Monaco system. Based on the structures, three different types of synthetic CT (sCT) were generated from MRI using the bulk rED assignment approach: the sCTICRU uses the rED values recommended by ICRU46, the sCTtailor uses the patient-specific mean rED values, and the sCTHomogeneity uses homogeneous water equivalent values. The same treatment plan was calculated on the three sCTs and the original CT. Dose calculation accuracy was investigated in terms of gamma analysis, point dose comparison, and dose volume histogram (DVH) parameters.ResultsGood agreement of dose distribution was observed between sCTtailor and the original CT, with a gamma passing rate (3%/3 mm) of 97.81% ± 1.06%, higher than that of sCTICRU (94.27% ± 1.48%, p = 0.005) and sCTHomogeneity (96.50% ± 1.02%, p = 0.005). For stricter criteria 1%/1 mm, gamma passing rates for plans on sCTtailor, sCTICRU, and sCTHomogeneity were 86.79% ± 4.31%, 79.81% ± 3.63%, and 77.56% ± 4.64%, respectively. The mean point dose difference in PTVnx between sCTtailor and planning CT was −0.14% ± 1.44%, much lower than that calculated on sCTICRU (−8.77% ± 2.33%) and sCTHomogeneity (1.65% ± 2.57%), all with p < 0.05. The DVH differences for the plan based on sCTtailor were much smaller than sCTICRU and sCTHomogeneity.ConclusionsThe bulk rED-assigned sCT by adopting the patient-specific rED values can achieve a clinically acceptable level of dose calculation accuracy in the presence of a 1.5 T magnetic field, making it suitable for online adaptive MR-guided radiotherapy for NPC patients.

A software tool for organ-specific second cancer risk assessment from exposure to therapeutic doses.

The aim of this study was the development of a software tool (SCRcalc) for the automatic estimation of the patient- and organ-specific cancer risk due to radiotherapy. SCRcalc was developed using the Python 3.8.7 programming language. It incorporates equations and parameters of mechanistic models for the calculation of the organ equivalent dose (OED), the excess absolute risk (EA R) and the lifetime attributable risk (LA R) of carcinogenesis for various organs due to radiotherapy. Data from differential dose-volume histograms, as defined by a treatment planning system, could be automatically inserted into the program. Eighteen different cancer risk estimates for various organs were performed of patients subjected to radiation therapy with conventional and modulated techniques. These software estimates were compared with manual calculations. SCRcalc was developed as a standalone executable program without any dependencies. It enables direct estimations of the OED and LAR for various organs at risk. An important aspect of the software is that it does not require pre-processing of the DVH data. No differences were found between the SCRcalc results and those derived from manual calculations. The newly developed software offers the possibility to medical physicists and radiation oncologists to directly estimate the probability of radiotherapy-induced secondary malignancies for various organs at risk.

Dosimetric Evaluation of 4 Field Three-Dimensional Conformal Radiation Therapy and 7 Field Intensity Modulated Radiation Therapy in Esophageal Cancer Treated with Definitive Chemoradiation

Background: The present study aimed to evaluate dosimetrically and correlate the lung and heart dose volume histogram (DVH) of the 4 Field three-dimensional conformal radiotherapy (3DCRT) with 7 field intensity modulated radiotherapy (IMRT) in patients with oesophageal cancers. Method: This retrospective dosimetric study considered 20 oesophagus cancer patients treated with definitive chemoradiation with IMRT technique. In the 7 field IMRT technique, the first phase delivered a dose of 36Gy/18fr followed by 18Gy/9fr in two weeks in the second phase. In the 3DCRT technique, the first phase was planned with 4 field technique with two parallel opposed and two posterior oblique fields, followed by the 3 field technique in the second phase. The assessments of the techniques were performed using Differential DVH analysis of the right and left lungs, heart, and the spinal cord. The values of the mean dose, V20 (Volume receiving 20 Gy), and V30 (volume receiving 30 Gy) were assessed for any correlations. Results: The DVH of V20 in IMRT showed 5 % less lung volume irradiation compared to the 3DCRT plans and over 20% less V30 for irradiated heart volume. The study demonstrated a statistical advantage of using 7 field IMRT over 4 field 3DCRT in reducing the mean percentage dose to both lungs, heart, and spinal cord. Conclusion: 7 field IMRT is superior to 4 field 3DCRT plans in significantly reducing the average percentage of irradiated volume of both the lung and heart in esophageal cancer radiation therapy.

Data-Driven Dose-Volume Histogram Prediction

PurposeTo evaluate dose-volume histogram (DVH) prediction from prior radiation therapy data. Methods and MaterialsAn Oncospace radiation therapy database was constructed including images, structures, and dose distributions for patients with advanced lung cancer. DVH data was queried for total lungs, esophagus, heart, and external body contours. Each query returned DVH data for the N-most similar organs at risk (OARs) based on OAR-to-planning-target-volume (PTV) geometry via the overlap volume histogram (OVH). The DVHs for 5, 20, and 50 of the most similar OVHs were returned for each OAR for each patient. The OVH(0cm) is the relative volume of the OAR overlapping with the PTV, and the OVH(2cm) is the relative volume of the OAR 2 cm away from the PTV. The OVH(cm) and DVH(%) queried from the database were separated into interquartile ranges (IQRs), nonoutlier ranges (NORs) (equal to 3 × IQR), and the average database DVH (DVH-DB) computed from the NOR data. The ability to predict the clinically delivered DVH was evaluated based on percentiles and differences between the DVH-DB and the clinical DVH (DVH-CL) for a varying number of returned patient DVHs for a subset of patients. ResultsThe ability to predict the clinically delivered DVH was excellent in the lungs and body; the IQR and NOR were <4% and <16%, respectively, in the lungs and <1% and <5%, respectively, in the body at all distances less than 2 cm from the PTV. For 21/23 patients considered, the differences in lung DVH-DB and DVH-CL were <4.6% and in 14/23 cases, <3%. In esophagus and heart, the ability to predict DVH-CL was weaker, with mean DVH differences >10% for 12/23 esophagi and 10/23 hearts. In esophagus and heart queries, the NOR was often 10% to 100% volume in dose ranges between 0% and 50% of prescription, independent of the number of patients queried. ConclusionsUsing prior data to predict clinical dosimetry is increasingly of interest, but model- and data-driven methods have limitations if based on limited data sets. This study's results showed that prediction may be reasonable in organs containing tumors with known overlap, but for nonoverlapped OARs, planning preference and plan design may dominate the clinical dose.

Assessment of Dose Accuracy for MR-Guided Online Adaptive Radiotherapy for Nasopharyngeal Carcinoma

MR-guided radiotherapy relies on the daily assignment of a relative electron density (rED) map to acquire the fraction specific dose calculation. The approach to acquire the rED map is used bulk rED assignment based on the rED values of the delineated region of interests (ROIs) to generate a synthetic CT (sCT). This study aims to evaluate the dose accuracy of this approach for application in 1.5 T MR-Linac, to assess the reliability of this approach in the case of online adaptive MR-guided radiotherapy for Nasopharyngeal Carcinoma (NPC) patients.The original CT and MRI T2 were acquired for 10 NPC patients and targets, organs at risk (OARs), bone and air regions were delineated on original CT in a treatment planning software. Each patient's MRI/CT image pair was aligned using a rigid registration algorithm, and the contours were copied to MRI from original CT. The sCT was then generated in MRI using bulk rED assignment approach, based on the rED values of the delineated region of interests (ROIs). Three different types of sCT images were generated: The sCTICRU uses the rED values recommended by ICRU46, the sCTtailor uses the patient-specific mean rED values, and the sCTHomogeneity was created using water equivalent value of 1 g/cm3 for entire patient data set. The same treatment plan was calculated on three sCTs and original CT. The dose calculation accuracy was investigated in terms of gamma analysis, point dose comparison and dose volume histogram (DVH) parameters.For both criteria, 1%/1mm and 2%/2mm gamma passing rate results of plans on sCTtailor were always higher than plans on sCTICRU and sCTHomogeneity when compared with original CT based plans (P = 0.005). The difference on point dose in high dose planning target volume for each case calculated on sCTtailor (-0.14% ± 1.44%) was smaller if compared to those calculated on sCTICRU (-8.77% ± 2.33%) and sCTHomogeneity (1.65% ± 2.57%). The DVH differences for plan based on sCTtailor were much smaller than sCTICRU and sCTHomogeneity.The sCT generated with bulk rED assignment approach guarantees an acceptable level of dose accuracy for NPC patients with 1.5 T MR-Linac, making this approach suitable to online adaptive MR-guided radiotherapy. The accuracy can be improved by using patient-specific rED values.

Combined effect of dose gradient and rotational error on prescribed dose coverage for single isocenter multiple brain metastases in frameless stereotactic radiotherapy

BackgroundTo evaluate the combined effect of rotational error and dose gradient on target dose coverage in frameless stereotactic radiotherapy.MethodsThree spherical targets of different diameters (1, 1.5, and 2 cm) were drawn and placed equidistantly on the same axial brain computed tomography (CT) images. To test the different isocenter-target distances, 2.5- and 5-cm configurations were prepared. Volumetric modulated arc therapy plans were created for different dose gradients from the target, in which the dose gradients were modified using the maximum dose inside the target. To simulate the rotational error, CT images and targets were rotated in two ways by 0.5°, 1°, and 2°, in which one rotation was in the axial plane and the other was in three dimensions. The initial optimized plan parameters were copied to the rotated CT sets, and the doses were recalculated. The coverage degradation after rotation was analyzed according to the target dislocation and 12-Gy volume.ResultsA shallower dose gradient reduced the loss of target coverage under target dislocation, and the effect was clearer for small targets. For example, the coverage of the 1-cm target under 1-mm dislocation increased from 93 to 95% by increasing the Paddick gradient index from 5.0 to 7.9. At the same time, the widely accepted necrosis indicator, the 12-Gy volume, increased from 1.2 to 3.5 cm3, which remained in the tolerable range. From the differential dose volume histogram (DVH) analysis, the shallower dose gradient ensured that the dose-deficient under-covered target volume received a higher dose similar to that in the prescription.ConclusionsFor frameless stereotactic brain radiotherapy, the gradient, alongside the margin addition, can be adjusted as an ancillary parameter for small targets to increase target coverage or at least limit coverage reduction in conditions with probable positioning error.