Geometric factors influencing dosimetric sparing of the parotid glands using IMRT

Analyzing the Relationship between Dose and Geometric Agreement Metrics for Auto-Contouring in Head and Neck Normal Tissues

This study aimed to determine the relationship between geometric and dosimetric agreement metrics in head and neck (H&N) cancer radiotherapy plans. A total 287 plans were retrospectively analyzed, comparing auto-contoured and clinically used contours using a Dice similarity coefficient (DSC), surface DSC (sDSC), and Hausdorff distance (HD). Organs-at-risk (OARs) with ≥200 cGy dose differences from the clinical contour in terms of Dmax (D0.01cc) and Dmean were further examined against proximity to the planning target volume (PTV). A secondary set of 91 plans from multiple institutions validated these findings. For 4995 contour pairs across 19 OARs, 90% had a DSC, sDSC, and HD of at least 0.75, 0.86, and less than 7.65 mm, respectively. Dosimetrically, the absolute difference between the two contour sets was <200 cGy for 95% of OARs in terms of Dmax and 96% in terms of Dmean. In total, 97% of OARs exhibiting significant dose differences between the clinically edited contour and auto-contour were within 2.5 cm PTV regardless of geometric agreement. There was an approximately linear trend between geometric agreement and identifying at least 200 cGy dose differences, with higher geometric agreement corresponding to a lower fraction of cases being identified. Analysis of the secondary dataset validated these findings. Geometric indices are approximate indicators of contour quality and identify contours exhibiting significant dosimetric discordance. For a small subset of OARs within 2.5 cm of the PTV, geometric agreement metrics can be misleading in terms of contour quality.

Development of Prediction Model for Mean Parotid Dose of HNC Undergoing Radiotherapy - A Single Institutional Study

Aim

The aim of the study was to develop a simple prediction model based on previous treatment plans for head-and-neck cancer (HNC).

Materials and Methods

This study was conducted on 95 patients who underwent volumetric-modulated arc therapy (VMAT) with curative intent for HNC at our institute between January 2016 and December 2022 with intact bilateral parotid glands. Two simple prediction models were used: one linear regression model and one exponential model. Both models use fractional overlapping parotid volume with planning target volume (PTV) as a predictor of mean parotid dose. The fractional overlapping volume was calculated as the difference between the volume of the parotid gland minus the volume of the parotid gland outside the PTV plus a 2 mm margin, divided by the volume of the parotid gland. Statistical calculations were done using data analysis tools and Solver in Microsoft Excel (Microsoft Office 2013, Redmond, WA, USA). To enhance the accuracy of the results, outliers were excluded with residuals >2 standard deviations below and above the residuals. R2 and root-mean-square error were calculated for both models to evaluate the quality of the predictions. The normality of both models' residuals was validated using the Shapiro-Wilk test.

Results

Both linear and exponential prediction models exhibited strong correlation statistics, with r2 = 0.85 and 0.82, respectively. The authors found a fractional overlap of 16.4% and 18.9% in linear and exponential models that predict parotid mean dose 26 Gy. The implementation was carried out on a cohort of 12 prospective patients, demonstrating a remarkable improvement in minimizing the dose to the parotid glands.

Conclusion

In this single-institutional study, the authors successfully developed a prediction model for mean parotid dose in HNC patients undergoing radiotherapy. The model showed promising accuracy and has the potential to assist planners in optimizing treatment plans and minimizing radiation-related toxicity. It is possible to avoid under sparing the organs at risks in some cases and wasting time or effort on physically impossible goals in others using this prediction model. As a result, planning resources can be used much more efficiently. Future studies should focus on validating the model's performance using external datasets and exploring its integration into clinical practice.

Automated tracking of morphologic changes in weekly magnetic resonance imaging during head and neck radiotherapy

BACKGROUND AND PURPOSE

Anatomic changes during head and neck radiotherapy can impact dose delivery, necessitate adaptive replanning, and indicate patient-specific response to treatment. We have developed an automated system to track these changes through longitudinal MRI scans to aid identification and clinical intervention. The purpose of this article is to describe this tracking system and present results from an initial cohort of patients.

MATERIALS AND METHODS

The Automated Watchdog in Adaptive Radiotherapy Environment (AWARE) was developed to process longitudinal MRI data for radiotherapy patients. AWARE automatically identifies and collects weekly scans, propagates radiotherapy planning structures, computes structure changes over time, and reports important trends to the clinical team. AWARE also incorporates manual structure review and revision from clinical experts and dynamically updates tracking statistics when necessary. AWARE was applied to patients receiving weekly T2-weighted MRI scans during head and neck radiotherapy. Changes in nodal gross tumor volume (GTV) and parotid gland delineations were tracked over time to assess changes during treatment and identify early indicators of treatment response.

RESULTS

N = 91 patients were tracked and analyzed in this study. Nodal GTVs and parotids both shrunk considerably throughout treatment (-9.7 ± 7.7% and -3.7 ± 3.3% per week, respectively). Ipsilateral parotids shrunk significantly faster than contralateral (-4.3 ± 3.1% vs. -2.9 ± 3.3% per week, p = 0.005) and increased in distance from GTVs over time (+2.7 ± 7.2% per week, p < 1 × 10^-5 ). Automatic structure propagations agreed well with manual revisions (Dice = 0.88 ± 0.09 for parotids and 0.80 ± 0.15 for GTVs), but for GTVs the agreement degraded 4-5 weeks after the start of treatment. Changes in GTV volume observed by AWARE as early as one week into treatment were predictive of large changes later in the course (AUC = 0.79).

CONCLUSION

AWARE automatically identified longitudinal changes in GTV and parotid volumes during radiotherapy. Results suggest that this system may be useful for identifying rapidly responding patients as early as one week into treatment.

Function-Wise Dual-Omics analysis for radiation pneumonitis prediction in lung cancer patients

Purpose: This study investigates the impact of lung function on radiation pneumonitis prediction using a dual-omics analysis method.

Methods: We retrospectively collected data of 126 stage III lung cancer patients treated with chemo-radiotherapy using intensity-modulated radiotherapy, including pre-treatment planning CT images, radiotherapy dose distribution, and contours of organs and structures. Lung perfusion functional images were generated using a previously developed deep learning method. The whole lung (WL) volume was divided into function-wise lung (FWL) regions based on the lung perfusion functional images. A total of 5,474 radiomics features and 213 dose features (including dosiomics features and dose-volume histogram factors) were extracted from the FWL and WL regions, respectively. The radiomics features (R), dose features (D), and combined dual-omics features (RD) were used for the analysis in each lung region of WL and FWL, labeled as WL-R, WL-D, WL-RD, FWL-R, FWL-D, and FWL-RD. The feature selection was carried out using ANOVA, followed by a statistical F-test and Pearson correlation test. Thirty times train-test splits were used to evaluate the predictability of each group. The overall average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and f1-score were calculated to assess the performance of each group.

Results: The FWL-RD achieved a significantly higher average AUC than the WL-RD group in the training (FWL-RD: 0.927 ± 0.031, WL-RD: 0.849 ± 0.064) and testing cohorts (FWL-RD: 0.885 ± 0.028, WL-RD: 0.762 ± 0.053, p < 0.001). When using radiomics features only, the FWL-R group yielded a better classification result than the model trained with WL-R features in the training (FWL-R: 0.919 ± 0.036, WL-R: 0.820 ± 0.052) and testing cohorts (FWL-R: 0.862 ± 0.028, WL-R: 0.750 ± 0.057, p < 0.001). The FWL-D group obtained an average AUC of 0.782 ± 0.032, obtaining a better classification performance than the WL-D feature-based model of 0.740 ± 0.028 in the training cohort, while no significant difference was observed in the testing cohort (FWL-D: 0.725 ± 0.064, WL-D: 0.710 ± 0.068, p = 0.54).

Conclusion: The dual-omics features from different lung functional regions can improve the prediction of radiation pneumonitis for lung cancer patients under IMRT treatment. This function-wise dual-omics analysis method holds great promise to improve the prediction of radiation pneumonitis for lung cancer patients.

Sharp loss: a new loss function for radiotherapy dose prediction based on fully convolutional networks

Background

Neural-network methods have been widely used for the prediction of dose distributions in radiotherapy. However, the prediction accuracy of existing methods may be degraded by the problem of dose imbalance. In this work, a new loss function is proposed to alleviate the dose imbalance and achieve more accurate prediction results. The U-Net architecture was employed to build a prediction model. Our study involved a total of 110 patients with left-breast cancer, who were previously treated by volumetric-modulated arc radiotherapy. The patient dataset was divided into training and test subsets of 100 and 10 cases, respectively. We proposed a novel ‘sharp loss’ function, and a parameter γ was used to adjust the loss properties. The mean square error (MSE) loss and the sharp loss with different γ values were tested and compared using the Wilcoxon signed-rank test.

Results

The sharp loss achieved superior dose prediction results compared to those of the MSE loss. The best performance with the MSE loss and the sharp loss was obtained when the parameter γ was set to 100. Specifically, the mean absolute difference values for the planning target volume were 318.87 ± 30.23 for the MSE loss versus 144.15 ± 16.27 for the sharp loss with γ = 100 (p < 0.05). The corresponding values for the ipsilateral lung, the heart, the contralateral lung, and the spinal cord were 278.99 ± 51.68 versus 198.75 ± 61.38 (p < 0.05), 216.99 ± 44.13 versus 144.86 ± 43.98 (p < 0.05), 125.96 ± 66.76 versus 111.86 ± 47.19 (p > 0.05), and 194.30 ± 14.51 versus 168.58 ± 25.97 (p < 0.05), respectively.

Conclusions

The sharp loss function could significantly improve the accuracy of radiotherapy dose prediction.

Knowledge-based radiation treatment planning: A data-driven method survey

This paper surveys the data-driven dose prediction methods investigated for knowledge-based planning (KBP) in the last decade. These methods were classified into two major categories-traditional KBP methods and deep-learning (DL) methods-according to their techniques of utilizing previous knowledge. Traditional KBP methods include studies that require geometric or anatomical features to either find the best-matched case(s) from a repository of prior treatment plans or to build dose prediction models. DL methods include studies that train neural networks to make dose predictions. A comprehensive review of each category is presented, highlighting key features, methods, and their advancements over the years. We separated the cited works according to the framework and cancer site in each category. Finally, we briefly discuss the performance of both traditional KBP methods and DL methods, then discuss future trends of both data-driven KBP methods to dose prediction.

Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer

Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk (p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model (p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs.

Mean parotid dose prediction model using machine learning regression method for intensity-modulated radiotherapy in head and neck cancer

Parotids are considered one of the major organs at risk in Head and Neck (HN) intensity-modulated radiotherapy (IMRT). Achieving proper target coverage with reduced mean parotid dose demands an elaborate time-consuming IMRT plan optimization. A parotid mean dose prediction model based on a machine-learning linear regression was developed and validated in this study. The model was developed using independent variables, such as parotid to PTV overlapping volume, dose coverage of the overlapping PTV, the ratio of overlapping parotid volume to total parotid volume, and volume of parotid overlapping with isotopically expanded PTV contours. The Pearson correlation coefficients between these independent variables and the mean parotid dose were calculated. Multicollinearity of the independent variables was checked by calculating the Variance Inflation Factor (VIF). All variables are having VIF less than ten were taken for the model. Fifty IMRT patient plans were used to develop the model. The mean parotid dose predicted by the model was in good agreement with the obtained mean parotid dose. The model is having a Root Mean Square Error (RMSE) of 2.89 Gy and an R-square of 0.7695. The model was successfully validated using the fivefold cross-validation method, resulting R-square value of 0.6179 and an RMSE of 2.93 Gy. The normality of the model's residuals was tested using Quartile-Quartile (Q-Q) plot and Shapiro Wilk test (p = 0.996, for null hypothesis ``residuals were normally distributed''). The data points in the Q-Q plot are falling approximately along the reference line. This model can be used in clinics to help the planner in the preplanning phase for efficient plan optimization.

Incorporating parotid gland inhomogeneity into head-and-neck treatment optimization through the use of artificial base plans

Despite a great improvement in target volume dose conformality made possible in recent years by modulated therapies, xerostomia remains a common and severe side effect for head-and-neck radiotherapy patients. It is known that parotid glands exhibit a spatially varying dose response; however, the relative importance of subregions throughout the entire gland has yet to be incorporated into treatment plan optimization, with the current standard being to minimize the mean dose to whole parotid glands. The relative importance of regions within contralateral parotid glands has been recently quantified, creating an opportunity for the development of a method for including this data in plan optimization. We present a universal and straightforward approach for imposing varying sub-parotid gland dose constraints during inverse treatment planning by using patient-specific artificial base plans to penalize dose deposited in sensitive regions. In this work, the proposed method of optimization is demonstrated to reduce dose to regions of high relative importance throughout contralateral parotids and improve predictions for stimulated saliva output at 1-year post-radiotherapy. This method may also be applied to impose varying dose constraints to other organs-at-risk for which regional importance data exists.

Characterization of knowledge-based volumetric modulated arc therapy plans created by three different institutions' models for prostate cancer

Background

The aim of this study was to clarify factors predicting the performance of knowledge-based planning (KBP) models in volume modulated arc therapy for prostate cancer in terms of sparing the organ at risk (OAR).

Materials and methods

In three institutions, each KBP model was trained by more than 20 library plans (LP) per model. To validate the characterization of each KBP model, 45 validation plans (VP) were calculated by the KBP system. The ratios of overlap between the OAR volume and the planning target volume (PTV) to the whole organ volume (V_overlap/V_whole) were analyzed for each LP and VP. Regression lines between dose-volume parameters (V₉₀, V₇₅, and V₅₀) and V_overlap/V_whole were evaluated. The mean OAR dose, V₉₀, V₇₅, and V₅₀ of LP did not necessarily match those of VP.

Results

In both the rectum and bladder, the dose-volume parameters for VP were strongly correlated with V_overlap/V_whole at institutes A, B, and C (R > 0.74, 0.85, and 0.56, respectively). Except in the rectum at institute B, the slopes of the regression lines for LP corresponded to those for VP. For dose-volume parameters for the rectum, the ratios of slopes of the regression lines in VP to those in LP ranged 0.51-1.26. In the bladder, most ratios were less than 1.0 (mean: 0.77).

Conclusion

For each OAR, each model made distinct dosimetric characterizations in terms of V_overlap/V_whole. The relationship between dose-volume parameters and V_overlap/V_whole of OARs in LP predicts the KBP models' performance sparing OARs.

Implementation of an optimization method for parotid gland sparing during inverse planning for head and neck cancer radiotherapy

PURPOSE

To guide parotid gland (PG) sparing at the dose planning step, a specific model based on overlap between PTV and organ at risk (Moore et al.) was developed and evaluated for VMAT in head-and-neck (H&N) cancer radiotherapy.

MATERIALS AND METHODS

One hundred and sixty patients treated for locally advanced H&N cancer were included. A model optimization was first performed (20 patients) before a model evaluation (110 patients). Thirty cases were planned with and without the model to quantify the PG dose sparing. The inter-operator variability was evaluated on one case, planned by 12 operators with and without the model. The endpoints were PG mean dose (D_mean), PTV homogeneity and number of monitor units (MU).

RESULTS

The PG D_mean predicted by the model was reached in 89% of cases. Using the model significantly reduced the PG D_mean: -6.1±4.3Gy. Plans with the model showed lower PTV dose homogeneity and more MUs (+10.5% on average). For the inter-operator variability, PG dose volume histograms without the optimized model were significantly different compared to those with the model; the D_mean standard deviation for the ipsilateral PG decreased from 2.2Gy to 1.2Gy. For the contralateral PG, this value decreased from 2.9Gy to 0.8Gy.

CONCLUSION

During the H&N inverse planning, the optimized model guides to the lowest PG achievable mean dose, allowing a significant PG mean dose reduction of -6.1Gy. Integrating this method at the treatment-planning step significantly reduced the inter-patient and inter-operator variabilities.

Validation of in-house knowledge-based planning model for advance-stage lung cancer patients treated using VMAT radiotherapy

OBJECTIVES

Radiotherapy plan quality may vary considerably depending on planner's experience and time constraints. The variability in treatment plans can be assessed by calculating the difference between achieved and the optimal dose distribution. The achieved treatment plans may still be suboptimal if there is further scope to reduce organs-at-risk doses without compromising target coverage and deliverability. This study aims to develop a knowledge-based planning (KBP) model to reduce variability of volumetric modulated arc therapy (VMAT) lung plans by predicting minimum achievable lung volume-dose metrics.

METHODS

Dosimetric and geometric data collected from 40 retrospective plans were used to develop KBP models aiming to predict the minimum achievable lung dose metrics via calculating the ratio of the residual lung volume to the total lung volume. Model accuracy was verified by replanning 40 plans. Plan complexity metrics were calculated using locally developed script and their effect on treatment delivery was assessed via measurement.

RESULTS

The use of KBP resulted in significant reduction in plan variability in all three studied dosimetric parameters V5, V20 and mean lung dose by 4.9% (p = 0.007, 10.8 to 5.9%), 1.3% (p = 0.038, 4.0 to 2.7%) and 0.9 Gy (p = 0.012, 2.5 to 1.6Gy), respectively. It also increased lung sparing without compromising the overall plan quality. The accuracy of the model was proven as clinically acceptable. Plan complexity increased compared to original plans; however, the implication on delivery errors was clinically insignificant as demonstrated by plan verification measurements.

CONCLUSION

Our in-house model for VMAT lung plans led to a significant reduction in plan variability with concurrent decrease in lung dose. Our study also demonstrated that treatment delivery verifications are important prior to clinical implementation of KBP models.

ADVANCES IN KNOWLEDGE

In-house KBP models can predict minimum achievable lung dose-volume constraints for advance-stage lung cancer patients treated with VMAT. The study demonstrates that plan complexity could increase and should be assessed prior to clinical implementation.

A Model-Based Method for Assessment of Salivary Gland and Planning Target Volume Dosimetry in Volumetric-Modulated Arc Therapy Planning on Head-and-Neck Cancer

This study examined the relationship of achievable mean dose and percent volumetric overlap of salivary gland with the planning target volume (PTV) in volumetric-modulated arc therapy (VMAT) plan in radiotherapy for a patient with head-and-neck cancer. The aim was to develop a model to predict the viability of planning objectives for both PTV coverage and organs-at-risk (OAR) sparing based on overlap volumes between PTVs and OARs, before the planning process. Forty patients with head-and-neck cancer were selected for this retrospective plan analysis. The patients were treated using 6 MV photons with 2-arc VMAT plan in prescriptions with simultaneous integrated boost in dose of 70 Gy, 63 Gy, and 58.1 Gy to primary tumor sites, high-risk nodal regions, and low-risk nodal regions, respectively, over 35 fractions. A VMAT plan was generated using Varian Eclipse (V13.6), in optimization with biological-based generalized equivalent uniform dose (gEUD) objective for OARs and targets. Target dose coverage (D₉₅, D_max, conformity index) and salivary gland dose (D_mean and D_max) were evaluated in those plans. With a range of volume overlaps between salivary glands and PTVs and dose constraints applied, results showed that dose D₉₅ for each PTV was adequate to satisfy D₉₅ >95% of the prescription. Mean dose to parotid <26 Gy could be achieved with <20% volumetric overlap with PTV₅₈ (parotid-PTV₅₈). On an average, the D_mean was seen at 15.6 Gy, 21.1 Gy, and 24.2 Gy for the parotid-PTV₅₈ volume at <5%, <10%, and <20%, respectively. For submandibular glands (SMGs), an average D_mean of 27.6 Gy was achieved in patients having <10% overlap with PTV₅₈, and 36.1 Gy when <20% overlap. Mean doses on parotid and SMG were linearly correlated with overlap volume (regression R² = 0.95 and 0.98, respectively), which were statistically significant (P < 0.0001). This linear relationship suggests that the assessment of the structural overlap might provide prospective for achievable planning objectives in the head-and-neck plan.

Three IMRT advanced planning tools: A multi-institutional side-by-side comparison

Purpose

To assess three advanced radiation therapy treatment planning tools on the intensity‐modulated radiation therapy (IMRT) quality and consistency when compared to the clinically approved plans, referred as manual plans, which were planned without using any of these advanced planning tools.

Materials and Methods

Three advanced radiation therapy treatment planning tools, including auto‐planning, knowledge‐based planning, and multiple criteria optimization, were assessed on 20 previously treated clinical cases. Three institutions participated in this study, each with expertise in one of these tools. The twenty cases were retrospectively selected from Cleveland Clinic, including five head‐and‐neck (HN) cases, five brain cases, five prostate with pelvic lymph nodes cases, and five spine cases. A set of general planning objectives and organs‐at‐risk (OAR) dose constraints for each disease site from Cleveland Clinic was shared with other two institutions. A total of 60 IMRT research plans (20 from each institution) were designed with the same beam configuration as in the respective manual plans. For each disease site, detailed isodoseline distributions and dose volume histograms for a randomly selected representative case were compared among the three research plans and manual plan. In addition, dosimetric endpoints of five cases for each site were compared.

Results

Compared to the manual plans, the research plans using advanced tools showed substantial improvement for the HN patient cases, including the maximum dose to the spinal cord and brainstem and mean dose to the parotid glands. For the brain, prostate, and spine cases, the four types of plans were comparable based on dosimetric endpoint comparisons.

Conclusion

With minimal planner interventions, advanced treatment planning tools are clinically useful, producing a plan quality similarly to or better than manual plans, improving plan consistency. For difficult cases such as HN cancer, advanced planning tools can further reduce radiation doses to numerous OARs while delivering adequate dose to the tumor targets.

Evaluation of auto-planning in IMRT and VMAT for head and neck cancer

PURPOSE

The purposes of this work are to (a) investigate whether the use of auto-planning and multiple iterations improves quality of head and neck (HN) radiotherapy plans; (b) determine whether delivery methods such as step-and-shoot (SS) and volumetric modulated arc therapy (VMAT) impact plan quality; (c) report on the observations of plan quality predictions of a commercial feasibility tool.

MATERIALS AND METHODS

Twenty HN cases were retrospectively selected from our clinical database for this study. The first ten plans were used to test setting up planning goals and other optimization parameters in the auto-planning module. Subsequently, the other ten plans were replanned with auto-planning using step-and-shoot (AP-SS) and VMAT (AP-VMAT) delivery methods. Dosimetric endpoints were compared between the clinical plans and the corresponding AP-SS and AP-VMAT plans. Finally, predicted dosimetric endpoints from a commercial program were assessed.

RESULTS

All AP-SS and AP-VMAT plans met the clinical dose constraints. With auto-planning, the dose coverage of the low dose planning target volume (PTV) was improved while the dose coverage of the high dose PTV was maintained. Compared to the clinical plans, the doses to critical organs, such as the brainstem, parotid, larynx, esophagus, and oral cavity were significantly reduced in the AP-VMAT (P < 0.05); the AP-SS plans had similar homogeneity indices (HI) and conformality indices (CI) and the AP-VMAT plans had comparable HI and improved CI. Good agreement in dosimetric endpoints between predictions and AP-VMAT plans were observed in five of seven critical organs.

CONCLUSION

With improved planning quality and efficiency, auto-planning module is an effective tool to enable planners to generate HN IMRT plans that are meeting institution specific planning protocols. DVH prediction is feasible in improving workflow and plan quality.

Comparison of 2 methods for prediction of liver dosimetric indices in hepatocellular cancer IMRT planning

To evaluate the performance of 2 methods predicting liver dosimetric indices for hepatocellular cancer patients treated with intensity modulated radiation therapy (IMRT). Two predicting methods were implemented to build correlation between geometric and dosimetric information of hepatocellular cancer IMRT plans. One method used Principle Component Analysis method to simplify information and Support Vector Machine (SVM) regression method to build correlation. The other method used a simple linear function to map volumes of certain regions to dosimetic indices. Thirty eight hepatocellular cancer IMRT plans were randomly selected to train the 2 methods. The effectiveness of the 2 methods was validated using another 8 plans. Liver dosimetric indices V₁₀, V₂₀, V₃₀, and mean dose of the 8 plans in validation cohort were calculated using the predicting methods. The predicted indices were compared with the indices derived from the clinically accepted treatment plans. The absolute differences of V₁₀ calculated with the SVM method and the actual values of treatment plans had a mean value of 7.87%. And the values for V₂₀, V₃₀, and mean dose were 4.37%, 4.41%, and 4.20%, respectively. The absolute differences of V₁₀ calculated with the linear formulation method and the actual values of treatment plans had a mean value of 6.09%. And the values for V₂₀, V₃₀, and mean dose were 5.28%, 5.05%, and 5.92%, respectively. These 2 methods have similar accuracy in predicting dosimetric indices V₁₀, V₂₀, V₃₀ of livers. The predicting of V₃₀ and V₂₀ is more accurate than the predicting of V₁₀. The SVM method is more accurate in predicting mean liver dose.

Dosimetric comparison between three-dimensional conformal radiotherapy (3D-CRT) and intensity-modulated radiotherapy (IMRT) in the treatment of different stages of nasopharyngeal carcinoma

Background

This work aims to compare the dosimetric performance of three-dimensional conformal radiotherapy (3D-CRT), a relatively available technique in developing countries, to intensity-modulated radiotherapy (IMRT) in the treatment of different stages of nasopharyngeal carcinoma (NPC).

Materials and Methods

According to the diagnostic stages, 40 NPC patients were divided into two equal groups. Three planning techniques such as 3D-CRT, seven-field IMRT (7F-IMRT) and nine-field IMRT (9F-IMRT) were compared. Dose prescriptions of 70 and 66 Gy were delivered in 35 fractions to gross planning target volume (PTV1) and bilateral retropharyngeal carcinoma (PTV2), respectively.

Results

Stage I dose data for almost all of the three investigated planning techniques obey the international recommendations. The dose delivered to PTV1 and PTV2 for 3D-CRT and 7F-IMRT are statistically similar, whereas 9F-IMRT is significantly better than 3D-CRT. For organs at risk (OARs), the delivered dose is significantly better for 9F-IMRT compared with the other two techniques, whereas 7F-IMRT is significantly better than 3D-CRT.

Conclusions

3D-CRT is an acceptable alternative treatment technique for stage I NPC patients in developing countries suffering from the lack of advanced radiotherapy treatment techniques. 3D-CRT and 7F-IMRT have comparable performance in PTVs, while 9F-IMRT is superior in PTVs and OARs.

Predicting the dose absorbed by organs at risk during intensity modulated radiation therapy for nasopharyngeal carcinoma

OBJECTIVE:

To develop a model for predicting the dose absorbed by organ at risk (OAR) during intensity modulated radiation therapy for nasopharyngeal carcinoma (NPC).

METHODS:

55 patients underwent intensity modulated radiation therapy for NPC. The OARs were divided into several suborgans, and SPSS software was used to evaluate multiple linear method for fitting the normalized volume for each suborgan, normalized mean dose (D_mean) D_nm (D_nm = D_mean/D_prescription), and normalized D_10%-D_100% values D_n10%-n100%(D_n10%-_n100% = D_10%-D_100%/D_prescription) for each OAR. Based on the Matlab software, the predicted D_n10%-n100% value was fitted to obtain the predicted DVH curve.

RESULTS:

The multiple linear fitting formulas revealed significant results for the oral cavity D_n100% (p = 0.017), the parotid gland D_n100% (p = 0.001), and the remaining OAR (all p < 0.0001). The correlation coefficients and p values indicated that the fitting formula was a good fit. The p values for the White test show that the prediction model is robust. This method was successfully used for verification cases.

CONCLUSION:

The present study provided a simple and effective model for predicting the dose absorbed by OAR for NPC.

ADVANCES IN KNOWLEDGE:

This method is a relatively simple mathematical model, just use prescription dose and V₀-V_n to predict the D_mean and D_10%-100%, which predict does not require buying new modules of treatment planning software or extracting the distance of each sampling point of the OAR with the dose information.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R2  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region

Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published results and within the range of the IOV. While dosimetric differences could be as large as 23% of the clinical goal, treatment plans fulfilled all imposed clinical goals for the gold standard OARs. Correlations between geometric and dosimetric measures were low with R²  <  0.5. The geometric accuracy and the ability to achieve clinically acceptable treatment plans indicate the suitability of using atlas-based contours for RT treatment planning purposes. The low correlations between geometric and dosimetric measures suggest that geometric measures alone are not sufficient to predict the dosimetric impact of segmentation inaccuracies on treatment planning for the data utilised in this study.

A quality control method for intensity-modulated radiation therapy planning based on generalized equivalent uniform dose

To ensure good quality intensity-modulated radiation therapy (IMRT) planning, we proposed the use of a quality control method based on generalized equivalent uniform dose (gEUD) that predicts absorbed radiation doses in organs at risk (OAR). We conducted a retrospective analysis of patients who underwent IMRT for the treatment of cervical carcinoma, nasopharyngeal carcinoma (NPC), or non-small cell lung cancer (NSCLC). IMRT plans were randomly divided into data acquisition and data verification groups. OAR in the data acquisition group for cervical carcinoma and NPC were further classified as sub-organs at risk (sOAR). The normalized volume of sOAR and normalized gEUD (a = 1) were analyzed using multiple linear regression to establish a fitting formula. For NSCLC, the normalized intersection volume of the planning target volume (PTV) and lung, the maximum diameter of the PTV (left-right, anterior-posterior, and superior-inferior), and the normalized gEUD (a = 1) were analyzed using multiple linear regression to establish a fitting formula for the lung gEUD (a = 1). The r-squared and P values indicated that the fitting formula was a good fit. In the data verification group, IMRT plans verified the accuracy of the fitting formula, and compared the gEUD (a = 1) for each OAR between the subjective method and the gEUD-based method. In conclusion, the gEUD-based method can be used effectively for quality control and can reduce the influence of subjective factors on IMRT planning optimization.

A model for predicting the dose to the parotid glands based on their relative overlapping with planning target volumes during helical radiotherapy

The sparing of the parotid glands in the treatment of head and neck cancers is of clinical relevance as high doses to the salivary glands may result in xerostomia. Xerostomia is a major cause of decreased quality of life for head and neck patients. This paper explores the relationship between the overlap of the target volumes and their expansions with the parotid glands for helical delivery plans and their ability to be spared. Various overlapping volumes were examined, and an overlap with a high statistical relevance was found. A model that predicts exceeding tolerance parotid mean dose based on its fractional overlapping volume with PTVs was developed. A fractional overlapping volume of 0.083 between the parotid gland and the high dose PTV plus 5 mm expansion – was determined to be the threshold value to predict parotid D_mean > 26 Gy for parotids that overlap with the high dose PTV plus 5 mm expansion. If the parotid gland only overlaps with the intermediate dose target (and/or low dose target) and the overlapping volume of the parotid gland and the intermediate dose target is less than 25%, the parotid mean dose is likely less than 26 Gy. If the parotid overlaps with the low dose target only then the mean dose to the parotid is likely to be less than 26 Gy. This finding will prove as a very useful guide for the physicians and planners involved in the planning process to know prior whether the parotid glands will be able to be spared with the current set of target volumes or if revisions are necessary. This work will serve as a helpful guide in the planning process of head and neck target cases.

Method of predicting the mean lung dose based on a patient׳s anatomy and dose-volume histograms

The aim of this study was to propose a method to predict the minimum achievable mean lung dose (MLD) and corresponding dosimetric parameters for organs-at-risk (OAR) based on individual patient anatomy. For each patient, the dose for 36 equidistant individual multileaf collimator shaped fields in the treatment planning system (TPS) was calculated. Based on these dose matrices, the MLD for each patient was predicted by the homemade DosePredictor software in which the solution of linear equations was implemented. The software prediction results were validated based on 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans previously prepared for 16 patients with stage III non-small-cell lung cancer (NSCLC). For each patient, dosimetric parameters derived from plans and the results calculated by DosePredictor were compared. The MLD, the maximum dose to the spinal cord (D_{max cord}) and the mean esophageal dose (MED) were analyzed. There was a strong correlation between the MLD calculated by the DosePredictor and those obtained in treatment plans regardless of the technique used. The correlation coefficient was 0.96 for both 3D-CRT and VMAT techniques. In a similar manner, MED correlations of 0.98 and 0.96 were obtained for 3D-CRT and VMAT plans, respectively. The maximum dose to the spinal cord was not predicted very well. The correlation coefficient was 0.30 and 0.61 for 3D-CRT and VMAT, respectively. The presented method allows us to predict the minimum MLD and corresponding dosimetric parameters to OARs without the necessity of plan preparation. The method can serve as a guide during the treatment planning process, for example, as initial constraints in VMAT optimization. It allows the probability of lung pneumonitis to be predicted.

Geometric dose prediction model for hemithoracic intensity-modulated radiation therapy in mesothelioma patients with two intact lungs

The presence of two intact lungs makes it challenging to reach a tumoricidal dose with hemithoracic pleural intensity-modulated radiation therapy (IMRT) in patients with malignant pleural mesothelioma (MPM) who underwent pleurectomy/decortications or have unresectable disease. We developed an anatomy-based model to predict attainable prescription dose before starting optimization. Fifty-six clinically delivered IMRT plans were analyzed regarding correlation of prescription dose and individual and total lung volumes, planning target volume (PTV), ipsilateral normal lung volume and ratios: contralateral/ipsilateral lung (CIVR); contralateral lung/PTV (CPVR); ipsilateral lung /PTV (IPVR); ipsilateral normal lung /total lung (INTLVR); ipsilateral normal lung/PTV (INLPVR). Spearman's rank correlation and Fisher's exact test were used. Correlation between mean ipsilateral lung dose (MILD) and these volume ratios and between prescription dose and single lung mean doses were studied. The prediction models were validated in 23 subsequent MPM patients. CIVR showed the strongest correlation with dose (R=0.603,p<0.001) and accurately predicted prescription dose in the validation cases. INLPVR and MILD as well as MILD and prescription dose were significantly correlated (R=-0.784,p<0.001 and R=0.554,p<0.001, respectively) in the training and validation cases. Parameters obtainable directly from planning scan anatomy predict achievable prescription doses for hemithoracic IMRT treatment of MPM patients with two intact lungs. PACS number(s): 87.55.de, 87.55.dk.

An Automated Treatment Plan Quality Control Tool for Intensity-Modulated Radiation Therapy Using a Voxel-Weighting Factor-Based Re-Optimization Algorithm

Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be satisfied when the TPS-QC tool generated re-optimized plans without sacrificing other dosimetric endpoints. In addition to its feasibility and accuracy, the proposed TPS-QC tool is also user-friendly and easy to operate, both of which are necessary characteristics for clinical use.

Hypopharyngeal squamous cell carcinoma: Three-dimensional or Intensity-modulated radiotherapy? A single institution's experience

OBJECTIVES/HYPOTHESIS

Compare outcomes of hypopharyngeal carcinoma that received conventional radiotherapy versus intensity-modulated radiotherapy (IMRT).

STUDY DESIGN

Retrospective single-institution trial.

METHODS

Between April 1990 and May 2011, 100 patients with hypopharyngeal cancer underwent curative radiotherapy (RT) at our institution: 50 with IMRT and 50 with conventional RT. The median age was 63 years. There were 12 T1, 22 T2, 37 T3, and 28 T4 patients. The majority of patients (82%) had nodal disease: 54% N2 and 8% N3. The majority of patients (83%) received chemotherapy. Of the patients who received chemotherapy, 84% received a platinum-based regimen. The median RT dose was 7,000 cGy. The majority of patients (62%) had prophylactic percutaneous endoscopic gastrostomy tube placement. Toxicities were reviewed. Local control (LC), locoregional control (LRC), freedom from distant metastasis (FFM) rates, functional larynx preservation (LP), laryngectomy-free survival (LFS), and overall-survival (OS) curves were generated using the Kaplan-Meier method. The log-rank test was used to test prognostic variables.

RESULTS

With a median follow up of 48.4 months, the 3/5-year LC, LRC, FFM, LP, LFS and OS rates were 74%/69%, 77%/74%, 70%/66%, 51%/29%, 49.6%/31.8%, and 49%/34%, respectively. The median OS was 2.9 years. The 3-year LC rate for IMRT was 77% versus 81% for conventional RT (P = .91); 3-year LRC for IMRT was 85% versus 76% for conventional RT (P = .32). There was no increased local failure with IMRT. There was no difference in the rate of stricture with IMRT (32%) versus conventional RT (25.3%) (P = .86).

CONCLUSIONS

IMRT achieved comparable LC and LRC rates to conventional RT.

LEVEL OF EVIDENCE

4 Laryngoscope, 126:620-626, 2016.

Patient-specific dosimetric endpoints based treatment plan quality control in radiotherapy

In intensity modulated radiotherapy (IMRT), the optimal plan for each patient is specific due to unique patient anatomy. To achieve such a plan, patient-specific dosimetric goals reflecting each patient's unique anatomy should be defined and adopted in the treatment planning procedure for plan quality control. This study is to develop such a personalized treatment plan quality control tool by predicting patient-specific dosimetric endpoints (DEs). The incorporation of patient specific DEs is realized by a multi-OAR geometry-dosimetry model, capable of predicting optimal DEs based on the individual patient's geometry. The overall quality of a treatment plan is then judged with a numerical treatment plan quality indicator and characterized as optimal or suboptimal. Taking advantage of clinically available prostate volumetric modulated arc therapy (VMAT) treatment plans, we built and evaluated our proposed plan quality control tool. Using our developed tool, six of twenty evaluated plans were identified as sub-optimal plans. After plan re-optimization, these suboptimal plans achieved better OAR dose sparing without sacrificing the PTV coverage, and the dosimetric endpoints of the re-optimized plans agreed well with the model predicted values, which validate the predictability of the proposed tool. In conclusion, the developed tool is able to accurately predict optimally achievable DEs of multiple OARs, identify suboptimal plans, and guide plan optimization. It is a useful tool for achieving patient-specific treatment plan quality control.

Simple tool for prediction of parotid gland sparing in intensity-modulated radiation therapy

Sparing one or both parotid glands is a key goal when planning head and neck cancer radiation treatment. If the planning target volume (PTV) overlaps one or both parotid glands substantially, it may not be possible to achieve adequate gland sparing. This finding results in physicians revising their PTV contours after an intensity-modulated radiation therapy (IMRT) plan has been run and reduces workflow efficiency. We devised a simple formula for predicting mean parotid gland dose from the overlap of the parotid gland and isotropically expanded PTV contours. We tested the tool using 44 patients from 2 institutions and found agreement between predicted and actual parotid gland doses (mean absolute error = 5.3 Gy). This simple method could increase treatment planning efficiency by improving the chance that the first plan presented to the physician will have optimal parotid gland sparing.

Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: an intertechnique and interinstitutional study

PURPOSE

To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution.

METHODS

Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol.

RESULTS

Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct.

CONCLUSIONS

The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results.The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique.

A predictive model to guide management of the overlap region between target volume and organs at risk in prostate cancer volumetric modulated arc therapy

PURPOSE

The goal of this study is to determine whether the magnitude of overlap between planning target volume (PTV) and rectum (Rectumoverlap) or PTV and bladder (Bladderoverlap) in prostate cancer volumetric-modulated arc therapy (VMAT) is predictive of the dose-volume relationships achieved after optimization, and to identify predictive equations and cutoff values using these overlap volumes beyond which the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) dose-volume constraints are unlikely to be met.

MATERIALS AND METHODS

Fifty-seven patients with prostate cancer underwent VMAT planning using identical optimization conditions and normalization. The PTV (for the 50.4 Gy primary plan and 30.6 Gy boost plan) included 5 to 10 mm margins around the prostate and seminal vesicles. Pearson correlations, linear regression analyses, and receiver operating characteristic (ROC) curves were used to correlate the percentage overlap with dose-volume parameters.

RESULTS

The percentage Rectumoverlap and Bladderoverlap correlated with sparing of that organ but minimally impacted other dose-volume parameters, predicted the primary plan rectum V45 and bladder V50 with R(2) = 0.78 and R(2) = 0.83, respectively, and predicted the boost plan rectum V30 and bladder V30 with R(2) = 0.53 and R(2) = 0.81, respectively. The optimal cutoff value of boost Rectumoverlap to predict rectum V75 >15% was 3.5% (sensitivity 100%, specificity 94%, p < 0.01), and the optimal cutoff value of boost Bladderoverlap to predict bladder V80 >10% was 5.0% (sensitivity 83%, specificity 100%, p < 0.01).

CONCLUSION

The degree of overlap between PTV and bladder or rectum can be used to accurately guide physicians on the use of interventions to limit the extent of the overlap region prior to optimization.

Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT

PURPOSE

Sparing of single-side parotid gland is a common practice in head-and-neck (HN) intensity modulated radiation therapy (IMRT) planning. It is a special case of dose sparing tradeoff between different organs-at-risk. The authors describe an improved mathematical model for predicting achievable dose sparing in parotid glands in HN IMRT planning that incorporates single-side sparing considerations based on patient anatomy and learning from prior plan data.

METHODS

Among 68 HN cases analyzed retrospectively, 35 cases had physician prescribed single-side parotid sparing preferences. The single-side sparing model was trained with cases which had single-side sparing preferences, while the standard model was trained with the remainder of cases. A receiver operating characteristics (ROC) analysis was performed to determine the best criterion that separates the two case groups using the physician's single-side sparing prescription as ground truth. The final predictive model (combined model) takes into account the single-side sparing by switching between the standard and single-side sparing models according to the single-side sparing criterion. The models were tested with 20 additional cases. The significance of the improvement of prediction accuracy by the combined model over the standard model was evaluated using the Wilcoxon rank-sum test.

RESULTS

Using the ROC analysis, the best single-side sparing criterion is (1) the predicted median dose of one parotid is higher than 24 Gy; and (2) that of the other is higher than 7 Gy. This criterion gives a true positive rate of 0.82 and a false positive rate of 0.19, respectively. For the bilateral sparing cases, the combined and the standard models performed equally well, with the median of the prediction errors for parotid median dose being 0.34 Gy by both models (p = 0.81). For the single-side sparing cases, the standard model overestimates the median dose by 7.8 Gy on average, while the predictions by the combined model differ from actual values by only 2.2 Gy (p = 0.005). Similarly, the sum of residues between the modeled and the actual plan DVHs is the same for the bilateral sparing cases by both models (p = 0.67), while the standard model predicts significantly higher DVHs than the combined model for the single-side sparing cases (p = 0.01).

CONCLUSIONS

The combined model for predicting parotid sparing that takes into account single-side sparing improves the prediction accuracy over the previous model.

Intensity-modulated radiation therapy for head and neck cancer: systematic review and meta-analysis

BACKGROUND AND PURPOSE

Intensity-modulated radiation therapy (IMRT) provides the possibility of dose-escalation with better normal tissue sparing. This study was performed to assess whether IMRT can improve clinical outcomes when compared with two-dimensional (2D-RT) or three-dimensional conformal radiation therapy (3D-CRT) in patients with head and neck cancer.

METHODS AND MATERIALS

Only prospective phase III randomized trials comparing IMRT with 2D-RT or 3D-CRT were eligible. Combined surgery and/or chemotherapy were allowed. Two authors independently selected and assessed the studies regarding eligibility criteria and risk of bias.

RESULTS

Five studies were selected. A total of 871 patients were randomly assigned for 2D-RT or 3D-CRT (437), versus IMRT (434). Most patients presented with nasopharyngeal cancers (82%), and stages III/IV (62.1%). Three studies were classified as having unclear risk and two as high risk of bias. A significant overall benefit in favor of IMRT was found (hazard ratio - HR=0.76; 95% CI: 0.66, 0.87; p<0.0001) regarding xerostomia scores grade 2-4, with similar loco-regional control and overall survival.

CONCLUSIONS

IMRT reduces the incidence of grade 2-4 xerostomia in patients with head and neck cancers without compromising loco-regional control and overall survival.

Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning: a head-and-neck case study

PURPOSE

To investigate whether an overlap volume histogram (OVH)-driven planning application using an intensity-modulated radiation therapy (IMRT) database can guide and automate volumetric-modulated arc therapy (VMAT) planning for head-and-neck cancer.

METHODS

Based on comparable head-and-neck dosimetric results between planner-generated VMAT and IMRT plans, an inhouse developed, OVH-driven automated planning application containing a database of prior clinical head-and-neck IMRT plans is built into Pinnacle(3) SmartArc for VMAT planning. Double-arc VMAT plans of four oropharynx, four nasopharynx, and four larynx patients are generated and compared with corresponding clinical IMRT plans.

RESULTS

Each VMAT plan is automatically generated in two optimization rounds, while the average number of optimization rounds in generating a clinical IMRT plan is 43. In VMAT plans, statistical superiority (p < 0.01) in sparing of the cord+4 mm, brainstem, brachial plexus, larynx, and inner ear is observed with a slight degradation in low-dose-level planning target volume (PTV) coverage. On average, D(0.1 cc) to the cord+4 mm, brainstem and brachial plexus is reduced by 3.7, 4.9, and 1.6 Gy, respectively; V(50 Gy) to the larynx is reduced by 5.3%; mean dose to the inner ear is reduced by 4.4 Gy; V(95) of low-dose-level PTV coverage is reduced by 0.3% with p = 0.25.

CONCLUSIONS

IMRT-data-driven VMAT planning offers a potential method for generating VMAT plans that are comparable to IMRT plans in terms of dosimetric quality.

Quality assessment for VMAT prostate radiotherapy planning based on data envelopment analysis

The majority of commercial radiotherapy treatment planning systems requires planners to iteratively adjust the plan parameters in order to find a satisfactory plan. This iterative trial-and-error nature of radiotherapy treatment planning results in an inefficient planning process and in order to reduce such inefficiency, plans can be accepted without achieving the best attainable quality. We propose a quality assessment method based on data envelopment analysis (DEA) to address this inefficiency. This method compares a plan of interest to a set of past delivered plans and searches for evidence of potential further improvement. With the assistance of DEA, planners will be able to make informed decisions on whether further planning is required and ensure that a plan is only accepted when the plan quality is close to the best attainable one. We apply the DEA method to 37 prostate plans using two assessment parameters: rectal generalized equivalent uniform dose (gEUD) as the input and D95 (the minimum dose that is received by 95% volume of a structure) of the planning target volume (PTV) as the output. The percentage volume of rectum overlapping PTV is used to account for anatomical variations between patients and is included in the model as a non-discretionary output variable. Five plans that are considered of lesser quality by DEA are re-optimized with the goal to further improve rectal sparing. After re-optimization, all five plans improve in rectal gEUD without clinically considerable deterioration of the PTV D95 value. For the five re-optimized plans, the rectal gEUD is reduced by an average of 1.84 Gray (Gy) with only an average reduction of 0.07 Gy in PTV D95. The results demonstrate that DEA can correctly identify plans with potential improvements in terms of the chosen input and outputs.

Reduced-order constrained optimization (ROCO): clinical application to head-and-neck IMRT

PURPOSE

The authors present the application of the reduced order constrained optimization (ROCO) method, previously successfully applied to the prostate and lung sites, to the head-and-neck (H&N) site, demonstrating that it can quickly and automatically generate clinically competitive IMRT plans. We provide guidelines for applying ROCO to larynx, oropharynx, and nasopharynx cases, and report the results of a live experiment that demonstrates how an expert planner can save several hours of trial-and-error interaction using the proposed approach.

METHODS

The ROCO method used for H&N IMRT planning consists of three major steps. First, the intensity space of treatment plans is sampled by solving a series of unconstrained optimization problems with a parameter range based on previously treated patient data. Second, the dominant modes in the intensity space are estimated by dimensionality reduction using principal component analysis (PCA). Third, a constrained optimization problem over this basis is quickly solved to find an IMRT plan that meets organ-at-risk (OAR) and target coverage constraints. The quality of the plan is assessed using evaluation tools within Memorial Sloan-Kettering Cancer Center (MSKCC)'s treatment planning system (TPS).

RESULTS

The authors generated ten H&N IMRT plans for previously treated patients using the ROCO method and processed them for deliverability by a dynamic multileaf collimator (DMLC). The authors quantitatively compared the ROCO plans to the previously achieved clinical plans using the TPS tools used at MSKCC, including DVH and isodose contour analysis, and concluded that the ROCO plans would be clinically acceptable. In our current implementation, ROCO H&N plans can be generated using about 1.6 h of offline computation followed by 5-15 min of semiautomatic planning time. Additionally, the authors conducted a live session for a plan designated by MSKCC performed together with an expert H&N planner. A technical assistant set up the first two steps, which were performed without further human interaction, and then collaborated in a virtual meeting with the expert planner to perform the third (constrained optimization) step. The expert planner performed in-depth analysis of the resulting ROCO plan and deemed it to be clinically acceptable and in some aspects superior to the clinical plan. This entire process took 135 min including two constrained optimization runs, in comparison to the estimated 4 h that would have been required using traditional clinical planning tools.

CONCLUSIONS

The H&N site is very challenging for IMRT planning, due to several levels of prescription and a large, variable number (6-20) of OARs that depend on the location of the tumor. ROCO for H&N shows promise in generating clinically acceptable plans both more quickly and with substantially less human interaction.

Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans

PURPOSE

The authors present an evidence-based approach to quantify the effects of an array of patient anatomical features of the planning target volumes (PTVs) and organs-at-risk (OARs) and their spatial relationships on the interpatient OAR dose sparing variation in intensity modulated radiation therapy (IMRT) plans by learning from a database of high-quality prior plans.

METHODS

The authors formulized the dependence of OAR dose volume histograms (DVHs) on patient anatomical factors into feature models which were learned from prior plans by a stepwise multiple regression method. IMRT plans for 64 prostate, 82 head-and-neck (HN) treatments were used to train the models. Two major groups of anatomical features were considered in this study: the volumetric information and the spatial information. The geometry of OARs relative to PTV is represented by the distance-to-target histogram, DTH. Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. The final models were tested by additional 24 prostate and 24 HN plans.

RESULTS

Significant patient anatomical factors contributing to OAR dose sparing in prostate and HN IMRT plans have been analyzed and identified. They are: the median distance between OAR and PTV, the portion of OAR volume within an OAR specific distance range, and the volumetric factors: the fraction of OAR volume which overlaps with PTV and the portion of OAR volume outside the primary treatment field. Overall, the determination coefficients R(2) for predicting the first principal component score (PCS1) of the OAR DVH by the above factors are above 0.68 for all the OARs and they are more than 0.53 for predicting the second principal component score (PCS2) of the OAR DVHs except brainstem and spinal cord. Thus, the above set of anatomical features combined has captured significant portions of the DVH variations for the OARs in prostate and HN plans. To test how well these features capture the interpatient organ dose sparing variations in general, the DVHs and specific dose-volume indices calculated from the regression models were compared with the actual DVHs and dose-volume indices from each patient's plan in the validation dataset. The dose-volume indices compared were V99%, V85%, and V50% for bladder and rectum in prostate plans and parotids median dose in HN plans. The authors found that for the bladder and rectum models, 17 out of 24 plans (71%) were within 6% OAR volume error and 21 plans (85%) were within 10% error; For the parotids model, the median dose values for 30 parotids out of 48 (63%) were within 6% prescription dose error and the values in 40 parotids (83%) were within 10% error.

CONCLUSIONS

Quantitative analysis of patient anatomical features and their correlation with OAR dose sparing has identified a number of important factors that explain significant amount of interpatient DVH variations in OARs. These factors can be incorporated into evidence-based learning models as effective features to provide patient-specific OAR dose sparing goals.

Interobserver variation in parotid gland delineation: a study of its impact on intensity-modulated radiotherapy solutions with a systematic review of the literature

OBJECTIVES

This study evaluates the interobserver variation in parotid gland delineation and its impact on intensity-modulated radiotherapy (IMRT) solutions.

METHODS

The CT volumetric data sets of 10 patients with oropharyngeal squamous cell carcinoma who had been treated with parotid-sparing IMRT were used. Four radiation oncologists and three radiologists delineated the parotid gland that had been spared using IMRT. The dose-volume histogram (DVH) for each study contour was calculated using the IMRT plan actually delivered for that patient. This was compared with the original DVH obtained when the plan was used clinically.

RESULTS

70 study contours were analysed. The mean parotid dose achieved during the actual treatment was within 10% of 24 Gy for all cases. Using the study contours, the mean parotid dose obtained was within 10% of 24 Gy for only 53% of volumes by radiation oncologists and 55% of volumes by radiologists. The parotid DVHs of 46% of the study contours were sufficiently different from those used clinically, such that a different IMRT plan would have been produced.

CONCLUSION

Interobserver variation in parotid gland delineation is significant. Further studies are required to determine ways of improving the interobserver consistency in parotid gland definition.

Geometry of volumes in radiotherapy planning. A new method for a quantitative assessment

Aims and background

The purpose of the study was to develop a general method able to quantify the mutual disposition in the 3D space of critical organs with respect to the target when these structures are designed for a radiotherapy treatment plan. To that end, we introduce the “expansion intersection histogram”, a function defined as the intersection between an organ at risk and the target volume, while the target is expanded in 3D.

Methods and study design

A software was developed to calculate the expansion intersection histogram of anatomical structures exported in a DICOM format from a commercial treatment planning system. A virtual phantom with spherical and cylindrical objects arranged in different dispositions in the 3D space was created for testing the software under known conditions.

Results and conclusions

Expansion intersection histogram computation was tested against reference data derived analytically for spherical volumes, with a resulting maximum error of 0.5%. Specific geometric features derived from the expansion intersection histogram, such as the distance between a selected target and each different ideal volume included in the virtual phantom, well matched the corresponding theoretical expected values. The expansion intersection histogram was evaluated also for the anatomical structures of a real patient. Data show this method as a tool to effectively take into account the mutual disposition of each critical organ with respect to the target, summarized in characteristics of distance, shape and orientation. The expansion intersection histogram method integrates and extends other preexisting modalities for evaluating the geometrical relationships among radiotherapy volumes and could be used to improve planning optimization.

Increased organ sparing using shape-based treatment plan optimization for intensity modulated radiation therapy of pancreatic adenocarcinoma

PURPOSE

To develop a model to assess the quality of an IMRT treatment plan using data of prior patients with pancreatic adenocarcinoma.

METHODS

The dose to an organ at risk (OAR) depends in large part on its orientation and distance to the planning target volume (PTV). A database of 33 previously treated patients with pancreatic cancer was queried to find patients with less favorable PTV-OAR configuration than a new case. The minimal achieved dose among the selected patients should also be achievable for the OAR of the new case. This way the achievable doses to the OARs of 25 randomly selected pancreas cancer patients were predicted. The patients were replanned to verify if the predicted dose could be achieved. The new plans were compared to their original clinical plans.

RESULTS

The predicted doses were achieved within 1 and 2 Gy for more than 82% and 94% of the patients, respectively, and were a good approximation of the minimal achievable doses. The improvement after replanning was 1.4 Gy (range 0-4.6 Gy) and 1.7 Gy (range 0-6.3 Gy) for the mean dose to the liver and the kidneys, respectively, without compromising target coverage or increasing radiation dose to the bowel, cord or stomach.

CONCLUSIONS

The model could accurately predict the achievable doses, leading to a considerable decrease in dose to the OARs and an increase in treatment planning efficiency.

Rotational IMRT techniques compared to fixed gantry IMRT and tomotherapy: multi-institutional planning study for head-and-neck cases

BACKGROUND

Recent developments enable to deliver rotational IMRT with standard C-arm gantry based linear accelerators. This upcoming treatment technique was benchmarked in a multi-center treatment planning study against static gantry IMRT and rotational IMRT based on a ring gantry for a complex parotid gland sparing head-and-neck technique.

METHODS

Treatment plans were created for 10 patients with head-and-neck tumours (oropharynx, hypopharynx, larynx) using the following treatment planning systems (TPS) for rotational IMRT: Monaco (ELEKTA VMAT solution), Eclipse (Varian RapidArc solution) and HiArt for the helical tomotherapy (Tomotherapy). Planning of static gantry IMRT was performed with KonRad, Pinnacle and Panther DAO based on step&shoot IMRT delivery and Eclipse for sliding window IMRT. The prescribed doses for the high dose PTVs were 65.1Gy or 60.9Gy and for the low dose PTVs 55.8Gy or 52.5Gy dependend on resection status. Plan evaluation was based on target coverage, conformity and homogeneity, DVHs of OARs and the volume of normal tissue receiving more than 5Gy (V5Gy). Additionally, the cumulative monitor units (MUs) and treatment times of the different technologies were compared. All evaluation parameters were averaged over all 10 patients for each technique and planning modality.

RESULTS

Depending on IMRT technique and TPS, the mean CI values of all patients ranged from 1.17 to 2.82; and mean HI values varied from 0.05 to 0.10. The mean values of the median doses of the spared parotid were 26.5Gy for RapidArc and 23Gy for VMAT, 14.1Gy for Tomo. For fixed gantry techniques 21Gy was achieved for step&shoot+KonRad, 17.0Gy for step&shoot+Panther DAO, 23.3Gy for step&shoot+Pinnacle and 18.6Gy for sliding window.V5Gy values were lowest for the sliding window IMRT technique (3499 ccm) and largest for RapidArc (5480 ccm). The lowest mean MU value of 408 was achieved by Panther DAO, compared to 1140 for sliding window IMRT.

CONCLUSIONS

All IMRT delivery technologies with their associated TPS provide plans with satisfying target coverage while at the same time respecting the defined OAR criteria. Sliding window IMRT, RapidArc and Tomo techniques resulted in better target dose homogeneity compared to VMAT and step&shoot IMRT. Rotational IMRT based on C-arm linacs and Tomotherapy seem to be advantageous with respect to OAR sparing and treatment delivery efficiency, at the cost of higher dose delivered to normal tissues. The overall treatment plan quality using Tomo seems to be better than the other TPS technology combinations.