Dose-volume Histogram Prediction Research Articles

Comparative analysis of two dose-volume histogram prediction tools for treatment planning in volumetric-modulated arc therapy: A multi-planner study

The increase in high-precision radiation therapy, particularly volumetric-modulated arc therapy (VMAT), has increased patient numbers and expanded treatment sites. However, a significant challenge in VMAT treatment planning is the inconsistent plan quality among different planners and facilities. This study explored the use of dose-volume histogram (DVH) prediction tools to address these disparities, specifically focusing on RapidPlan (Varian Medical Systems) and PlanIQ (Sun Nuclear). RapidPlan predicts achievable DVHs and automatically generates optimization objectives. While it has demonstrated organ-at-risk (OAR) dose reduction benefits, the quality of the plan used to build its model significantly affects its predictions. On the other hand, PlanIQ offers ease of use and does not require prior model-building. Five planners participated in this study, each creating two treatment plans: one referencing RapidPlan and the other using PlanIQ. The planners had the freedom to adjust parameters while referencing the DVH predictions. The plans were evaluated using “Plan Quality Metric” (PQM) scores to assess the planning target volume excluding the rectum and OARs. The results revealed that RapidPlan-referenced plans often outperformed PlanIQ-based plans, with less interplanner variability. PlanIQ played a pivotal role in the construction of the RapidPlan model. This study is the first to compare plans generated by multiple planners using both tools. This study provides insights into optimizing treatment planning by considering the characteristics of both RapidPlan and PlanIQ.

Knowledge-based planning using both the predicted DVH of organ-at risk and planning target volume.

The purpose of this study was to evaluate the performance of a knowledge-based planning (KBP) method in nasopharyngeal cancer radiotherapy using the predicted dose-volume histogram (DVH) of organ-at risk (OAR) and planning target volume (PTV). A total of 85 patients previously treated for nasopharyngeal cancer using 9-field 6-MV intensity-modulated radiation therapy (IMRT) were identified for training and 30 similar patients were identified for testing. The dosimetric deposition information, individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation, was used to predict both OAR and PTV DVH. Two KBP methods (KBPOAR and KBPOAR+PTV) were established for plan generation based on the DVH prediction. The KBPOAR method utilized the dose constraints based on the predicted OAR DVH and the PTV dose constraints obtained according to the planning experience, while the KBPOAR+PTV method applied the dose constraints based on the predicted OAR and PTV DVH. For the plan evaluation, the PTV dose coverage was used D98 and D2, and the maximum dose, mean dose or dose-volume parameters were used for the OARs. Statistical differences of the two KBP methods were tested with the Wilcoxon signed rank test. For patients with T3 tumors, there was no significant difference between the KBPOAR and KBPOAR+PTV methods in dosimetric results at most OARs and PTVs. Both KBP methods achieved a similar number of plans meeting the dose requirements. For patients with T4 tumors, KBPOAR+PTV reduced the maximum dose by more than 1Gy in the body, spinal cord, optic nerve, eye and temporal lobes and reduced the V50 value by more than 3.9% in the larynx and tongue without reducing the PTV dose compared with KBPOAR. The KBPOAR+PTV method increased the plans by more than 14.2% in meeting the maximum dose requirements at the body, optic nerve, mandible and eye and increased the plans by more than 21.4% in meeting the V50 of the larynx and V50 of the tongue when compared with the KBPOAR method. For patients with T3 tumors, no significant difference was found between the KBPOAR and KBPOAR+PTV methods in dosimetric results at most OARs and PTVs. For patients with T4 tumors, the KBPOAR+PTV method performs better than the KBPOAR method in improving the quality of the plans. Compared with the KBPOAR method, dose sparing of some OARs was achieved without reducing PTV dose coverage and helped to increase the number of plans meeting the dose requirements when the KBPOAR+PTV method was utilized.

A personalized DVH prediction model for HDR brachytherapy in cervical cancer treatment.

PurposeAlthough the knowledge-based dose-volume histogram (DVH) prediction has been largely researched and applied in External Beam Radiation Therapy, it is still less investigated in the domain of brachytherapy. The purpose of this study is to develop a reliable DVH prediction method for high-dose-rate brachytherapy plans.MethodA DVH prediction workflow combining kernel density estimation (KDE), k-nearest neighbor (kNN), and principal component analysis (PCA) was proposed. PCA and kNN were first employed together to select similar patients based on principal component directions. 79 cervical cancer patients with different applicators inserted was included in this study. The KDE model was built based on the relationship between distance-to-target (DTH) and the dose in selected cases, which can be subsequently used to estimate the dose probability distribution in the validation set. Model performance of bladder and rectum was quantified by |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the form of mean and standard deviation. The model performance between KDE only and the combination of kNN, PCA, and KDE was compared.Result20, 30 patients were selected for rectum and bladder based on KNN and PCA, respectively. The absolute residual between the actual plans and the predicted plans were 0.38 ± 0.29, 0.4 ± 0.32, 0.43 ± 0.36, 0.97 ± 0.66, and 0.13 ± 0.99 for |ΔD2cc|, |ΔD1cc|, |ΔD0.1cc|, |ΔDmax|, and |ΔDmean| in the bladder, respectively. For rectum, the corresponding results were 0.34 ± 0.27, 0.38 ± 0.33, 0.63 ± 0.57, 1.41 ± 0.99 and 0.23 ± 0.17, respectively. The combination of kNN, PCA, and KDE showed a significantly better prediction performance than KDE only, with an improvement of 30.3% for the bladder and 33.3% for the rectum.ConclusionIn this study, a knowledge-based machine learning model was proposed and verified to accurately predict the DVH for new patients. This model is proved to be effective in our testing group in the workflow of HDR brachytherapy.

Knowledge-based planning for the radiation therapy treatment plan quality assurance for patients with head and neck cancer.

This study aimed to investigate the feasibility of using a knowledge‐based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. We created two dose–volume histogram (DVH) prediction models using a commercial knowledge‐based planning system (RapidPlan, Varian Medical Systems, Palo Alto, CA) from plans generated by manual planning (MP) and automated planning (AP) approaches. DVHs were predicted for evaluation cohort 1 (EC1) of 25 patients and compared with achieved DVHs of MP and AP plans to evaluate prediction accuracy. Additionally, we predicted DVHs for evaluation cohort 2 (EC2) of 25 patients for which we intentionally generated plans with suboptimal normal tissue sparing while satisfying dose–volume limits of standard practice. Three radiation oncologists reviewed these plans without seeing the DVH predictions. We found that predicted DVH ranges (upper–lower predictions) were consistently wider for the MP model than for the AP model for all normal structures. The average ranges of mean dose predictions among all structures was 9.7 Gy (MP model) and 3.4 Gy (AP model) for EC1 patients. RapidPlan models identified 7 MP plans as outliers according to mean dose or D1% for at least one structure, while none of AP plans were flagged. For EC2 patients, 22 suboptimal plans were identified by prediction. While re‐generated AP plans validated that these suboptimal plans could be improved, 40 out of 45 structures with predicted poor sparing were also identified by oncologist reviews as requiring additional planning to improve sparing in the clinical setting. Our study shows that knowledge‐based DVH prediction models can be sufficiently accurate for plan quality assurance purposes. A prediction model built by a small cohort automatically‐generated plans was effective in detecting suboptimal plans. Such tools have potential to assist the plan quality assurance workflow for individual patients in the clinic.

Technical note: Determining the applicability of a clinical knowledge-based learning model via prospective outlier detection.

Knowledge-based planning (KBP) is increasingly implemented clinically because of its demonstrated ability to improve treatment planning efficiency and reduce plan quality variations. However, cases with large dose-volume histogram (DVH) prediction uncertainties may still need manual adjustments by the planner to achieve high plan quality. The purpose of this study is to develop a data-driven method to detect patients with high prediction uncertainties so that intentional effort is directed to these patients. We apply an anomaly detection method known as the local outlier factor (LOF) to a dataset consisting of the training set and each of the prospective patients considered, to evaluate their likelihood of being an anomaly when compared with the training cases. Features used in the LOF analysis include anatomical features and the model-generated DVH principal component scores. To test the efficacy of the proposed model, 142 prostate patients were retrieved from the clinical database and split into a training dataset of 100 patients and a test dataset of 42 patients. The outlier identification performance was quantified by the difference between the DVH prediction root-mean-squared errors (RMSE) of the identified outlier cases and that of the remaining inlier cases. With a predefined LOF threshold of 1.4, the inlier cases achieved average RMSEs of 5.0 and 6.7 for bladder and rectum, while the outlier cases have substantially higher RMSEs of 6.7 and 13.0 in comparison. We propose a method that can determine the prospective patient's outlier status. This method can be integrated into existing automated treatment planning workflows to reduce the risk of generating suboptimal treatment plans while providing an upfront alert to the treatment planner.

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.

Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

A practical method to quantify knowledge-based DVH prediction accuracy and uncertainty with reference cohorts.

The adoption of knowledge‐based dose‐volume histogram (DVH) prediction models for assessing organ‐at‐risk (OAR) sparing in radiotherapy necessitates quantification of prediction accuracy and uncertainty. Moreover, DVH prediction error bands should be readily interpretable as confidence intervals in which to find a percentage of clinically acceptable DVHs. In the event such DVH error bands are not available, we present an independent error quantification methodology using a local reference cohort of high‐quality treatment plans, and apply it to two DVH prediction models, ORBIT‐RT and RapidPlan, trained on the same set of 90 volumetric modulated arc therapy (VMAT) plans. Organ‐at‐risk DVH predictions from each model were then generated for a separate set of 45 prostate VMAT plans. Dose‐volume histogram predictions were then compared to their analogous clinical DVHs to define prediction errors Vclin,i‐Vpred,i (ith plan), from which prediction bias μ, prediction error variation σ, and root‐mean‐square error RMSEpred≡1N∑iVclin,i‐Vpred,i2≅σ2+μ2 could be calculated for the cohort. The empirical RMSEpred was then contrasted to the model‐provided DVH error estimates. For all prostate OARs, above 50% Rx dose, ORBIT‐RT μ and σ were comparable to or less than those of RapidPlan. Above 80% Rx dose, μ < 1% and σ < 3‐4% for both models. As a result, above 50% Rx dose, ORBIT‐RT RMSEpred was below that of RapidPlan, indicating slightly improved accuracy in this cohort. Because μ ≈ 0, RMSEpred is readily interpretable as a canonical standard deviation σ, whose error band is expected to correctly predict 68% of normally distributed clinical DVHs. By contrast, RapidPlan's provided error band, although described in literature as a standard deviation range, was slightly less predictive than RMSEpred (55–70% success), while the provided ORBIT‐RT error band was confirmed to resemble an interquartile range (40–65% success) as described. Clinicians can apply this methodology using their own institutions’ reference cohorts to (a) independently assess a knowledge‐based model's predictive accuracy of local treatment plans, and (b) interpret from any error band whether further OAR dose sparing is likely attainable.

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nasopharyngeal cancer intensity-modulated radiation therapy (IMRT) plans and 60 rectal cancer volumetric modulated arc therapy (VMAT) plans were employed in this study. Of these, 20 nasopharyngeal cancer cases and 15 rectal cancer cases were randomly selected as the testing data. The DVH prediction was performed using two methods. One method applied the individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation and the other method applied the distance-to-target histogram and the conformal-plan-dose-volume histogram (DTH + CPDVH). The determination coefficient R2 and mean absolute error (MAE) were used to evaluate DVH prediction accuracy. The PTV DVH prediction was performed using the IDVHs. The PTV dose coverage was evaluated using D98, D95, D1 and uniformity index (UI). The OAR dose was compared using the maximum dose, V30 and V40. The significance of the results was examined with the Wilcoxon signed rank test. For PTV DVH prediction using IDVHs, the clinical plan and IDVHs prediction method achieved mean UI values of 1.07 and 1.06 for nasopharyngeal cancer, and 1.04 and 1.05 for rectal cancer, respectively. No significant difference was found between the clinical plan results and predicted results using the IDVHs method in achieving PTV dose coverage (D98,D95,D1 and UI) for both nasopharyngeal cancer and rectal cancer (p-values ≥ 0.052). For OAR DVH prediction, no significant difference was found between the IDVHs and DTH + CPDVH methods for the R2, MAE, the maximum dose, V30 and V40 (p-values ≥ 0.087 for all OARs). This work evaluates the performance of dosimetric information of several individual fields with uniform-intensity radiation for DVH prediction, and extends its application to PTV DVH prediction. The results indicated that the IDVHs method is comparable to the DTH + CPDVH method in accurately predicting the OAR DVH. The IDVHs method quantified the input features of the PTV and showed reliable PTV DVH prediction, which is helpful for plan quality evaluation and plan generation.

DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects.

Purpose A recurrent neural network (RNN) and its variants such as gated recurrent unit-based RNN (GRU-RNN) were found to be very suitable for dose-volume histogram (DVH) prediction in our previously published work. Using the dosimetric information generated by nonmodulated beams of different orientations, the GRU-RNN model was capable of accurate DVH prediction for nasopharyngeal carcinoma (NPC) treatment planning. On the basis of our previous work, we proposed an improved approach and aimed to further improve the DVH prediction accuracy as well as study the feasibility of applying the proposed method to relatively small-size patient data. Methods Eighty NPC volumetric modulated arc therapy (VMAT) plans with local IRB's approval in recent two years were retrospectively and randomly selected in this study. All these original plans were created using the Eclipse treatment planning system (V13.5, Varian Medical Systems, USA) with ≥95% of PGTVnx receiving the prescribed doses of 70 Gy, ≥95% of PGTVnd receiving 66 Gy, and ≥95% of PTV receiving 60 Gy. Among them, fifty plans were used to train the DVH prediction model, and the remaining were used for testing. On the basis of our previously published work, we simplified the 3-layer GRU-RNN model to a single-layer model and further trained every organ at risk (OAR) separately with an OAR-specific equivalent uniform dose- (EUD-) based loss function. Results The results of linear least squares regression obtained by the new proposed method showed the excellent agreements between the predictions and the original plans with the correlation coefficient r = 0.976 and 0.968 for EUD results and maximum dose results, respectively, and the coefficient r of our previously published method was 0.957 and 0.946, respectively. The Wilcoxon signed-rank test results between the proposed and the previous work showed that the proposed method could significantly improve the EUD prediction accuracy for the brainstem, spinal cord, and temporal lobes with a p value < 0.01. Conclusions The accuracy of DVH prediction achieved in different OARs showed the great improvements compared to the previous works, and more importantly, the effectiveness and robustness showed by the simplified GRU-RNN trained from relatively small-size DVH samples, fully demonstrated the feasibility of applying the proposed method to small-size patient data. Excellent agreements in both EUD results and maximum dose results between the predictions and original plans indicated the application prospect in a physically and biologically related (or a mixture of both) model for treatment planning.

Quantitative Comparison of Knowledge-Based and Manual Intensity Modulated Radiation Therapy Planning for Nasopharyngeal Carcinoma.

Background and purposeTo validate the feasibility and efficiency of a fully automatic knowledge-based planning (KBP) method for nasopharyngeal carcinoma (NPC) cases, with special attention to the possible way that the success rate of auto-planning can be improved.Methods and materialsA knowledge-based dose volume histogram (DVH) prediction model was developed based on 99 formerly treated NPC patients, by means of which the optimization objectives and the corresponding priorities for intensity modulation radiation therapy (IMRT) planning were automatically generated for each head and neck organ at risk (OAR). The automatic KBP method was thus evaluated in 17 new NPC cases with comparison to manual plans (MP) and expert plans (EXP) in terms of target dose coverage, conformity index (CI), homogeneity index (HI), and normal tissue protection. To quantify the plan quality, a metric was applied for plan evaluation. The variation in the plan quality and time consumption among planners was also investigated.ResultsWith comparable target dose distributions, the KBP method achieved a significant dose reduction in critical organs such as the optic chiasm (p<0.001), optic nerve (p=0.021), and temporal lobe (p<0.001), but failed to spare the spinal cord (p<0.001) compared with MPs and EXPs. The overall plan quality evaluation gave mean scores of 144.59±11.48, 142.71±15.18, and 144.82±15.17, respectively, for KBPs, MPs, and EXPs (p=0.259). A total of 15 out of 17 KBPs (i.e., 88.24%) were approved by our physician as clinically acceptable.ConclusionThe automatic KBP method using the DVH prediction model provided a possible way to generate clinically acceptable plans in a short time for NPC patients.

Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy

To adopt a transfer learning approach and establish a convolutional neural network (CNN) model for the prediction of rectum and bladder dose-volume histograms (DVH) in prostate patients treated with a VMAT technique. One hundred forty-four VMAT patients with intermediate or high-risk prostate cancer were included in this study. Data were split into two sets: 120 and 24 patients, respectively. The second set was used for final validation. To ensure the accuracy of the training data, we developed a ground-truth analysis for detecting and correcting for all potential outliers. We used transfer learning in combination with a pre-trained VGG-16 network. We dropped the fully connected layers from the VGG-16 and added a new fully connected neural network. The inputs for the CNN were a 2D image of the volumes contoured in the CT, but we only retained the geometrical information of every CT-slice. The outputs were the corresponding rectum and bladder DVH for every slice. We used a confusion matrix to analyze the performance of our model. Our model achieved 100% and 81% of true positive and true negative predictions, respectively. We have an overall accuracy of 87.5%, a misclassification rate of 12.5%, and a precision of 100%. We have successfully developed a model for reliable prediction of rectum and bladder DVH in prostate patients by applying a previously pre-trained CNN. To our knowledge, this is the first attempt to apply transfer learning to the prediction of DVHs that accounts for the ground truth problem.

A Study of Automatic Planning of Esophageal Cancer Based on Dose Prediction and Beam Angle Optimization

To propose an automatic planning method for esophageal cancer based on dose volume histogram prediction and beam angle optimization in the treatment planning system (TPS). 50 IMRT plans of esophageal cancer were selected as the training set to establish a dose prediction model for organ at risk. The gantry optimization function of the three-dimensional conformal radiotherapy (3D-CRT) module was used in the TPS to provide the beam angle for automatic planning. Another 20 testing plans were optimized in the TPS and manual method respectively, the dosimetric differences and planning efficiency between the two methods were statistically compared with paired t-test. The predicted dose of organs at risk fit well with real DVH parameters in automatic plans. There was no significant dosimetric difference observed in Dmax, Dmin, CI, HI of PTV, V5 of both lungs, the Dmax of spinal cord between automatic plans and manual plans. The V20 and Dmean of both lungs generated from automatic plans were reduced by 1.1%, 0.37Gy and 1.2%, 0.38Gy, respectively, compared with those in manual plans. The V30, V40 and Dmean of heart in automatic plans were decreased significantly by 5.1%, 3.0% and 1.41Gy, respectively, compared with those in manual plans. The labor time, computer working time, and MU number of automatic plans were decreased by 65.8%, 14.1%, and 17.2%, respectively, compared with manual planning. The automatic planning script improve the efficiency of esophageal cancer planning by dose prediction of organ at risk and beam angle optimization, and provides another alternative for esophageal cancer radiotherapy planning.

Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics.

Background and purposeRadiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis.Material and methodsA treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort).ResultsFor the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and −4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: −0.1 ± 1.1 Gy and −0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning.ConclusionAn automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation.

Application of dose-volume histogram prediction in biologically related models for nasopharyngeal carcinomas treatment planning

PurposeIn this study, we employed a gated recurrent unit (GRU)-based recurrent neural network (RNN) using dosimetric information induced by individual beam to predict the dose-volume histogram (DVH) and investigated the feasibility and usefulness of this method in biologically related models for nasopharyngeal carcinomas (NPC) treatment planning.Methods and materialsOne hundred patients with NPC undergoing volumetric modulated arc therapy (VMAT) between 2018 and 2019 were randomly selected for this study. All the VMAT plans were created using the Monaco treatment planning system (Elekta, Sweden) and clinically approved: > 98% of PGTVnx received the prescribed doses of 70 Gy, > 98% of PGTVnd received the prescribed doses of 66 Gy and > 98% of PCTV received 60 Gy. Of these, the data from 80 patients were used to train the GRU-RNN, and the data from the other 20 patients were used for testing. For each NPC patient, the DVHs of different organs at risk were predicted by a trained GRU-based RNN using the information given by individual conformal beams. Based on the predicted DVHs, the equivalent uniform doses (EUD) were calculated and applied as dose constraints during treatment planning optimization. The regenerated VMAT experimental plans (EPs) were evaluated by comparing them with the clinical plans (CPs).ResultsFor the 20 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs showed better consistency in PTV dose distribution and better dose sparing for many organs at risk, and significant differences were found in the maximum/mean doses to the brainstem, brainstem PRV, spinal cord, lenses, temporal lobes, parotid glands and larynx with P-values < 0.05. On average, compared with the CPs, the maximum/mean doses to these OARs were altered by − 3.44 Gy, − 1.94 Gy, − 1.88 Gy, 0.44 Gy, 1.98 Gy, − 1.82 Gy and 2.27 Gy, respectively. In addition, significant differences were also found in brainstem and spinal cord for the dose received by 1 cc volume with 4.11 and 1.67 Gy dose reduction in EPs on average.ConclusionThe GRU-RNN-based DVH prediction method was capable of accurate DVH prediction. The regenerated plans guided by the predicted EUDs were not inferior to the manual plans, had better consistency in PTVs and better dose sparing in critical OARs, indicating the usefulness and effectiveness of biologically related model in knowledge-based planning.

Robustness comparative study of dose–volume–histogram prediction models for knowledge-based radiotherapy treatment planning

Purpose: To compare the robustness of three dose-volume-histogram (DVH) prediction models for knowledge-based treatment planning (KBP) for radiation therapy.Methods: Three models proposed by Zhu et...

Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals

In this study, we developed a gated recurrent unit (GRU)-based recurrent neural network (RNN) for dose-volume histogram (DVH) prediction in volumetric modulated arc therapy (VMAT) planning for nasopharyngeal carcinomas (NPCs) based on uniform-intensity radiation with equal angle intervals and investigated the feasibility and usefulness of this method for treatment optimization.One hundred twenty-four NPC patients were selected from a database containing clinical VMAT plans from 2015 to 2018; of these, the data from 100 patients were used to train the GRU-RNN, and the data of the other 24 patients were used for testing. For the prescribed doses to D95 (the absorbed dose for 95% of the planning target volume) of all the plans in 30 or 31 fractions, 70 Gy were delivered to PTV70 (the gross tumour volume with circumferential margin), 60 Gy were delivered to PTV60, 54 Gy were delivered to PTV54 and 66 Gy were delivered to PTV66 (lymph node gross tumour volume with circumferential margin). For each NPC patient, an equal-field-weight conformal radiotherapy plan was generated by a treatment planning system (TPS) to offer uniform-intensity radiation. By adjusting the field weights, the dose distribution induced by individual conformal beams was acquired, and the corresponding DVH was calculated. Direction-dependent DVHs were employed to predict the DVH for VMAT with the GRU-RNN, and the regenerated VMAT experimental plans (EPs), guided by the predicted DVHs, were evaluated by comparing them with the clinical plans (CPs).For the 24 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved good consistency relative to the CPs. The EPs resulted in better dose sparing for many organs at risk (OARs) while still meeting the acceptable criteria for the PTVs. Significant differences were found in the maximum/mean doses to the optic nerves, temporal lobes, lenses, mandibles, temporomandibular joints (TMJs), larynx and inner ears, with P-values of 0.03, 0.01, 0.01, <0.01, 0.02, 0.02 and <0.01, respectively. On average, compared to the CPs, the maximum/mean doses to these OARs were altered by −1.38 Gy, −0.92 Gy, 0.53 Gy, −1.19 Gy, −1.16 Gy, 2.39 Gy and −1.71 Gy, respectively.The results showed the accuracy and effectiveness of the proposed uniform-intensity radiation approach. The regenerated plans guided by the predictive method were not inferior to the manual plans, indicating their great potential for improved planning and quality control in clinical applications.

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

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

Assessment of DVH Prediction Model and Auto-Planning Module for Head and Neck VMAT Planning

The Auto-Planning module on a treatment planning system (TPS) can efficiently produce clinically acceptable VMAT plans with consistent quality. However, the initial optimization objectives of organs at risk (OARs) in Auto-Planning module need to be manually pre-set, with the result that plan quality and planning time are planner dependent. In this study, a dose volume histogram (DVH) prediction model was implemented for predicting reasonable initial optimization objectives to the Auto-Planning module. The advantage of this combination method was investigated for head and neck VMAT planning. 20 patients with head and neck cancer were selected randomly. 70 Gy in 35 fractions was given to PGTV (planning target volume expanded form gross tumor volume) and 63 Gy in 35 fractions was given to PCTV1 (covering adjacent high-risk structures and bilateral retropharyngeal lymph nodes), with (11 cases) or without (9 cases) 56 Gy in 35 fractions to PCTV2 (for prophylactic lymph nodes if clinically indicated). A DVH prediction model, which uses the geometry of targets and OARs to predict the DVH for OARs in PlanIQ software, was implemented for predicting the initial optimization objectives to the Auto-Planning module. To evaluate the advantage of this combination method, two types VMAT plans were generated as followed: VMATap plans were optimized by the Auto-Planning module with initial objectives from the Auto-Planning template with manually pre-setting objectives, and VMATpre plans were optimized by the Auto-Planning module with initial objectives predicted from the DVH prediction model in PlanIQ. Then, the dosimetric indices between VMATap and VMATpre plans were compared. There were no significant differences of V70Gy, V63 Gy, and V56 Gy between VMATap plans and VMATpre plans for PGTV, PCTV1, and PCTV2, respectively. Additionally, there were no significant differences of Dmax between VMATap plans and VMATpre plans for eye-L, lens-L and optic nerve-L. In some extent, VMATpre plans were more sparing eye-R and len-R than VMATap plans, while VMATap plans produced lower optic nerve-R dose than VMATpre plans. Furthermore, Dmax of brainstem and spinal-cord were both dramatically reduced in VMATpre plans than VMATap plans with significant differences (p < 0.01). Especially for brainstem, Dmax of VMATpre plans were well consistent with the predicted values by the DVH prediction model. For Dmean of parotids, no significant differences were found between these VMAT plans. The reason maybe that the predicted values, initial objectives in VMATpre plans, were around the manually pre-setting initial objectives in VMATap plans. DVH prediction model can generate reasonable initial objectives for the Auto-Planning module in a treatment planning system. The combination method has the potential advantage to further reduce radiation dose received by OARs.

Implement a knowledge-based automated dose volume histogram prediction module in Pinnacle3 treatment planning system for plan quality assurance and guidance.

PurposeThis work aims to develop a knowledge‐based automated dose volume histogram (DVH) prediction module that serves as a plan quality evaluation tool and treatment planning guidance in commercial Pinnacle3 treatment planning system (Philips Radiation Oncology Systems, Fitchburg, WI, USA).MethodsThe knowledge‐based automated DVH prediction module was developed with kernel density estimation (KDE) method and applied for Pinnacle3 treatment planning system. Treatment plan data from 20 esophageal cancer cases were used for creating a module to predict DVHs. Twenty additional esophageal clinical plans were evaluated on the developed module. Predicted DVHs were compared with manual ones. Differences between the predicted and achieved DVHs were analyzed.ResultsThe plan evaluation module was successfully implemented in Pinnacle3 treatment planning system. Strong linear correlations were found between predicted and achieved DVH for organs at risk. Suboptimal treatment plan quality could be improved according to the predicted DVHs by the module.ConclusionThe knowledge‐based automated DVH prediction module implemented in Pinnacle3 could be used to efficiently evaluate the treatment plan quality and as guidance for further plan optimization.

Modeling of multiple planning target volumes for head and neck treatments in knowledge-based treatment planning.

The purpose of this study is to develop an accurate and reliable dose volume histogram (DVH) prediction method for external beam radiation therapy plans with multiple planning target volumes (PTVs). We present a novel DVH prediction workflow, including new features and a modeling methodology, that makes better use of multiple PTVs: (a) We propose a generalized feature to characterize the geometric relationship of organ-at-risk (OARs) with respect to two or more PTVs with different prescribed dose levels; (b) We incorporate a novel data augmentation method to improve the data distribution in the feature space; (c) A similarity metric that leverages such information is subsequently used to select a subset of similar cases from the training dataset for model building; (d) Finally, a DVH prediction model is trained with these selected cases. To evaluate this new modeling workflow, we used 120 head and neck (HN) cases to tune the model, and used a separate dataset consisting of 148 cases for validation. The proposed model has been compared with the conventional knowledge-based model in terms of model prediction accuracy, which was measured by the root mean squared error (RMSE) between the predicted DVHs and the actual clinical plan DVHs. Furthermore, 25 randomly selected plans were replanned guided by the proposed model and evaluated against clinical plans using clinical evaluation criteria. The proposed modeling workflow significantly improved DVH prediction accuracy for brainstem (P<0.001), cord (P<0.001), larynx (P=0.004), mandible (P<0.001), oral cavity (P=0.011), parotid (P<0.001) and pharynx (P=0.001). Cases replanned with the guidance of the proposed model spared OARs significantly better by clinical evaluation criteria. The replanned cases showed a 15% increase in the number of satisfied criteria, compared with clinical plans. The proposed modeling workflow generates DVH predictions with improved accuracy and robustness when multiple PTVs exist in a plan. It has demonstrated that the improvement in the DVH prediction model translates into better plan quality in knowledge-based planning.