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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.

Purpose In this paper, we compare four novel knowledge‐based planning (KBP) algorithms using deep learning to predict three‐dimensional (3D) dose distributions of head and neck plans using the same patients’ dataset and quantitative assessment metrics. Methods A dataset of 340 oropharyngeal cancer patients treated with intensity‐modulated radiation therapy was used in this study, which represents the AAPM OpenKBP – 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel‐wise dose predictions: U‐Net, attention U‐Net, residual U‐Net (Res U‐Net), and attention Res U‐Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground‐truth using dose statistics and dose‐volume indices. Results The four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D99 index for all targets was 0.92 Gy (p = 0.51) for attention Res U‐Net, 0.94 Gy (p = 0.40) for Res U‐Net, 2.94 Gy (p = 0.09) for attention U‐Net, and 3.51 Gy (p = 0.08) for U‐Net. For the OARs, the values for the Dmax ${D_{max}}$and Dmean ${D_{mean}}$indices were 2.72 Gy (p < 0.01) for attention Res U‐Net, 2.94 Gy (p < 0.01) for Res U‐Net, 1.10 Gy (p < 0.01) for attention U‐Net, 0.84 Gy (p < 0.29) for U‐Net. Conclusion All models demonstrated almost comparable performance for voxel‐wise dose prediction. KBP models that employ 3D U‐Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.

Purpose Deep learning–based knowledge‐based planning (KBP) methods have been introduced for radiotherapy dose distribution prediction to reduce the planning time and maintain consistent high‐quality plans. This paper presents a novel KBP model using an attention‐gating mechanism and a three‐dimensional (3D) U‐Net for intensity‐modulated radiation therapy (IMRT) 3D dose distribution prediction in head‐and‐neck cancer. Methods A total of 340 head‐and‐neck cancer plans, representing the OpenKBP—2020 AAPM Grand Challenge data set, were used in this study. All patients were treated with the IMRT technique and a dose prescription of 70 Gy. The data set was randomly divided into 64%/16%/20% as training/validation/testing cohorts. An attention‐gated 3D U‐Net architecture model was developed to predict full 3D dose distribution. The developed model was trained using the mean‐squared error loss function, Adam optimization algorithm, a learning rate of 0.001, 120 epochs, and batch size of 4. In addition, a baseline U‐Net model was also similarly trained for comparison. The model performance was evaluated on the testing data set by comparing the generated dose distributions against the ground‐truth dose distributions using dose statistics and clinical dosimetric indices. Its performance was also compared to the baseline model and the reported results of other deep learning‐based dose prediction models. Results The proposed attention‐gated 3D U‐Net model showed high capability in accurately predicting 3D dose distributions that closely replicated the ground‐truth dose distributions of 68 plans in the test set. The average value of the mean absolute dose error was 2.972 ± 1.220 Gy (vs. 2.920 ± 1.476 Gy for a baseline U‐Net) in the brainstem, 4.243 ± 1.791 Gy (vs. 4.530 ± 2.295 Gy for a baseline U‐Net) in the left parotid, 4.622 ± 1.975 Gy (vs. 4.223 ± 1.816 Gy for a baseline U‐Net) in the right parotid, 3.346 ± 1.198 Gy (vs. 2.958 ± 0.888 Gy for a baseline U‐Net) in the spinal cord, 6.582 ± 3.748 Gy (vs. 5.114 ± 2.098 Gy for a baseline U‐Net) in the esophagus, 4.756 ± 1.560 Gy (vs. 4.992 ± 2.030 Gy for a baseline U‐Net) in the mandible, 4.501 ± 1.784 Gy (vs. 4.925 ± 2.347 Gy for a baseline U‐Net) in the larynx, 2.494 ± 0.953 Gy (vs. 2.648 ± 1.247 Gy for a baseline U‐Net) in the PTV_70, and 2.432 ± 2.272 Gy (vs. 2.811 ± 2.896 Gy for a baseline U‐Net) in the body contour. The average difference in predicting the D99 value for the targets (PTV_70, PTV_63, and PTV_56) was 2.50 ± 1.77 Gy. For the organs at risk, the average difference in predicting the Dmax ${D_{max}}$(brainstem, spinal cord, and mandible) and Dmean ${D_{mean}}$(left parotid, right parotid, esophagus, and larynx) values was 1.43 ± 1.01 and 2.44 ± 1.73 Gy, respectively. The average value of the homogeneity index was 7.99 ± 1.45 for the predicted plans versus 5.74 ± 2.95 for the ground‐truth plans, whereas the average value of the conformity index was 0.63 ± 0.17 for the predicted plans versus 0.89 ± 0.19 for the ground‐truth plans. The proposed model needs less than 5 s to predict a full 3D dose distribution of 64 × 64 × 64 voxels for a new patient that is sufficient for real‐time applications. Conclusions The attention‐gated 3D U‐Net model demonstrated a capability in predicting accurate 3D dose distributions for head‐and‐neck IMRT plans with consistent quality. The prediction performance of the proposed model was overall superior to a baseline standard U‐Net model, and it was also competitive to the performance of the best state‐of‐the‐art dose prediction method reported in the literature. The proposed model could be used to obtain dose distributions for decision‐making before planning, quality assurance of planning, and guiding‐automated planning for improved plan consistency, quality, and planning efficiency.

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.

Purpose External beam radiotherapy for cancer treatment historically has faced the challenge of delivering sufficient dose to the target while minimizing dose to critical organs. Inverse planning techniques in modulated therapy can improve organ‐at‐risk (OAR) sparing but require significant human resources and can depend on planner experience. Advances in software and artificial intelligence (AI) have enabled the development of commercial treatment planning systems (TPS) with scripting and auto‐planning capabilities, potentially reducing human resource demands and standardizing plan quality. This study aimed to create and validate knowledge‐based planning (KBP) and multicriterial optimization (MCO) algorithms for assistive auto‐planning of prostate volumetric modulated arc therapy (VMAT) plans using the Elekta Monaco TPS. Methods Our methodology involved implementing and validating these algorithms retrospectively. We compared dose volume histogram (DVH), generalize equivalent uniform dose (gEUD), and plan quality scores with KBP and MCO algorithms to the clinical plans. Results KBP and MCO generated plans on average spared OARs better and on average had better conformity to the targets whilst not sacrificing target coverage. Additionally, algorithm generated plans could be produced within 30 min and were of clinical quality for 72% (KBP) and 78% (MCO) of plans. When comparing the KBP and MCO plans, MCO was dosimetrically slightly superior, slightly faster, and produced plans of clinical quality in more of the validation population than the KBP algorithm. Conclusion This work showed that it is possible to build KBP and MCO based assistive auto‐planning in the Elekta Monaco TPS focusing on gEUD based cost functions. When using the Monaco Scripting functionality, these algorithms could assist in reducing the clinical planning workload while maintaining a patient specific standard of quality.

Purpose Knowledge‐based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head‐and‐neck KBP models were trained to address these questions. Methods The six models differed in training size and plan composition: The KBPFull (n = 203 plans), KBP101 (n = 101), KBP50 (n = 50), and KBP25 (n = 25) were trained with plans from two head‐and‐neck physicians. KBPA and KBPB each contained n = 101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000–7000 cGy by a third physician was re‐planned with all KBP models for validation. Standard head‐and‐neck dosimetric parameters were used to compare resulting plans. KBPFull plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBPFull plans were presented to another physician for blind review. Dosimetric comparison of KBPFull against KBP101, KBP50, and KBP25 investigated the effect of model size. Finally, KBPA versus KBPB tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t‐test (p < 0.05). Results Compared to manual plans, KBPFull significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBPFull plan over the manual plan in 20/39 cases. Dosimetric differences between KBPFull, KBP101, KBP50, and KBP25 plans did not exceed 187 cGy on aggregate, except for the cochlea. Further, average differences between KBPA and KBPB were below 110 cGy. Conclusions Overall, all models were shown to produce high‐quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head‐and‐neck KBP models.

Purpose Swallowing dysfunction after radiotherapy (RT) is often linked to pharyngeal mucosal damage. This study aimed to develop a dysphagia‐optimized knowledge‐based planning (DO‐KBP) model by incorporating individual pharyngeal constrictors (DO‐KBP) into an existing model, which was based on a conventional approach of including pharynx as a single structure during treatment planning (P‐KBP). Materials and methods The P‐KBP model was trained on 175 head and neck cases with the pharynx contoured as a single organ. The DO‐KBP model included 36 additional oropharynx cases (∼20% increase) with individual pharyngeal constrictors delineated. Both models were evaluated on 25 test patients. Treatment plans generated by each model were normalized to planning‐target‐volume (PTV) coverage (D95%), and dosimetric parameters were compared using two‐tailed paired t‐tests. A blind physician review assessed clinical preference. Results The DO‐KBP model was able to significantly reduce the mean dose to the inferior constrictor (36.52 ± 9.87 Gy to 19.52 ± 6.23 Gy) and superior/middle constrictors (51.89 ± 6.31 Gy to 47.46 ± 6.12 Gy) (p < 0.05) with the addition of 36 high‐quality treatment plans. Though statistically significant increases in mean dose were observed for the spinal cord PRV (D0.03cc), cochlea, mandible, and left brachial plexus with the DO‐KBP model, these differences were small in magnitude and remained within clinical goals. However, these differences were small in magnitude and remained within clinical goals. Plan homogeneity was equivalent (HI = 0.09). The DO‐KBP plans were preferred in blinded review. Conclusion Targeted addition of a small number of cases with individually contoured constrictors to an existing model significantly improved sparing of swallowing structures, without compromising overall plan quality or increasing organs‐at‐risk (OAR) doses beyond clinical thresholds, highlighting that modest, focused data augmentation can yield clinically meaningful gains.

Purpose Knowledge‐based planning (KBP) offers the ability to predict dose‐volume metrics based on information extracted from previous plans, reducing plan variability and improving plan quality. As clinical integration of KBP is increasing there is a growing need for quantitative evaluation of KBP models. A .NET‐based application, RapidCompare, was created for automated plan creation and analysis of Varian RapidPlan models. Methods RapidCompare was designed to read calculation parameters and a list of reference plans. The tool copies the reference plan field geometry and structure set, applies the RapidPlan model, optimizes the KBP plan, and generates data for quantitative evaluation of dose‐volume metrics. A cohort of 85 patients, divided into training (50), testing (10), and validation (25) groups, was used to demonstrate the utility of RapidCompare. After training and tuning, the KBP model was paired with three different optimization templates to compare various planning strategies in the validation cohort. All templates used the same set of constraints for the planning target volume (PTV). For organs‐at‐risk, the optimization template provided constraints using the whole dose‐volume histogram (DVH), fixed‐dose/volume points, or generalized equivalent uniform dose (gEUD). The resulting plans from each optimization approach were compared using DVH metrics. Results RapidCompare allowed for the automated generation of 75 total plans for comparison with limited manual intervention. In comparing optimization techniques, the Dose/Volume and Lines optimization templates generated plans with similar DVH metrics, with a slight preference for the Lines technique with reductions in heart V30Gy and spinal cord max dose. The gEUD model produced high target heterogeneity. Conclusion Automated evaluation allowed for the exploration of multiple optimization templates in a larger validation cohort than would have been feasible using a manual approach. A final KBP model using line optimization objectives produced the highest quality plans without human intervention.

Purpose To investigate the performance of a model‐based optimization process for volumetric modulated arc therapy (VMAT) applied to prostate cancer patients with the multi‐planner. Methods and Materials The 120 prostate plans for VMAT treatment were entered into the database system of the RapidPlan (RP) knowledge‐based treatment planning. The treatment planning data for each plan was used to create and train the RP model. Twelve prostate cancer cases were selected and were used for planning by a manual of 12 planners based on the clinical protocol for dose constraints. Then, the treatment plans for each patient were compared with the RP model plans and analyzed with Wilcoxon tests. Results On average, the RP models can estimate comparable doses among all planner plans and clinical plans for the PTV, which Dmax, D95%, D98%, HI, and CI were used to evaluate. For the normal organ doses of the bladder, rectum, penile bulb, and femoral head, all RP model plans showed comparable or better dose sparing than all planner plans and clinical plans. Moreover, the average planning time of the RP model was faster than manual plans by about two times. The RP model can significantly reduce the variation dose of the normal organs compared with the manual plans among the planners. Conclusion The automated plans of the RP model might benefit from further fine‐tuning of the dose constraints of the normal organs, although both procedure plans are acceptable and fulfill the clinical protocol goals so that the RP model can enhance the efficacy and quality of plans.

Knowledge‐based planning (KBP) can be used to estimate dose–volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not properly addressed. This work used RapidPlan™ to create models for the prostate and head and neck intended for large‐scale distribution. Potential outlier plans were identified by means of regression analysis scatter plots, Cook's distance, coefficient of determination, and the chi‐squared test. Outlier plans were identified as falling into three categories: geometric, dosimetric, and over‐fitting outliers. The models were validated by comparing DVHs estimated by the model with those from a separate and independent set of clinical plans. The estimated DVHs were also used as optimization objectives during inverse planning. The analysis tools lead us to identify as many as 7 geometric, 8 dosimetric, and 20 over‐fitting outliers in the raw models. Geometric and over‐fitting outliers were removed while the dosimetric outliers were replaced after re‐planning. Model validation was done by comparing the DVHs at 50%, 85%, and 99% of the maximum dose for each OAR (denoted as V50, V85, and V99) and agreed within −2% to 4% for the three metrics for the final prostate model. In terms of the head and neck model, the estimated DVHs agreed from −2.0% to 5.1% at V50, 0.1% to 7.1% at V85, and 0.1% to 7.6% at V99. The process used to create these models improved the accuracy for the pharyngeal constrictor DVH estimation where one plan was originally over‐estimated by more than twice. In conclusion, our results demonstrate that KBP models should be carefully created since their accuracy could be negatively affected by outlier plans. Outlier plans can be addressed by removing them from the model and re‐planning.