RapidPlan head and neck model: the objectives and possible clinical benefit

Determining the library size for the optimal output plan in the RapidPlan knowledge-based planning system using multicriteria optimization

OBJECTIVES

The aim of this study was to determine the number of trade-off explored (TO) library plans required for building a RapidPlan (RP) library that would generate the optimal clinical treatment plan.

METHODS

We developed 2 RP models, 1 each for the 2 clinical sites, head and neck (HN) and cervix. The models were created using 100 plans and were validated using 70 plans (VP) for each site respectively. Each of the 2 libraries comprising 100 TO plans was divided into 5 different subsets of library plans comprising 20, 40, 60, 80, and 100 plans, leading to 5 different RP models for each site. For every validation patient, a TO plan (TO_VP) was created. For every patient, 5 RP plans were automatically generated using RP models. The dosimetric parameters of the 6 plans (TO_VP + 5 RP plans) were compared using Pearson correlation and Greenhouse-Geisser analysis.

RESULTS

Planning target volume (PTV) dose volume parameters PTVD95% in 6 competing plans varied between 97.6 ± 0.7% and 98.1 ± 0.6% in HN cases and 98.8 ± 0.3% and 99.0 ± 0.4% in cervix cases. Overall, for both sites, the mean variations in organ at risk (OAR) doses or volumes were within 50 cGy, 0.5%, and 0.2 cc between library plans, and if TO_VP was included the variations deteriorated to 180 cGy, 0.4%, and 15 cc. All OARs in both sites, except D0.1 ccspine, showed a statistically insignificant variation between all plans.

CONCLUSIONS

Dosimetric variation among various output plans generated from 5 RP libraries is minimal and clinically insignificant. The optimal output plan can be derived from the least-weighted library consisting of 20 plans.

ADVANCES IN KNOWLEDGE

This article shows that, when the constituent plans are subjected to trade-off exploration, the number of constituent plans for a knowledge-based planning module is not relevant in terms of its dosimetric output.

Dosimetry and efficiency comparison of knowledge-based and manual planning using volumetric modulated arc therapy for craniospinal irradiation

BACKGROUND

Craniospinal irradiation (CSI) poses a challenge to treatment planning due to the large target, field junction, and multiple organs at risk (OARs) involved. The aim of this study was to evaluate the performance of knowledge-based planning (KBP) in CSI by comparing original manual plans (MP), KBP RapidPlan initial plans (RPI), and KBP RapidPlan final plans (RPF), which received further re-optimization to meet the dose constraints.

PATIENTS AND METHODS

Dose distributions in the target were evaluated in terms of coverage, mean dose, conformity index (CI), and homogeneity index (HI). The dosimetric results of OARs, planning time, and monitor unit (MU) were evaluated.

RESULTS

All MP and RPF plans met the plan goals, and 89.36% of RPI plans met the plan goals. The Wilcoxon tests showed comparable target coverage, CI, and HI for the MP and RPF groups; however, worst plan quality was demonstrated in the RPI plans than in MP and RPF. For the OARs, RPF and RPI groups had better dosimetric results than the MP group (P < 0.05 for optic nerves, eyes, parotid glands, and heart). The planning time was significantly reduced by the KBP from an average of 677.80 min in MP to 227.66 min (P < 0.05) and 307.76 min (P < 0.05) in RPI, and RPF, respectively. MU was not significantly different between these three groups.

CONCLUSIONS

The KBP can significantly reduce planning time in CSI. Manual re-optimization after the initial KBP is recommended to enhance the plan quality.

Development and Validation of Single-Optimization Knowledge-Based Volumetric Modulated Arc Therapy Model Plan in Nasopharyngeal Carcinomas

Purpose

Knowledge-based planning (KBP) has evolved to standardize and expedite the complex process of radiation therapy planning for nasopharyngeal cancer (NPC). Herein, we aim to develop and validate the suitability of a single-optimization KBP for NPC.

Methods and Materials

Volumetric modulated arc therapy plans of 103 patients with NPC treated between 2016 and 2020 were reviewed and used to generate a KBP model. A validation set of 15 patients was employed to compare the quality of single optimization KBP and clinical plans using the paired t test and the Wilcoxon signed rank test. The time required for either planning was also analyzed.

Results

Most patients (86.7%) were of locally advanced stage (III/IV). The median dose received by 95% of the high-risk planning target volume was significantly higher for the KBP (97.1% vs 96.4%; P = .017). The median homogeneity (0.09 vs 0.1) and conformity (0.98 vs 0.97) indices for high-risk planning target volume and sparing of the normal tissues like optic structures, spinal cord, and uninvolved dysphagia and aspiration-related structures were better with the KBP (P < .05). In the blinded evaluation, the physician preferred the KBP plan in 13 out of 15 patients. The median time required to generate the KBP and manual plans was 53 and 77 minutes, respectively.

Conclusions

KBP with a single optimization is an efficient and time saving alternative for manual planning in NPC.

Improving knowledge-based treatment planning for lung cancer radiotherapy with automatic multi-criteria optimized training plans

BACKGROUND

Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans.

MATERIAL AND METHODS

Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans.

RESULTS

The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients.

CONCLUSION

RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.

Knowledge-based plan optimization for prostate SBRT delivered with CyberKnife according to RTOG0938 protocol

PURPOSE

To extend the knowledge-based (KB) automatic planning approach to CyberKnife in the case of Stereotactic Body Radiation Therapy (SBRT) for prostate cancer.

METHODS

Seventy-two clinical plans of patients treated according to the RTOG0938 protocol (36.25 Gy/5fr) with CyberKnife were exported from the CyberKnife system to Eclipse to train a KB-model using the Rapid Plan tool. The KB approach provided dose-volume objectives for specific OARs only and not PTV. Bladder, rectum and femoral heads were considered in the model. The KB-model was successfully trained on 51 plans and then validated on 20 new patients. A KB-based template was tuned in the Precision system for both sequential optimization (SO) and VOLO optimization algorithms. Plans of the validation group were re-optimized (KB-TP) using both algorithms without any operator intervention and compared against the original plans (TP) in terms of OARs/PTV dose-volume parameters. Paired Wilcoxon signed-rank tests were performed to assess statistically significant differences (p < 0.05).

RESULTS

Regarding SO, automatic KB-TP plans were generally better than or equivalent to TP plans. PTVs V95% was slightly worse while OARs sparing for KB-TP was significantly improved. Regarding VOLO optimization, the PTVs coverage was significantly better for KB-TP while there was a limited worsening in the rectum. A significant improvement was observed in the bladder in the range of low-intermediate doses.

CONCLUSIONS

An extension of the KB optimization approach to the CyberKnife system has been successfully developed and validated in the case of SBRT prostate cancer.

A novel automated planning approach for multi-anatomical sites cancer in Raystation treatment planning system

PURPOSE

To develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios.

METHODS

An automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared.

RESULTS

For target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA.

CONCLUSIONS

This study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.

Implementation, Dosimetric Assessment, and Treatment Validation of Knowledge-Based Planning (KBP) Models in VMAT Head and Neck Radiation Oncology

The aim of this study was to evaluate knowledge-based treatment planning (KBP) models in terms of their dosimetry and deliverability and to investigate their clinical benefits. Three H&N KBP models were built utilizing RapidPlan™, based on the dose prescription, which is given according to the planning target volume (PTV). The training set for each model consisted of 43 clinically acceptable volumetric modulated arc therapy (VMAT) plans. Model quality was assessed and compared to the delivered treatment plans using the homogeneity index (HI), conformity index (CI), structure dose difference (PTV, organ at risk—OAR), monitor units, MU factor, and complexity index. Model deliverability was assessed through a patient-specific quality assurance (PSQA) gamma index-based analysis. The dosimetric assessment showed better OAR sparing for the RapidPlan™ plans and for the low- and high-risk PTV, and the HI, and CI were comparable between the clinical and RapidPlan™ plans, while for the intermediate-risk PTV, CI was better for clinical plans. The 2D gamma passing rates for RapidPlan™ plans were similar or better than the clinical ones using the 3%/3 mm gamma-index criterion. Monitor units, the MU factors, and complexity indices were found to be comparable between RapidPlan™ and the clinical plans. Knowledge-based treatment plans can be safely adapted into clinical routines, providing improved plan quality in a time efficient way while minimizing user variability.

The effect of integrating knowledge-based planning with multicriteria optimization in treatment planning for prostate SBRT

Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.

Dosimetric predictors of toxicity in a randomized study of short-course vs conventional radiotherapy for glioblastoma

PURPOSE

There is no consensus on appropriate organ at risk (OAR) constraints for short-course radiotherapy for patients with glioblastoma. Using dosimetry and prospectively-collected toxicity data from a trial of short-course radiotherapy for glioblastoma, this study aims to empirically examine the OAR constraints, with particular attention to left hippocampus dosimetry and impact on neuro-cognitive decline.

METHODS AND MATERIALS

Data was taken from a randomized control trial of 133 adults (age 18-70 years; ECOG performance score 0-2) with newly diagnosed glioblastoma treated with 60 Gy in 30 (conventional arm) versus 20 (short-course arm) fractions of adjuvant chemoradiotherapy (ClinicalTrials.gov Identifier: NCT02206230). The delivered plan's dosimetry to the OARs was correlated to prospective-collected toxicity and Mini-Mental State Examination (MMSE) data.

RESULTS

Toxicity events were not significantly increased in the short-course arm versus the conventional arm. Across all OARs, delivered radiation doses within protocol-allowable maximum doses correlated with lack of grade ≥ 2 toxicities in both arms (p < 0.001), while patients with OAR doses at or above protocol limits correlated with increased grade ≥ 2 toxicities across all examined OARs in both arms (p-values 0.063-0.250). Mean left hippocampus dose was significantly associated with post-radiotherapy decline in MMSE scores (p = 0.005), while the right hippocampus mean dose did not reach statistical significance (p = 0.277). Compared to the original clinical plan, RapidPlan left hippocampus sparing model decreased left hippocampus mean dose by 43 % (p < 0.001), without compromising planning target volume coverage.

CONCLUSIONS

In this trial, protocol OAR constraints were appropriate for limiting grade ≥ 2 toxicities in conventional and short-course adjuvant chemoradiotherapy for glioblastoma. Higher left hippocampal mean doses were predictive for neuro-cognitive decline post-radiotherapy. Routine contouring and use of dose constraints to limit hippocampal dose is recommended to minimize neuro-cognitive decline in patients with glioblastoma treated with chemoradiotherapy.

A comparison of in-house and shared RapidPlan models for prostate radiation therapy planning

Knowledge-based planning (KBP) can increase plan quality, consistency and efficiency. In this study, we assess the success of a using a publicly available KBP model compared with developing an in-house model for prostate cancer radiotherapy using a single, commercially available treatment planning system based on the ability of the model to achieve the centre's planning goals. Two radiation oncology centres each created a prostate cancer KBP model using the Eclipse RapidPlan software. These two models and a third publicly-available, shared model were tested at three centres in a retrospective planning study. The publicly-available model achieved lower rectum doses than the other two models. However, the planning-target-volume (PTV) doses did not meet the local planning goals and the model could not be adjusted to correct this. As a result, the plans most likely to satisfy local planning goals and requirements were created using an in-house model. For centres without an existing in-house model, a model created by another centre with similar planning goals was found to be preferred. Variations in local planning practices including contouring, treatment technique and planning goals can influence the relative performance of KBP. The value of publicly available KBP models could be enhanced through standardisation of planning goals and contouring guidelines, providing information related to the planning goals used to create the model and increased flexibility to allow local adaptation of the KBP model.

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

PURPOSE

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).

METHODS AND MATERIALS

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 (KBP_OAR and KBP_OAR+PTV) were established for plan generation based on the DVH prediction. The KBP_OAR method utilized the dose constraints based on the predicted OAR DVH and the PTV dose constraints obtained according to the planning experience, while the KBP_OAR+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.

RESULTS

For patients with T3 tumors, there was no significant difference between the KBP_OAR and KBP_OAR+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, KBP_OAR+PTV reduced the maximum dose by more than 1 Gy 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 KBP_OAR. The KBP_OAR+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 KBP_OAR method.

CONCLUSIONS

For patients with T3 tumors, no significant difference was found between the KBP_OAR and KBP_OAR+PTV methods in dosimetric results at most OARs and PTVs. For patients with T4 tumors, the KBP_OAR+PTV method performs better than the KBP_OAR method in improving the quality of the plans. Compared with the KBP_OAR 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 KBP_OAR+PTV method was utilized.

Dosimetric comparison between VMAT plans using the fast-rotating O-ring linac with dual-layer stacked MLC and helical tomotherapy for nasopharyngeal carcinoma

BACKGROUND

To compare the dosimetric profiles of volumetric modulated arc therapy (VMAT) plans using the fast-rotating O-ring linac (the Halcyon system) based on a dual-layer stacked multi-leaf collimator and helical tomotherapy (HT) for nasopharyngeal cancer (NPCa).

METHODS

For 30 NPCa patients, three sets of RT plans were generated, under the same policy of contouring and dose constraints: HT plan; Halcyon VMAT plan with two arcs (HL_2arc); and Halcyon VMAT plan with four arcs (HL_4arc), respectively. The intended dose schedule was to deliver 67.2 Gy to the planning gross target volume (P-GTV) and 56.0 Gy to the planning clinical target volume (P-CTV) in 28 fractions using the simultaneously integrated boost concept. Target volumes and organ at risks dose metrics were evaluated for all plans. Normal tissue complication probabilities (NTCP) for esophagus, parotid glands, spinal cord, and brain stem were compared.

RESULTS

The HT plan achieved the best dose homogeneity index for both P_GTV and P_CTV, followed by the HL_4arc and L_2arc plans. No significant difference in the dose conformity index (CI) for P_GTV was observed between the HT plan (0.80) and either the HL_2arc plan (0.79) or the HL_4arc plan (0.83). The HL_4arc plan showed the best CI for P_CTV (0.88), followed by the HL_2arc plan (0.83) and the HT plan (0.80). The HL_4arc plan (median, interquartile rage (Q1, Q3): 25.36 (22.22, 26.89) Gy) showed the lowest D_mean in the parotid glands, followed by the HT (25.88 (23.87, 27.87) Gy) and HL_2arc plans (28.00 (23.24, 33.99) Gy). In the oral cavity (OC) dose comparison, the HT (22.03 (19.79, 24.85) Gy) plan showed the lowest D_mean compared to the HL_2arc (23.96 (20.84, 28.02) Gy) and HL_4arc (24.14 (20.17, 27.53) Gy) plans. Intermediate and low dose regions (40-65% of the prescribed dose) were well fit to the target volume in HL_4arc, compared to the HT and HL_2arc plans. All plans met the dose constraints for the other OARs with sufficient dose margins. The between-group differences in the median NTCP values for the parotid glands and OC were < 3.47% and < 1.7% points, respectively.

CONCLUSIONS

The dosimetric profiles of Halcyon VMAT plans were comparable to that of HT, and HL_4arc showed better dosimetric profiles than HL_2arc for NPCa.

Development and Clinical Implementation of an Automated Virtual Integrative Planner for Radiation Therapy of Head and Neck Cancer

Purpose

Head and neck (HN) radiation (RT) treatment planning is complex and resource intensive. Deviations and inconsistent plan quality significantly affect clinical outcomes. We sought to develop a novel automated virtual integrative (AVI) knowledge-based planning application to reduce planning time, increase consistency, and improve baseline quality.

Methods and Materials

An in-house write-enabled script was developed from a library of 668 previously treated HN RT plans. Prospective hazard analysis was performed, and mitigation strategies were implemented before clinical release. The AVI-planner software was retrospectively validated in a cohort of 52 recent HN cases. A physician panel evaluated planning limitations during initial deployment, and feedback was enacted via software refinements. A final second set of plans was generated and evaluated. Kolmogorov-Smirnov test in addition to generalized evaluation metric and weighted experience score were used to compare normal tissue sparing between final AVI planner versus respective clinically treated and historically accepted plans. A t test was used to compare the interactive time, complexity, and monitor units for AVI planner versus manual optimization.

Results

Initially, 86% of plans were acceptable to treat, with 10% minor and 4% major revisions or rejection recommended. Variability was noted in plan quality among HN subsites, with high initial quality for oropharynx and oral cavity plans. Plans needing revisions were comprised of sinonasal, nasopharynx, P-16 negative squamous cell carcinoma unknown primary, or cutaneous primary sites. Normal tissue sparing varied within subsites, but AVI planner significantly lowered mean larynx dose (median, 18.5 vs 19.7 Gy; P < .01) compared with clinical plans. AVI planner significantly reduced interactive optimization time (mean, 2 vs 85 minutes; P < .01).

Conclusions

AVI planner reliably generated clinically acceptable RT plans for oral cavity, salivary, oropharynx, larynx, and hypopharynx cancers. Physician-driven iterative learning processes resulted in favorable evolution in HN RT plan quality with significant time savings and improved consistency using AVI planner.

Normal tissue objective (NTO) tool in Eclipse treatment planning system for dose distribution optimization

Introduction: The purpose of this study was to determine the best normal tissue objective (NTO) values based on the dose distribution from brain tumor radiation therapy.

Material and methods: The NTO is a constraint provided by Eclipse to limit the dose to normal tissues by steepening the dose gradient. The multitude of NTO setting combinations necessitates optimal NTO settings. The Eclipse supports manual and automatic NTOs. Fifteen patients were re-planned using NTO priorities of 1, 50, 100, 150, 200, and 500 in combination with dose fall-offs of 0.05, 0.1, 0.2, 0.3, 0.5, 1 and 5 mm-1. NTO distance to planning target volume (PTV), start dose, and end dose were 1 mm, 105%, and 60%, respectively, for all plans. In addition, planning without the NTO was arranged to find out its effect on planning. The prescription dose covered 95% of the PTV. Planning was evaluated using several indices: conformity index (CI), homogeneity index (HI), gradient index (GI), modified gradient index (mGI), comprehensive quality index (CQI), and monitor unit (MU). Differences among automatic NTO, manual NTO, and without NTO were evaluated using the Wilcoxon signed-rank test.

Results: Comparisons obtained without and with manual NTO were: CI of 0.77 vs. 0.96 (p = 0.002), GI of 4.52 vs. 4.69 (p = 0.233), mGI of 4.93 vs. 3.95 (p = 0.001), HI of 1.10 vs. 1.10 (p = 0.330), and MU/cGy of 3.44 vs. 3.42 (p = 0.460). Planning without NTO produced a poor conformity index. Comparisons of automatic and manual NTOs were: CI of 0.92 vs. 0.96 (p = 0.035), GI of 5.25 vs. 4.69 (p = 0.253), mGI of 4.46 vs. 3.95 (p = 0.001), HI of 1.09 vs. 1.10 (p = 0.004), MU/cGy of 3.31 vs. 3.42 (p = 0.041).

Conclusions: Based on these results, manual NTO with a priority of 100 and dose fall-off 0.5 mm-1 was optimal, as indicated by the high dose reduction in normal tissue.

Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists

PURPOSE

Little is known about the scale of clinical implementation of automated treatment planning techniques in the United States. In this work, we examine the barriers and facilitators to adoption of commercially available automated planning tools into the clinical workflow using a survey of medical dosimetrists.

METHODS/MATERIALS

Survey questions were developed based on a literature review of automation research and cognitive interviews of medical dosimetrists at our institution. Treatment planning automation was defined to include auto-contouring and automated treatment planning. Survey questions probed frequency of use, positive and negative perceptions, potential implementation changes, and demographic and institutional descriptive statistics. The survey sample was identified using both a LinkedIn search and referral requests sent to physics directors and senior physicists at 34 radiotherapy clinics in our state. The survey was active from August 2020 to April 2021.

RESULTS

Thirty-four responses were collected out of 59 surveys sent. Three categories of barriers to use of automation were identified. The first related to perceptions of limited accuracy and usability of the algorithms. Eighty-eight percent of respondents reported that auto-contouring inaccuracy limited its use, and 62% thought it was difficult to modify an automated plan, thus limiting its usefulness. The second barrier relates to the perception that automation increases the probability of an error reaching the patient. Third, respondents were concerned that automation will make their jobs less satisfying and less secure. Large majorities reported that they enjoyed plan optimization, would not want to lose that part of their job, and expressed explicit job security fears.

CONCLUSION

To our knowledge this is the first systematic investigation into the views of automation by medical dosimetrists. Potential barriers and facilitators to use were explicitly identified. This investigation highlights several concrete approaches that could potentially increase the translation of automation into the clinic, along with areas of needed research.

The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer

Purpose

To develop a 3D‐Unet dose prediction model to predict the three‐dimensional dose distribution of volumetric modulated arc therapy (VMAT) for cervical cancer and test the dose prediction performance of the model in endometrial cancer to explore the feasibility of model generalization.

Methods

One hundred and seventeen cases of cervical cancer and 20 cases of endometrial cancer treated with VMAT were used for the model training, validation, and test. The prescribed dose was 50.4 Gy in 28 fractions. Eight independent channels of contoured structures were input to the model, and the dose distribution was used as the output of the model. The 3D‐Unet prediction model was trained and validated on the training set (n = 86) and validation set (n = 11), respectively. Then the model was tested on the test set (n = 20) of cervical cancer and endometrial cancer, respectively. The results between clinical dose distribution and predicted dose distribution were compared in the following aspects: (a) the mean absolute error (MAE) within the body, (b) the Dice similarity coefficients (DSCs) under different isodose volumes, (c) the dosimetric indexes including the mean dose (D_mean), the received dose of 2 cm³ (D_2cc), the percentage volume of receiving 40 Gy dose of organs‐at‐risk (V₄₀), planning target volume (PTV) D_98%, and homogeneity index (HI), (d) dose–volume histograms (DVHs).

Results

The model can accurately predict the dose distribution of the VMAT plan for cervical cancer and endometrial cancer. The overall average MAE and maximum MAE for cervical cancer were 2.43 ± 3.17% and 3.16 ± 4.01% of the prescribed dose, respectively, and for endometrial cancer were 2.70 ± 3.54% and 3.85 ± 3.11%. The average DSCs under different isodose volumes is above 0.9. The predicted dosimetric indexes and DVHs are equivalent to the clinical dose for both cervical cancer and endometrial cancer, and there is no statistically significant difference.

Conclusion

A 3D‐Unet dose prediction model was developed for VMAT of cervical cancer, which can predict the dose distribution accurately for cervical cancer. The model can also be generalized for endometrial cancer with good performance.

Plan quality in radiotherapy treatment planning - Review of the factors and challenges

A high-quality treatment plan aims to best achieve the clinical prescription, balancing high target dose to maximise tumour control against sufficiently low organ-at-risk dose for acceptably low toxicity. Treatment planning (TP) includes multiple steps from simulation/imaging and segmentation to technical plan production and reporting. Consistent quality across this process requires close collaboration and communication between clinical and technical experts, to clearly understand clinical requirements and priorities and also practical uncertainties, limitations and compromises. TP quality depends on many aspects, starting from commissioning and quality management of the treatment planning system (TPS), including its measured input data and detailed understanding of TPS models and limitations. It requires rigorous quality assurance of the whole planning process and it links to plan deliverability, assessable by measurement-based verification. This review highlights some factors influencing plan quality, for consideration for optimal plan construction and hence optimal outcomes for each patient. It also indicates some challenges, sources of difference and current developments. The topics considered include: the evolution of TP techniques; dose prescription issues; tools and methods to evaluate plan quality; and some aspects of practical TP. The understanding of what constitutes a high-quality treatment plan continues to evolve with new techniques, delivery methods and related evidence-based science. This review summarises the current position, noting developments in the concept and the need for further robust tools to help achieve it.

Implementation of automatic plan optimization in Italy: Status and perspectives

PURPOSE

To investigate and report on the diffusion and clinical use of automated radiotherapy planning systems in Italy and to assess the perspectives of the community of Italian medical physicists involved in radiotherapy on the use of these tools.

MATERIALS AND METHODS

A survey of medical physicists (one per Institute) of 175 radiotherapy centers in Italy was conducted between February 21st and April 1st, 2021. The information collected included the institute's characteristics, plan activity, availability/use of automatic tools and related issues regarding satisfaction, criticisms, expectations, and perceived professional modifications. Responses were analysed, including the impact of a few variables such as the institute type and experience.

RESULTS

125 of the centers (71%) answered the survey, with regional variability (range: 47%-100%); among these, 49% have a TPS with some automatic option. Clinical use of automatic planning is present in 33% of the centers, with 13% applying it in >50% of their plans. Among the 125 responding centres the most used systems are Pinnacle (16%), Raystation (9%) and Eclipse (4%). The majority of participants consider the use of automated techniques to be beneficial, while only 1% do not see any advantage; 83% of respondents see the possibility of enriching their professional role as a potential benefit, while 3% see potential threats.

CONCLUSIONS

Our survey shows that 49% of the responding centres have an automatic planning solution although clinically used in only 33% of the cases. Most physicists consider the use of automated techniques to be beneficial and show a prevalently positive attitude.

Knowledge-Based Planning for Robustly Optimized Intensity-Modulated Proton Therapy of Head and Neck Cancer Patients

PURPOSE

To assess the performance of a proton-specific knowledge-based planning (KBP) model in the creation of robustly optimized intensity-modulated proton therapy (IMPT) plans for treatment of advanced head and neck (HN) cancer patients.

METHODS

Seventy-three patients diagnosed with advanced HN cancer previously treated with volumetric modulated arc therapy (VMAT) were selected and replanned with robustly optimized IMPT. A proton-specific KBP model, RapidPlanPT (RPP), was generated using 53 patients (20 unilateral cases and 33 bilateral cases). The remaining 20 patients (10 unilateral and 10 bilateral cases) were used for model validation. The model was validated by comparing the target coverage and organ at risk (OAR) sparing in the RPP-generated IMPT plans with those in the expert plans. To account for the robustness of the plan, all uncertainty scenarios were included in the analysis.

RESULTS

All the RPP plans generated were clinically acceptable. For unilateral cases, RPP plans had higher CTV_primary V100 (1.59% ± 1.24%) but higher homogeneity index (HI) (0.7 ± 0.73) than had the expert plans. In addition, the RPP plans had better ipsilateral cochlea Dmean (-5.76 ± 6.11 Gy), with marginal to no significant difference between RPP plans and expert plans for all other OAR dosimetric indices. For the bilateral cases, the V100 for all clinical target volumes (CTVs) was higher for the RPP plans than for the expert plans, especially the CTV_primary V100 (5.08% ± 3.02%), with no significant difference in the HI. With respect to OAR sparing, RPP plans had a lower spinal cord Dmax (-5.74 ± 5.72 Gy), lower cochlea Dmean (left, -6.05 ± 4.33 Gy; right, -4.84 ± 4.66 Gy), lower left and right parotid V20Gy (left, -6.45% ± 5.32%; right, -6.92% ± 3.45%), and a lower integral dose (-0.19 ± 0.19 Gy). However, RPP plans increased the Dmax in the body outside of CTV (body-CTV) (1.2 ± 1.43 Gy), indicating a slightly higher hotspot produced by the RPP plans.

CONCLUSION

IMPT plans generated by a broad-scope RPP model have a quality that is, at minimum, comparable with, and at times superior to, that of the expert plans. The RPP plans demonstrated a greater robustness for CTV coverage and better sparing for several OARs.

Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer

Purpose:

To create and to validate knowledge-based volumetric modulated arc therapy (VMAT) models for breast cancer treatments without lymph node irradiation.

Materials and Methods:

One hundred VMAT-based breast plans (manual plans [MP]) were selected to create two knowledge-based VMAT models (breast left and breast right) using RapidPlan™. The plans were generated on Eclipse v15.5 (Varian Medical Systems, Palo Alto, CA) with 6 MV of a Novalis Tx equipped with a high-resolution multileaf collimator. The models were verified based on goodness-of-fit statistics using the coefficients of determination (R²) and Chi-square (χ²), and the goodness-of-estimation statistics through the mean square error (MSE). Geometrical and dosimetrical constraints were identified and removed from the RP models using statistical evaluation metrics and plots. For validation, 20 plans that integrate the models and 20 plans that do not were reoptimized with RP (closed and opened validation). Dosimetrical parameters of interest were used to compare MP versus RP plans for the Heart, Homolateral_Lung, Contralateral_Lung, and Contralateral_Breast. Optimization planning time and user independency were also analyzed.

Results:

The most unfavorable results of R² in both models for the organs at risk were as follows: for Contralateral_Lung 0.51 in RP right breast (RP_RB) and for Heart 0.60 in RP left breast (RP_LB). The most unfavorable results of χ² test were: for Contralateral_Breast 1.02 in RP_RB and for Heart 1.03 in RP_LB. These goodness-of-fit results show that no overfitting occurred in either of the models. There were no unfavorable results of mean square error (MSE, all < 0.05) in any of the two models. These goodness-of-estimation results show that the models have good estimation power. For closed validation, significant differences were found in RP_RB for Homolateral_Lung (all P ≤ 0.001), and in the RP_LB differences were found for the heart (all P ≤ 0.04) and for Homolateral_Lung (all P ≤ 0.022). For open validation, no statistically significant differences were obtained in either of the models. RP models had little impact on reducing optimization planning times for expert planners; nevertheless, the result showed a 30% reduction time for beginner planners. The use of RP models generates high-quality plans, without differences from the planner experience.

Conclusion:

Two RP models for breast cancer treatment using VMAT were successfully implemented. The use of RP models for breast cancer reduces the optimization planning time and improves the efficiency of the treatment planning process while ensuring high-quality plans.

Deep learning method for prediction of patient-specific dose distribution in breast cancer

Background

Patient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™.

Methods

Patient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction.

Results

The mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, − 0.88 ± 1.83%, − 1.16 ± 2.58%, and − 0.97 ± 1.73% for D_95%, D_mean in the PTV, and the OARs of the body, left breast, heart, left lung, and right lung, respectively, and those measured between the clinical and RapidPlan dose prediction models were 0.02 ± 0.14%, 0.87 ± 0.63%, − 0.29 ± 0.98%, 1.30 ± 0.86%, − 0.32 ± 1.10%, 0.12 ± 2.13%, and − 1.74 ± 1.79, respectively.

Conclusions

In this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.

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.

Use of knowledge based DVH predictions to enhance automated re-planning strategies in head and neck adaptive radiotherapy

This study aimed to investigate if a commercial, knowledge-based tool for radiotherapy planning could be used to estimate the amount of sparing in organs at risk (OARs) in the re-planning strategy for adaptive radiotherapy (ART). Eighty head and neck (HN) VMAT Pareto plans from our institute's database were used to train a knowledge-based planning (KBP) model. An evaluation set of another 20 HN patients was randomly selected. For each patient in the evaluation set, the planning computed tomography (CT) and 2 sets of on-board cone-beam CT, corresponding to the middle and second half of the radiotherapy treatment course, were extracted. The original plan was re-calculated on a daily deformed CT (delivered dose-volume histogram (DVH)) and compared with the KBP DVH predictions and with the final KBP DVH after optimisation of the plan, which was performed on the same image sets. To evaluate the feasibility of this method, the range of KBP DVH uncertainties was compared with the gains obtained from re-planning. DVH differences and receiver operating characteristic (ROC) curve analysis were used for this purpose. On average, final KBP uncertainties were smaller than the gain in re-planning. Statistical tests confirmed significant differences between the two groups. ROC analysis showed KBP performance in terms of area under the curve values higher than 0.7, which confirmed a good accuracy in predicted values. Overall, for 48% of cases, KBP predicted a desirable outcome from re-planning, and the final dose confirmed an effective gain in 47% of cases. We have established a systematic workflow to identify effective OAR sparing in re-planning based on KBP predictions that can be implemented in an on-line, ART process.

Evaluation of fully automated a priori MCO treatment planning in VMAT for head-and-neck cancer

PURPOSE

Automated planning techniques aim to reduce manual planning time and inter-operator variability without compromising the plan quality which is particularly challenging for head-and-neck (HN) cancer radiotherapy. The objective of this study was to evaluate the performance of an a priori-multicriteria plan optimization algorithm on a cohort of HN patients.

METHODS

A total of 14 nasopharyngeal carcinoma (upper-HN) and 14 "middle-lower indications" (lower-HN) previously treated in our institution were enrolled in this study. Automatically generated plans (autoVMAT) were compared to manual VMAT or Helical Tomotherapy planning (manVMAT-HT) by assessing differences in dose delivered to targets and organs at risk (OARs), calculating plan quality indexes (PQIs) and performing blinded comparisons by clinicians. Quality control of the plans and measurements of the delivery times were also performed.

RESULTS

For the 14 lower-HN patients, with equivalent planning target volume (PTV) dosimetric criteria and dose homogeneity, significant decrease in the mean doses to the oral cavity, esophagus, trachea and larynx were observed for autoVMAT compared to manVMAT-HT. Regarding the 14 upper-HN cases, the PTV coverage was generally significantly superior for autoVMAT which was also confirmed with higher calculated PQIs on PTVs for 13 out of 14 patients, whereas PQIs calculated on OARs were generally equivalent. Number of MUs and total delivery time were significantly higher for autoVMAT compared to manVMAT. All plans were considered clinically acceptable by clinicians.

CONCLUSIONS

Overall superiority of autoVMAT compared to manVMAT-HT plans was demonstrated for HN cancer. The obtained plans were operator-independent and required no post-optimization or manual intervention.

Optimal beam angle selection and knowledge-based planning significantly reduces radiotherapy dose to organs at risk for lung cancer patients

BACKGROUND

Lung cancer patients struggle with high toxicity rates. This study investigates if IMRT plans with individually set beam angles or uni-lateral VMAT plans results in dose reduction to OARs. We investigate if introduction of a RapidPlan model leads to reduced dose to OARs. Finally, the model is validated prospectively.

MATERIAL AND METHODS

Seventy-four consecutive lung cancer patients treated with IMRT were included. For all patients, new IMRT plans were made by an experienced dose planner re-tuning beam angles aiming for minimized dose to the lungs and heart. Additionally, VMAT plans were made. The IMRT plans were selected as input for a RapidPlan model, which was used to generate 74 new IMRT plans. The new IMRT plans were used as input for a second RapidPlan model. This model was clinically implemented and used for generation of clinical treatment plans. Dosimetric parameters were compared using a Wilcoxon signed rank test or a 1-sided student's t-test. p < .05 was considered significant.

RESULTS

IMRT plans significantly reduced mean doses to lungs (MLD) and heart (MHD) by 1.6 Gy and 1.7 Gy in mean compared to VMAT plans. MLD was significantly (p < .001) reduced from 10.8 Gy to 9.4 Gy by using the second RapidPlan model. MHD was significantly (p < .001) reduced from 4.9 Gy to 3.9 Gy. The model was validated in prospectively collected treatment plans showing significantly lower MLD after the implementation of the second RapidPlan model.

CONCLUSION

Introduction of RapidPlan and beam angles selected based on the target and OARs position reduces dose to OARs.

Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments

Purpose

To develop a knowledge‐based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT).

Methods

Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (r_{eq_PTV}) and the equivalent radius of volume that receives a set of prescription dose (r_{eq_Vi}, where V_i = V_10%, V_20% … V_120%). To evaluate the model’s predictability, a fourfold cross‐validation was performed for dosimetric indices such as gradient measure (GM) and brain V_50%. The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., ΔGM = GM_pred − GM_clin and fractional ΔV_50% = (V_50%pred − V_50%clin)/V_50%clin) and a coefficient of determination, R². Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency.

Results

Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross‐validation was only 0.36 ± 0.06 mm for ΔGM, and 0.12 ± 0.08 for ΔV_50%. The R² for the linear fit between r_{eq_PTV} and r_{eq_vi} was 0.985 ± 0.019 for isodose volumes ranging from V_10% to V_120%; particularly, R² = 0.995 for V_50% and R² = 0.997 for V_100%. Compared to the training plans, our KBP test plan nCI was improved from 1.31 ± 0.15 to 1.15 ± 0.08 (P < 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner’s intervention.

Conclusion

We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency.

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.

Evaluation of a generalized knowledge-based planning performance for VMAT irradiation of breast and locoregional lymph nodes-Internal mammary and/or supraclavicular regions

PURPOSE

To evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions.

METHODS AND MATERIALS

Six RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures.

RESULTS

All models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average.

CONCLUSIONS

All KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.

Development and clinical validation of a robust knowledge-based planning model for stereotactic body radiotherapy treatment of centrally located lung tumors

PURPOSE

To develop a robust and adaptable knowledge-based planning (KBP) model with commercially available RapidPlan^TM for early stage, centrally located non-small-cell lung tumors (NSCLC) treated with stereotactic body radiotherapy (SBRT) and improve a patient's"simulation to treatment" time.

METHODS

The KBP model was trained using 86 clinically treated high-quality non-coplanar volumetric modulated arc therapy (n-VMAT) lung SBRT plans with delivered prescriptions of 50 or 55 Gy in 5 fractions. Another 20 independent clinical n-VMAT plans were used for validation of the model. KBP and n-VMAT plans were compared via Radiation Therapy Oncology Group (RTOG)-0813 protocol compliance criteria for conformity (CI), gradient index (GI), maximal dose 2 cm away from the target in any direction (D2cm), dose to organs-at-risk (OAR), treatment delivery efficiency, and accuracy. KBP plans were re-optimized with larger calculation grid size (CGS) of 2.5 mm to assess feasibility of rapid adaptive re-planning.

RESULTS

Knowledge-based plans were similar or better than n-VMAT plans based on a range of target coverage and OAR metrics. Planning target volume (PTV) for validation cases was 30.5 ± 19.1 cc (range 7.0-71.7 cc). KBPs provided an average CI of 1.04 ± 0.04 (0.97-1.11) vs. n-VMAT plan'saverage CI of 1.01 ± 0.04 (0.97-1.17) (P < 0.05) with slightly improved GI with KBPs (P < 0.05). D2cm was similar between the KBPs and n-VMAT plans. KBPs provided lower lung V10Gy (P = 0.003), V20Gy (P = 0.007), and mean lung dose (P < 0.001). KBPs had overall better sparing of OAR at the minimal increased of average total monitor units and beam-on time by 460 (P < 0.05) and 19.2 s, respectively. Quality assurance phantom measurement showed similar treatment delivery accuracy. Utilizing a CGS of 2.5 mm in the final optimization improved planning time (mean, 5 min) with minimal or no cost to the plan quality.

CONCLUSION

The RTOG-compliant adaptable RapidPlan model for early stage SBRT treatment of centrally located lung tumors was developed. All plans met RTOG dosimetric requirements in less than 30 min of planning time, potentially offering shorter "simulation to treatment" times. OAR sparing via KBPs may permit tumorcidal dose escalation with minimal penalties. Same day adaptive re-planning is plausible with a 2.5-mm CGS optimizer setting.

An Automated knowledge-based planning routine for stereotactic body radiotherapy of peripheral lung tumors via DCA-based volumetric modulated arc therapy

Purpose

To develop a knowledge‐based planning (KBP) routine for stereotactic body radiotherapy (SBRT) of peripherally located early‐stage non‐small‐cell lung cancer (NSCLC) tumors via dynamic conformal arc (DCA)‐based volumetric modulated arc therapy (VMAT) using the commercially available RapidPlan^TM software. This proposed technique potentially improves plan quality, reduces complexity, and minimizes interplay effect and small‐field dosimetry errors associated with treatment delivery.

Methods

KBP model was developed and validated using 70 clinically treated high quality non‐coplanar VMAT lung SBRT plans for training and 20 independent plans for validation. All patients were treated with 54 Gy in three treatments. Additionally, a novel k‐DCA planning routine was deployed to create plans incorporating historical three‐dimensional‐conformal SBRT planning practices via DCA‐based approach prior to VMAT optimization in an automated planning engine. Conventional KBPs and k‐DCA plans were compared with clinically treated plans per RTOG‐0618 requirements for target conformity, tumor dose heterogeneity, intermediate dose fall‐off and organs‐at‐risk (OAR) sparing. Treatment planning time, treatment delivery efficiency, and accuracy were recorded.

Results

KBPs and k‐DCA plans were similar or better than clinical plans. Average planning target volume for validation was 22.4 ± 14.1 cc (7.1–62.3 cc). KBPs and k‐DCA plans provided similar conformity to clinical plans with average absolute differences of 0.01 and 0.01, respectively. Maximal doses to OAR were lowered in both KBPs and k‐DCA plans. KBPs increased monitor units (MU) on average 1316 (P < 0.001) while k‐DCA reduced total MU on average by 1114 (P < 0.001). This routine can create k‐DCA plan in less than 30 min. Independent Monte Carlo calculation demonstrated that k‐DCA plans showed better agreement with planned dose distribution.

Conclusion

A k‐DCA planning routine was developed in concurrence with a knowledge‐based approach for the treatment of peripherally located lung tumors. This method minimizes plan complexity associated with model‐based KBP techniques and improve plan quality and treatment planning efficiency.

Rolling out RapidPlan: What we've learnt

INTRODUCTION

RapidPlan (RP), a knowledge-based planning system, aims to consistently improve plan quality and efficiency in radiotherapy. During the early stages of implementation, some of the challenges include knowing how to optimally train a model and how to integrate RP into a department. We discuss our experience with the implementation of RP into our institution.

METHODS

We reviewed all patients planned using RP over a 7-month period following inception in our department. Our primary outcome was clinically acceptable plans (used for treatment) with secondary outcomes including model performance and a comparison of efficiency and plan quality between RP and manual planning (MP).

RESULTS

Between November 2017 and May 2018, 496 patients were simulated, of which 217 (43.8%) had an available model. RP successfully created a clinically acceptable plan in 87.2% of eligible patients. The individual success of the 24 models ranged from 50% to 100%, with more than 90% success in 15 (62.5%) of the models. In 40% of plans, success was achieved on the 1st optimisation. The overall planning time with RP was reduced by up to 95% compared with MP times. The quality of the RP plans was at least equivalent to historical MP plans in terms of target coverage and organ at risk constraints.

CONCLUSION

While initially time-consuming and resource-intensive to implement, plans optimised with RP demonstrate clinically acceptable plan quality, while significantly improving the efficiency of a department, suggesting RP and its application is a highly effective tool in clinical practice.

Clinical Implementation of Knowledge-Based Automatic Plan Optimization for Helical Tomotherapy

PURPOSE

To implement knowledge-based (KB) automatic planning for helical TomoTherapy (HTT). The focus of the first clinical implementation was the case of high-risk prostate cancer, including pelvic node irradiation.

METHODS AND MATERIALS

One hundred two HTT clinical plans were selected to train a KB model using the RapidPlan tool incorporated in the Eclipse system (v13.6, Varian Inc). The individually optimized KB-based templates were converted into HTT-like templates and sent automatically to the HTT treatment planning system through scripting. The full dose calculation was set after 300 iterations without any additional planner intervention. Internal (20 patients in the training cohort) and external (28 new patients) validation were performed to assess the performance of the model: Automatic HTT plans (KB-TP) were compared against the original plans (TP) in terms of organs at risk and planning target volume (PTV) dose-volume parameters and by blinded clinical evaluation of 3 expert clinicians.

RESULTS

KB-TP plans were generally better than or equivalent to TP plans in both validation cohorts. A significant improvement in PTVs and rectum-PTV overlap dosimetry parameters were observed for both sets. Organ-at-risk sparing for KB-TP was slightly improved, which was more evident in the external validation group and for bladder and bowel. Clinical evaluation reported KB-TP to be better in 60% of cases and worse in 10% compared with TP (P < .05).

CONCLUSIONS

The fully KB-based automatic planning workflow was successfully implemented for HTT planning optimization in the case of high-risk patients with prostate cancer.

Dosimetric evaluation with knowledge-based planning created at different periods in volumetric-modulated arc therapy for prostate cancer: a multi-institution study

Dosimetric evaluation and variation assessment were performed with two knowledge-based planning (KBP) models created at different periods for volumetric-modulated arc therapy (VMAT) for prostate cancer at five institutes. The first and second models (F- and S-models) for KBP were created before April 2017 and April 2019, respectively. The S-model was created using feedback plans from the F-model. Dose evaluation was compared between the two models using the same two computed tomography (CT) datasets and structures. The evaluation metrics were the dose received by 95.0% and 2.0% of the planning target volume (PTV); dose-volume parameters to the rectum and bladder as V90, V80, and V50; and monitor unit (MU). Dosimetric variation was compared by exporting estimated dose-volume histograms for each model to the Model Analytics website and assessing the organ at risk volume. There were no dosimetric differences between the two models for PTV. The V50 of the rectum in the S-model had improved compared to that of the F-model (case I: 49.3 ± 15.6 and 43.5 ± 15.2 [p = 0.08]; case II: 42.5 ± 16.9 and 36.0 ± 15.6 [p = 0.138]). The differences in other parameters were within ± 1.8% between the rectum and the bladder. The MU was slightly higher in the S-model than in the F-model, and dosimetric variation was reduced to the rectum and bladder among all the institutes. The polished S-model for KBP could be used for standardization of the plan quality and sharing of KBP models in VMAT for prostate cancer.

Using deep learning to predict beam-tunable Pareto optimal dose distribution for intensity-modulated radiation therapy

PURPOSE

Many researchers have developed deep learning models for predicting clinical dose distributions and Pareto optimal dose distributions. Models for predicting Pareto optimal dose distributions have generated optimal plans in real time using anatomical structures and static beam orientations. However, Pareto optimal dose prediction for intensity-modulated radiation therapy (IMRT) prostate planning with variable beam numbers and orientations has not yet been investigated. We propose to develop a deep learning model that can predict Pareto optimal dose distributions by using any given set of beam angles, along with patient anatomy, as input to train the deep neural networks. We implement and compare two deep learning networks that predict with two different beam configuration modalities.

METHODS

We generated Pareto optimal plans for 70 patients with prostate cancer. We used fluence map optimization to generate 500 IMRT plans that sampled the Pareto surface for each patient, for a total of 35 000 plans. We studied and compared two different models, Models I and II. Although they both used the same anatomical structures - including the planning target volume (PTV), organs at risk (OARs), and body - these models were designed with two different methods for representing beam angles. Model I directly uses beam angles as a second input to the network as a binary vector. Model II converts the beam angles into beam doses that are conformal to the PTV. We divided the 70 patients into 54 training, 6 validation, and 10 testing patients, thus yielding 27 000 training, 3000 validation, and 5000 testing plans. Mean square loss (MSE) was taken as the loss function. We used the Adam optimizer with a default learning rate of 0.01 to optimize the network's performance. We evaluated the models' performance by comparing their predicted dose distributions with the ground truth (Pareto optimal) dose distribution, in terms of dose volume histogram (DVH) plots and evaluation metrics such as PTV D98 , D95 , D50 , D2 , Dmax , Dmean , Paddick Conformation Number, R50, and Homogeneity index.

RESULTS

Our deep learning models predicted voxel-level dose distributions that precisely matched the ground truth dose distributions. The DVHs generated also precisely matched the ground truth. Evaluation metrics such as PTV statistics, dose conformity, dose spillage (R50), and homogeneity index also confirmed the accuracy of PTV curves on the DVH. Quantitatively, Model I's prediction error of 0.043 (confirmation), 0.043 (homogeneity), 0.327 (R50), 2.80% (D95), 3.90% (D98), 0.6% (D50), and 1.10% (D2) was lower than that of Model II, which obtained 0.076 (confirmation), 0.058 (homogeneity), 0.626 (R50), 7.10% (D95), 6.50% (D98), 8.40% (D50), and 6.30% (D2). Model I also outperformed Model II in terms of the mean dose error and the max dose error on the PTV, bladder, rectum, left femoral head, and right femoral head.

CONCLUSIONS

Treatment planners who use our models will be able to use deep learning to control the trade-offs between the PTV and OAR weights, as well as the beam number and configurations in real time. Our dose prediction methods provide a stepping stone to building automatic IMRT treatment planning.

Influence of Cleaned-up Commercial Knowledge-Based Treatment Planning on Volumetric-Modulated Arc Therapy of Prostate Cancer

Purpose:

This study aimed to investigate the influence of cleaned-up knowledge-based treatment planning (KBP) models on the plan quality for volumetric-modulated arc therapy (VMAT) of prostate cancer.

Materials and Methods:

Thirty prostate cancer VMAT plans were enrolled and evaluated according to four KBP modeling methods as follows: (1) model not cleaned – trained by fifty other clinical plans (KBP_ORIG); (2) cases cleaned by removing plans that did not meet all clinical goals of the dosimetric parameters, derived from dose–volume histogram (DVH) (KBP_C-DVH); (3) cases cleaned outside the range of ±1 standard deviation through the principal component analysis regression plots (KBP_C-REG); and (4) cases cleaned using both methods (2) and (3) (KBP_C-ALL). Rectal and bladder structures in the training models numbered 34 and 48 for KBP_C-DVH, 37 and 33 for KBP_C-REG, and 26 and 33 for KBP_C-ALL, respectively. The dosimetric parameters for each model with one-time auto-optimization were compared.

Results:

All KBP models improved target dose coverage and conformity and provided comparable sparing of organs at risks (rectal and bladder walls). There were no significant differences in plan quality among the KBP models. Nevertheless, only the KBP_C-ALL model generated no cases of >1% V_{78 Gy} (prescribed dose) to the rectal wall, whereas the KBP_ORIG, KBP_C-DVH, and KBP_C-REG models included two, four, and three cases, respectively, which were difficult to overcome with KBP because the planning target volume (PTV) and rectum regions overlapped.

Conclusions:

The cleaned-up KBP model based on DVH and regression plots improved plan quality in the PTV–rectum overlap region.

Dose-volume histogram analysis and clinical evaluation of knowledge-based plans with manual objective constraints for pharyngeal cancer

The present study aimed to evaluate whether knowledge-based plans (KBP) from a single optimization could be used clinically, and to compare dose-volume histogram (DVH) parameters and plan quality between KBP with (KBPCONST) and without (KBPORIG) manual objective constraints and clinical manual optimized (CMO) plans for pharyngeal cancer. KBPs were produced from a system trained on clinical plans from 55 patients with pharyngeal cancer who had undergone intensity-modulated radiation therapy or volumetric-modulated arc therapy (VMAT). For another 15 patients, DVH parameters of KBPCONST and KBPORIG from a single optimization were compared with CMO plans with respect to the planning target volume (D98%, D50%, D2%), brainstem maximum dose (Dmax), spinal cord Dmax, parotid gland median and mean dose (Dmed and Dmean), monitor units and modulation complexity score for VMAT. The Dmax of spinal cord and brainstem and the Dmed and Dmean of ipsilateral parotid glands were unacceptably high for KBPORIG, although the KBPCONST DVH parameters met our goal for most patients. KBPCONST and CMO plans produced comparable DVH parameters. The monitor units of KBPCONST were significantly lower than those of the CMO plans (P < 0.001). Dose distribution of the KBPCONST was better than or comparable to that of the CMO plans for 13 (87%) of the 15 patients. In conclusion, KBPORIG was found to be clinically unacceptable, while KBPCONST from a single optimization was comparable or superior to CMO plans for most patients with head and neck cancer.

Parotid sparing in RapidPlan Oropharynx models: To split or not to split

INTRODUCTION

Differences in knowledge and experience, patient anatomy and tumour location and manipulation of inverse planning objectives and priorities will lead to a variability in the quality of radiation planning. The aim of this study was to investigate whether parotid glands should be treated as separate or combined structures when using knowledge-based planning (KBP) to create oropharyngeal plans, based on the dose they receive.

METHOD

Two separate RapidPlan (RP) models were created using the same 70 radical oropharyngeal patients. The 'separated model' divided the parotids into ipsilateral and contralateral structures. The 'combined model' did not separate the parotids. The models were independently validated using 20 patients not included in the models. The same dose constraints and priorities were applied to planning target volumes (PTVs) and organs at risk (OARs) for all plans. An auto-generated line objective and priority was applied in both models, with parotid mean dose and V50 doses evaluated and compared.

RESULTS

Plans optimised using the combined model resulted in lower ipsilateral mean doses and lower V50 doses in 80% and 75% of cases, respectively. Fifty-five per cent of plans produced lower mean doses for the contralateral parotid when optimised using the combined model, while lower V50 doses were evenly split between the models.

CONCLUSION

Combining the data for both parotids into one RP model resulted in better ipsilateral parotid sparing. Results also suggest that a combined parotid model will spare dose to the contralateral parotid; however, further investigation is required to confirm these results.

Automatic Planning for Nasopharyngeal Carcinoma Based on Progressive Optimization in RayStation Treatment Planning System

OBJECTIVE

To evaluate and quantify the planning performance of automatic planning (AP) with manual planning (MP) for nasopharyngeal carcinoma in the RayStation treatment planning system (TPS).

METHODS

A progressive and effective design method for AP of nasopharyngeal carcinoma was realized through automated scripts in this study. A total of 30 patients with nasopharyngeal carcinoma with initial treatment was enrolled. The target coverage, conformity index (CI), homogeneity index (HI), organs at risk sparing, and the efficiency of design and execution were compared between automatic and manual volumetric modulated arc therapy (VMAT) plans.

RESULTS

The results of the 2 design methods met the clinical dose requirement. The differences in D95 between the 2 groups in PTV1 and PTV2 showed statistical significance, and the MPs are higher than APs, but the difference in absolute dose was only 0.21% and 0.16%. The results showed that the conformity index of planning target volumes (PTV1, PTV2, PTVnd and PGTVnx+rpn [PGTVnx and PGTVrpn]), homogeneity index of PGTVnx+rpn, and HI of PTVnd in APs are better than that in MPs. For organs at risk, the APs are lower than the MPs, and the difference was statistically significant (P < .05). The manual operation time in APs was 83.21% less than that in MPs, and the computer processing time was 34.22% more.

CONCLUSION

IronPython language designed by RayStation TPS has clinical application value in the design of automatic radiotherapy plan for nasopharyngeal carcinoma. The dose distribution of tumor target and organs at risk in the APs was similar or better than those in the MPs. The time of manual operation in the plan design showed a sharp reduction, thus significantly improving the work efficiency in clinical application.

A hybrid automated treatment planning solution for esophageal cancer

OBJECTIVE

This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer.

METHODS

In order to fully investigate the advantages of HAP, three planning strategies were implemented in the present study: HAP, KBP, and full manual planning. Each method was applied to 20 patients. For HAP and KBP, the objective functions for plan optimization were generated from a dose-volume histogram (DVH) estimation model, which was based on 70 esophageal patients. Script-based automated planning was used for HAP, while the regular IMRT inverse planning method was used for KBP. For full manual planning, clinical standards were applied to create the plans. Paired t-tests were performed to compare the differences in dose-volume indices among the three planning methods.

RESULTS

Among the three planning strategies, HAP exhibited the best performance in all dose-volume indices, except for PTV dose homogeneity and lung V5. PTV conformity and spinal cord sparing were significantly improved in HAP (P < 0.001). Compared to KBP, HAP improved all indices, except for lung V5. Furthermore, the OAR sparing and target coverage between HAP and full manual planning were similar. Moreover, HAP had the shortest average planning time (57 min), when compared to KBP (63 min) and full manual planning (118 min).

CONCLUSION

HAP is an effective planning strategy for obtaining a high quality treatment plan for esophageal cancer.

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.

RapidPlan development of VMAT plans for cervical cancer patients in low- and middle-income countries

Cervical cancer has a high incidence and mortality rate in low- and middle-income countries (LMICs) largely due to limited resources and insufficient staffing. Knowledge-based planning (KBP) could alleviate understaffing issues by streamlining the radiotherapy treatment planning process. Varian's KBP system (RapidPlan) was used to develop a model capable of producing volumetric modulated arc therapy (VMAT) plans for cervical cancer patients. Plan data from 46 patients previously treated at MD Anderson Cancer Center (MDACC) were used to create and train the model which was then applied to 32 patients excluded from the training process. Dose volume histogram (DVH) values for the planning target volume (PTV_High), bladder, rectum, and bowel were evaluated for the validation plans and found to have satisfied the required PTV coverage and organ-at-risk (OAR) dose constraints. The average value for PTV_High D_95.0% was 48.0 Gy (sd = 3.0 Gy) for existing clinical plans and 48.4 Gy (sd = 2.6 Gy) for the validation plans. The mean dose for the bladder, rectum, and bowel was 39.8 Gy (sd = 3.9 Gy), 41.6 Gy (sd = 5.2 Gy), and 21.6 Gy (sd = 5.0 Gy) for existing clinical plans and 38.9 Gy (sd = 4.0 Gy), 40.3 Gy (sd = 4.8 Gy), and 21.5 Gy (sd = 4.6 Gy) for validation plans, respectively. A TOST test showed that the p values for the PTV_High D_95.0% (p < 0.001), rectum V_30Gy (p = 0.039), and mean dose to the bladder (p = 0.0014), rectum (p = 0.025), and bowel (p = 0.006) were statistically significant within a 5% equivalence margin of the clinical value thereby providing strong evidence of equivalence. Based on this statistical analysis, it was determined that the model was capable of generating treatable VMAT plans for cervical cancer patients.

Inter-planner variation in treatment-plan quality of plans created with a knowledge-based treatment planning system

PURPOSE

This study aimed to clarify the inter-planner variation of plan quality in knowledge-based plans created by nine planners.

METHODS

Five hypofractionated prostate-only (HPO) volumetric modulated arc therapy (VMAT) plans and five whole-pelvis (WP) VMAT plans were created by each planner using a knowledge-based planning (KBP) system. Nine planners were divided into three groups of three planners each: Senior, Junior, and Beginner. Single optimization with only priority modification for all objectives was performed to stay within the dose constraints. The coefficients of variation (CVs) for dosimetric parameters were evaluated, and a plan quality metric (PQM) was used to evaluate comprehensive plan quality.

RESULTS

Lower CVs (<0.05) were observed at dosimetric parameters in the planning target volume for both HPO and WP plans, while the CVs in the rectum and bladder for WP plans (<0.91) were greater than those for HPO plans (<0.17). The PQM values of HPO plans for Cases1-5 (average ± standard deviation) were 41.2 ± 7.1, 40.9 ± 5.6, and 39.9 ± 4.6 in the Senior, Junior, and Beginner groups, respectively. For the WP plans, the PQM values were 51.9 ± 6.3, 47.5 ± 4.3, and 40.0 ± 6.6, respectively. The number of clinically acceptable HPO and WP plans were 13/15 and 11/15 in the Senior group, 13/15 and 10/15 plans in the Junior group, and 8/15 and 2/15 plans in the Beginner group, respectively.

CONCLUSION

Inter-planner variation in the plan quality with RapidPlan remains, especially for the complicated VMAT plans, due to planners' heuristics.

RapidPlan knowledge based planning: iterative learning process and model ability to steer planning strategies

Purpose

To determine if the performance of a knowledge based RapidPlan (RP) planning model could be improved with an iterative learning process, i.e. if plans generated by an RP model could be used as new input to re-train the model and achieve better performance.

Methods

Clinical VMAT plans from 83 patients presenting with head and neck cancer were selected to train an RP model, CL-1. With this model, new plans on the same patients were generated, and subsequently used as input to train a novel model, CL-2. Both models were validated on a cohort of 20 patients and dosimetric results compared. Another set of 83 plans was realised on the same patients with different planning criteria, by using a simple template with no attempt to manually improve the plan quality. Those plans were employed to train another model, TP-1. The differences between the plans generated by CL-1 and TP-1 for the validation cohort of patients were compared with respect to the differences between the original plans used to build the two models.

Results

The CL-2 model presented an improvement relative to CL-1, with higher R² values and better regression plots. The mean doses to parallel organs decreased with CL-2, while D_1% to serial organs increased (but not significantly). The different models CL-1 and TP-1 were able to yield plans according to each original strategy.

Conclusion

A refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.

Multi-institutional evaluation of knowledge-based planning performance of volumetric modulated arc therapy (VMAT) for head and neck cancer

PURPOSE

The aim of this study was to investigate whether additional manual objectives are necessary for the RapidPlan (RP) with a single optimization. We conducted multi-institutional comparisons of plan quality for head and neck cancer (HNC) using the models created at each institute.

METHODS

The ability of RP to produce acceptable plans for dose requirements was evaluated in two types of oropharynx cancers at five institutes in Japan. Volumetric modulated arc therapy plans created without (RP plan) and with additional manual objectives (M-RP plan) were compared in terms of planning target volume (PTV), brainstem, spinal cord and parotid glands in dosimetric parameters.

RESULTS

There were no major dosimetric PTV differences between RP and M-RP plans. For the brainstem and spinal cord in the RP plans, only 40% and 30% of the plans achieved the dose requirements, while the M-RP plans with upper objective added to volume 0% at all institutes achieved them for 90% of the plans. For the L-parotid gland, there was no difference in the RP and M-RP plans (both were 40%) in achieving the acceptable criteria. For the R-parotid gland, 60% and 80% of the RP and M-RP plans achieved the constraint criteria, and in terms of the achievement rate, the RP plans were relatively high.

CONCLUSIONS

M-RP plans did not require reoptimization; only an upper objective was needed for the brainstem and spinal cord, while the parotid gland dose was reduced in both RP plans with the auto generated line objectives alone.

Real-time interactive planning for radiotherapy of head and neck cancer with volumetric modulated arc therapy

Background and purpose

Planning complex radiotherapy treatments can be inefficient, with large variation in plan quality. In this study we evaluated plan quality and planning efficiency using real-time interactive planning (RTIP) for head and neck (HN) volumetric modulated arc therapy (VMAT).

Materials and methods

RTIP allows manipulation of dose volume histograms (DVHs) in real-time to assess achievable planning target volume (PTV) coverage and organ at risk (OAR) sparing. For 20 HN patients previously treated with VMAT, RTIP was used to minimize OAR dose while maintaining PTV coverage. RTIP DVHs were used to guide VMAT optimization. Dosimetric differences between RTIP-assisted plans and original clinical plans were assessed. Five blinded radiation oncologists indicated their preference for each PTV, OAR and overall plan. To assess efficiency, ten patients were planned de novo by experienced and novice planners and a RTIP user.

Results

The average planning time with RTIP was <20 min, and most plans required only one optimization. All 20 RTIP plans were preferred by a majority of oncologists due to improvements in OAR sparing. The average maximum dose to the spinal cord was reduced by 10.5 Gy (from 49.5 to 39.0 Gy), and the average mean doses for the oral cavity, laryngopharynx, contralateral parotid and submandibular glands were reduced by 3.5 Gy (39.1-35.7 Gy), 6.8 Gy (42.5-35.7 Gy), 1.7 Gy (17.0-15.3 Gy) and 3.3 Gy (22.9-19.5 Gy), respectively.

Conclusions

Incorporating RTIP into clinical workflows may increase both planning efficiency and OAR sparing.