Multicentre quality assurance of intensity-modulated radiation therapy plans: A precursor to clinical trials

Knowledge-based model building for treatment planning for prostate cancer using commercial treatment planning quality assurance software tools

This study devised a method to efficiently launch the RapidPlan model for volumetric-modulated arc therapy for prostate cancer in small- and medium-sized facilities using high-quality treatment plans with the PlanIQ software as a reference. Treatment plans were generated for 30 patients with prostate cancer to construct the RapidPlan model using PlanIQ as a reference. In the context of PlanIQ-referenced treatment planning, treatment plans were developed, such that the feasibility dose-volume histogram of each organ-at-risk fell within F ≤ 0.1. For validation of the RapidPlan model, treatment plans were formulated for 20 patients using both RapidPlan and PlanIQ, and the differences were evaluated. The results of RapidPlan model validity assessment revealed that the RapidPlan-produced treatment plans exhibited higher quality in 11 of 20 patients. No significant differences were found between the treatment plans. In conclusion, high-quality treatment plans formulated using PlanIQ as reference facilitated efficient implementation of RapidPlan modeling.

Differences between professionals in treatment planning for patients with stage III lung cancer using treatment-planning QA software

Background

The quality of treatment planning for stage III non-small cell lung cancer varies within and between facilities due to the different professions involved in planning. Dose estimation parameters were calculated using a feasibility dose-volume histogram (FDVH) implemented in the treatment planning quality assurance software PlanIQ. This study aimed to evaluate differences in treatment planning between occupations using manual FDVH-referenced treatment planning to identify their characteristics.

Materials and methods

The study included ten patients with stage III non-small cell lung cancer, and volumetric-modulated arc therapy was used as the treatment planning technique. Fifteen planners, comprising five radiation oncologists, five medical physicists, and five radiological technologists, developed treatment strategies after referring to the FDVH.

Results

Medical physicists had a higher mean dose at D98% of the planning target volume (PTV) and a lower mean dose at D2% of the PTV than those in other occupations. Medical physicists had the lowest irradiation lung volumes (V5 Gy and V13 Gy) compared to other professions, and radiation oncologists had the lowest V20 Gy and mean lung dose. Radiological technologists had the highest irradiation volumes for dose constraints at all indexes on the normal lung volume.

Conclusions

The quality of the treatment plans developed in this study differed between occupations due to their background expertise, even when an FDVH was used as a reference. Therefore, discussing and sharing knowledge and treatment planning techniques among professionals is essential to determine the optimal treatment plan for each facility and patient.

Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy

PURPOSE

We previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Using deep reinforcement learning guided by human knowledge, we trained the VTP to autonomously adjust relevant parameters in treatment plan optimization, similar to a human planner, to generate high-quality plans for prostate cancer stereotactic body radiation therapy (SBRT). This study describes the clinical implementation and evaluation of VTP.

MATERIALS AND METHODS

We integrate VTP with Eclipse TPS using scripting Application Programming Interface. VTP observes dose-volume histograms of relevant structures, decides how to adjust dosimetric constraints, including doses, volumes, and weighting factors, and applies the adjustments to the TPS interface to launch the optimization engine. This process continues until a high-quality plan is achieved. We evaluated VTP's performance using the prostate SBRT case from the 2016 American Association of Medical Dosimetrist/Radiosurgery Society plan study with its plan scoring system, and compared to human-generated plans submitted to the challenge. Using the same scoring system, we also compared the plan quality of 36 prostate SBRT cases (20 planned with IMRT and 16 planned with VMAT) treated at our institution for both VTP and human-generated plans.

RESULTS

In the plan study case, VTP achieved a score of 142.1/150.0, ranking the third in the competition (median 134.6). For the clinical cases, VTP achieved 110.6 ± 6.5 for 20 IMRT plans and 126.2 ± 4.7 for 16 VMAT plans, similar to scores of human-generated plans with 110.4 ± 7.0 for IMRT plans and 125.4 ± 4.4 for VMAT plans. The workflow, plan quality and planning time of VTP were reviewed to be satisfactory by experienced physicists.

CONCLUSION

We successfully implemented VTP to operate a TPS for autonomous human-like treatment planning for prostate SBRT.

A sub-analysis of multi-center planning radiosurgery for intracranial metastases through automation (MC-PRIMA) comparing UK and international centers

OBJECTIVES

A sub-analysis of the MC-PRIMA study was performed to compare the plan quality of stereotactic radiosurgery (SRS) to multiple brain metastases (MBM) between UK and other international centres.

METHODS AND MATERIALS

Six centres from the UK and nineteen from other international centres autoplanned using Multiple Brain Mets™ (AutoMBM; Brainlab, Munich, Germany) software for a five MBM study case from a prior planning competition that was originally organized by the Trans-Tasmania Radiation Oncology Group (TROG). Twenty-three dosimetric metrics and the resulting composite plan score per the TROG planning competition were compared between the UK and other international centres. Planning experience and planning time from each planner were recorded and statistically compared.

RESULTS

Planning experiences between two groups are equal. Except for mean dose to the hippocampus, all other 22 dosimetric metrics were comparable between two groups. The inter-planner variations in these 23 dosimetric metrics and the composite plan score were also statistically equivalent. Planning time is slightly longer in the UK group (mean = 86.8 min) with a mean difference of 50.3 min.

CONCLUSIONS

AutoMBM effectively achieves standardization of the plan quality of SRS to MBM within UK and further against the other international centres. Significant planning efficiency gain by AutoMBM both among the UK and other international centres may help to increase the capacity of SRS service by alleviating the clinical and technical loadings.

Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning

Purpose

Deep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel's dose. In this study, we combined DPTV and anatomical information as input for a deep-learning-based dose-prediction network to improve performance.

Materials and methods

One hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation.

Results

The mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p<0.001). The ME values of the body for the 2-type models were similar (p >0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p<0.05), and there were highly significant differences between 10 Gy and 55 Gy (p<0.001). For most organs at risk, the ME, MAE, and dosimetric parameters predicted by both models were concurrent with the ground truth values except the MAE values of the pituitary and optic chiasm in the ANAT model and the average mean dose of the right parotid in the ANAT model.

Conclusions

The COM model outperformed the ANAT model and could improve automated planning with statistically highly significant differences.

Multi-institution model (big model) versus single-institution model of knowledge-based volumetric modulated arc therapy (VMAT) planning for prostate cancer

We established a multi-institution model (big model) of knowledge-based treatment planning with over 500 treatment plans from five institutions in volumetric modulated arc therapy (VMAT) for prostate cancer. This study aimed to clarify the efficacy of using a large number of registered treatment plans for sharing the big model. The big model was created with 561 clinically approved VMAT plans for prostate cancer from five institutions (A: 150, B: 153, C: 49, D: 60, and E: 149) with different planning strategies. The dosimetric parameters of planning target volume (PTV), rectum, and bladder for two validation VMAT plans generated with the big model were compared with those from each institutional model (single-institution model). The goodness-of-fit of regression lines (R2 and χ2 values) and ratios of the outliers of Cook’s distance (CD) > 4.0, modified Z-score (mZ) > 3.5, studentized residual (SR) > 3.0, and areal difference of estimate (dA) > 3.0 for regression scatter plots in the big model and single-institution model were also evaluated. The mean ± standard deviation (SD) of dosimetric parameters were as follows (big model vs. single-institution model): 79.0 ± 1.6 vs. 78.7 ± 0.5 (D50) and 0.13 ± 0.06 vs. 0.13 ± 0.07 (Homogeneity Index) for the PTV; 6.6 ± 4.0 vs. 8.4 ± 3.6 (V90) and 32.4 ± 3.8 vs. 46.6 ± 15.4 (V50) for the rectum; and 13.8 ± 1.8 vs. 13.3 ± 4.3 (V90) and 39.9 ± 2.0 vs. 38.4 ± 5.2 (V50) for the bladder. The R2 values in the big model were 0.251 and 0.755 for rectum and bladder, respectively, which were comparable to those from each institution model. The respective χ2 values in the big model were 1.009 and 1.002, which were closer to 1.0 than those from each institution model. The ratios of the outliers in the big model were also comparable to those from each institution model. The big model could generate a comparable VMAT plan quality compared with each single-institution model and therefore could possibly be shared with other institutions.

A conjugate gradient-assisted multi-objective evolutionary algorithm for fluence map optimization in radiotherapy treatment

Intensity-modulated radiotherapy (IMRT) is one of the most applied techniques for cancer radiotherapy treatment. The fluence map optimization is an essential part of IMRT plan designing, which has a significant impact on the radiotherapy treatment effect. In fact, the treatment planing of IMRT is an inverse multi-objective optimization problem. Existing approaches of solving the fluence map optimization problem (FMOP) obtain a satisfied treatment plan via trying different coupling weights, the optimization process needs to be conducted many times and the coupling weight setting is completely based on the experience of a radiation physicist. For fast obtaining diverse high-quality radiotherapy plans, this paper formulates the FMOP into a three-objective optimization problem, and proposes a conjugate gradient-assisted multi-objective evolutionary algorithm (CG-MOEA) to solve it. The proposed algorithm does not need to set the coupling weights and can produce the diverse radiotherapy plans within a single run. Moreover, the convergence speed is further accelerated by an adaptive local search strategy based on the conjugate-gradient method. Compared with five state-of-the-art multi-objective evolutionary algorithms (MOEAs), the proposed CG-MOEA can obtain the best hypervolume (HV) values and dose–volume histogram (DVH) performance on five clinical cases in cancer radiotherapy. Moreover, the proposed algorithm not only obtains the more optimal solution than traditional method used to solve the FMOP, but also can find diverse Pareto solution set which can be provided to radiation physicist to select the best treatment plan. The proposed algorithm outperforms dose-volume histogram state-of-the-art multi-objective evolutionary algorithms and traditional method for FMOP on five clinical cases in cancer radiotherapy.

Algorithm for an automatic treatment planning system using a single-arc VMAT for prostate cancer

Optimization process in treatment planning for intensity‐modulated radiation therapy varies with the treatment planner. Therefore, a large variation in the quality of dose distribution is usually observed. To reduce variation, an automatic optimizing toolkit was developed for the Monaco treatment planning system (Elekta AB, Stockholm, Sweden) for prostate cancer using volumetric‐modulated arc therapy (VMAT). This toolkit was able to create plans automatically. However, most plans needed two arcs per treatment to ensure the dose coverage for targets. For prostate cancer, providing a plan with a single arc was advisable in clinical practice because intrafraction motion management must be considered to irradiate accurately. The purpose of this work was to develop an automatic treatment planning system with a single arc per treatment for prostate cancer using VMAT. We designed the new algorithm for the automatic treatment planning system to use one arc per treatment for prostate cancer in Monaco. We constructed the system in two main steps: (1) Determine suitable cost function parameters for each case before optimization, and (2) repeat the calculation and optimization until the conditions for dose indices are fulfilled. To evaluate clinical suitability, the plan quality between manual planning and the automatic planning system was compared. Our system created the plans automatically in all patients within a few iterations. Statistical differences between the plans were not observed for the target and organ at risk. It created the plans with no human input other than the initial template setting and system initiation. This system offers improved efficiency in running the treatment planning system and human resources while ensuring high‐quality outputs.

Dosimetric comparison of RapidPlan and manually optimised volumetric modulated arc therapy plans in prostate cancer

Purpose:

The aim of this study was to evaluate whether RapidPlan (RP) could generate clinically acceptable prostate volumetric modulated arc therapy (VMAT) plans.

Methods:

The in-house RP model was used to generate VMAT plans for 50 previously treated prostate cancer patients, with no additional optimisation being performed. The VMAT plans that were generated using the RP model were compared with the patients’ previous, manually optimised clinical plans (MP), none of which had been used for the development of the in-house RP prostate model. Differences between RP and MP in planning target volume (PTV) doses, organs at risk (OAR) sparing, monitor units (MU) and planning time required to produce treatment plans were analysed. Assessment of PTV doses was based on the conformation number (CN), homogeneity index (HI), D2%, D99% and the mean dose of the PTV. The OAR doses evaluated were the rectal V50 Gy, V65 Gy, V70 Gy and the mean dose, the bladder V65 Gy, V70 Gy and the mean dose, and the mean dose to both femurs.

Results:

D99% and mean dose of the PTV were lower for RP than for MP (p = 0·006 and p = 0·040, respectively).V50 Gy, V65 Gy and the mean dose to rectum were lower in RP than in MP (p < 0·001). V65 Gy, V70 Gy and the mean dose to bladder were lower in RP than in MP (p < 0·001). RP had enhanced the sparing of both femurs (p < 0·001) and significantly reduced the planning time to less than 5% of the time taken with MP. MU in RP was significantly higher than MP by an average of 52·5 MU (p < 0·001) and 46 out of the 50 RP plans were approved by the radiation oncologist.

Conclusion:

This study has demonstrated that VMAT plans generated using an in-house RP prostate model in a single optimisation for prostate patients were clinically acceptable with comparable or better plan quality compared to MP. RP can add value and improve treatment planning efficiency in a high-throughput radiotherapy department through reduced plan optimisation time while maintaining consistency in the plan quality.

An updating approach for knowledge-based planning models to improve plan quality and variability in volumetric-modulated arc therapy for prostate cancer

PURPOSE

The purpose of this study was to compare the dose-volume parameters and regression scatter plots of the iteratively improved RapidPlan (RP) models, specific knowledge-based planning (KBP) models, in volumetric-modulated arc therapy (VMAT) for prostate cancer over three periods.

METHODS

A RP1 model was created from 47 clinical intensity-modulated radiation therapy (IMRT)/VMAT plans. A RP2 model was created to exceed dosimetric goals which set as the mean values +1SD of the dose-volume parameters of RP1 (50 consecutive new clinical VMAT plans). A RP3 model was created with more strict dose constraints for organs at risks (OARs) than RP1 and RP2 models (50 consecutive anew clinical VMAT plans). Each RP model was validated against 30 validation plans (RP1, RP2, and RP3) that were not used for model configuration, and the dose-volume parameters were compared. The Cook's distances of regression scatterplots of each model were also evaluated.

RESULTS

Significant differences (p < 0.05) between RP1 and RP2 were found in D_mean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V_40Gy (47.3% vs. 45.7%), V_60Gy (27.9% vs. 27.1%), V_70Gy (16.4% vs. 15.2%), and V_78Gy (0.4% vs. 0.2%) for the rectal wall, and V_40Gy (43.8% vs. 41.8%) and V_70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V_70Gy (15.2% vs. 15.8%) of the rectal wall differed significantly between RP2 and RP3. The proportions of cases with a Cook's distance of <1.0 (RP1, RP2, and RP3 models) were 55%, 78%, and 84% for the rectal wall, and 77%, 68%, and 76% for the bladder wall, respectively.

CONCLUSIONS

The iteratively improved RP models, reflecting the clear dosimetric goals based on the RP feedback (dose-volume parameters) and more strict dose constraints for the OARs, generated superior dose-volume parameters and the regression scatterplots in the model converged. This approach could be used to standardize the inverse planning strategies.

Dosimetric Evaluation of Simplified Knowledge-Based Plan with an Extensive Stepping Validation Approach in Volumetric-Modulated Arc Therapy-Stereotactic Body Radiotherapy for Lung Cancer

Purpose:

We investigated the performance of the simplified knowledge-based plans (KBPs) in stereotactic body radiotherapy (SBRT) with volumetric-modulated arc therapy (VMAT) for lung cancer.

Materials and Methods:

For 50 cases who underwent SBRT, only three structures were registered into knowledge-based model: total lung, spinal cord, and planning target volume. We performed single auto-optimization on VMAT plans in two steps: 19 cases used for the model training (closed-loop validation) and 16 new cases outside of training set (open-loop validation) for TrueBeam (TB) and Halcyon (Hal) linacs. The dosimetric parameters were compared between clinical plans (CLPs) and KBPs: CLP_closed, KBP_closed-TB and KBP_closed-Hal in closed-loop validation, CLP_open, KBP_open-TB and KBP_open-Hal in open-loop validation.

Results:

All organs at risk were comparable between CLPs and KBPs except for contralateral lung: V₅ of KBPs was approximately 3%–7% higher than that of CLPs. V₂₀ of total lung for KBPs showed comparable to CLPs; CLP_closed vs. KBP_closed-TB and CLP_closed vs. KBP_closed-Hal: 4.36% ± 2.87% vs. 3.54% ± 1.95% and 4.36 ± 2.87% vs. 3.54% ± 1.94% (P = 0.54 and 0.54); CLP_open vs. KBP_open-TB and CLP_open vs. KBP_open-Hal: 4.18% ± 1.57% vs. 3.55% ± 1.27% and 4.18% ± 1.57% vs. 3.67% ± 1.26% (P = 0.19 and 0.27). CI₉₅ of KBPs with both linacs was superior to that of the CLP in closed-loop validation: CLP_closed vs. KBP_closed-TB vs. KBP_closed-Hal: 1.32% ± 0.12% vs. 1.18% ± 0.09% vs. 1.17% ± 0.06% (P < 0.01); and open-loop validation: CLP_open vs. KBP_open-TB vs. KBP_open-Hal: 1.22% ± 0.09% vs. 1.14% ± 0.04% vs. 1.16% ± 0.05% (P ≤ 0.01).

Conclusions:

The simplified KBPs with limited number of structures and without planner intervention were clinically acceptable in the dosimetric parameters for lung VMAT-SBRT planning.

DVHnet: A deep learning-based prediction of patient-specific dose volume histograms for radiotherapy planning

PURPOSE

To develop a deep learning method to predict patient-specific dose volume histograms (DVHs) for radiotherapy planning.

METHODS

Patient data included 180 cases with nasopharyngeal cancer, of which 153 cases were used for training and 27 for testing. A network (named "DVHnet") based on a convolutional neural network (CNN) was designed for directly predicting DVHs of organs at risk (OARs). Two-channel images with contoured structures were generated as the inputs for training the model. A one-dimensional array consisting of 256 continuous volume percentages on a DVH curve for each slice was calculated as the corresponding output. The combined DVH was then calculated. Sixteen OARs were modeled in the study. Prediction accuracy was evaluated against the corresponding DVH curve of ground truth (GT) plans. A global DVH analysis and critical dosimetry metrics for each OAR were calculated for quantitative evaluation. The performance of DVHnet also was evaluated against two baselines: DosemapNet (developed by our research group) and commercial RapidPlan software.

RESULTS

The predicted mean difference in average dose of all OARs using DVHnet was 0.30 ± 0.95 Gy. And the predicted differences in D2% and D50 can be control within 2.32 and 0.69 Gy. For most OARs, there were no obvious differences between the dosimetric metrics of the predicted and GT values for both DVHnet and DosemapNet (P ≥ 0.05). Only the predicted D2% of the optic organs for DVHnet, and of brain stem PRV for DosemapNet displayed statistically significant differences. Except for the optic organs, DVHnet performs better than or comparably with RapidPlan. The mean difference in proportion of points of interest was 3.59% ± 7.78%.

CONCLUSIONS

A deep learning network model was developed to automatically extract useful features for accurate prediction of patient-specific DVH curves directly. The performance of DVHnet was comparable to DosemapNet and RapidPlan.

Evaluation of treatment plan quality for head and neck IMRT: a multicenter study

Intensity-modulated radiotherapy (IMRT) treatment planning for head and neck cancer is challenging and complex due to many organs at risk (OAR) in this region. The experience and skills of planners may result in substantial variability of treatment plan quality. This study assessed the performance of IMRT planning in Malaysia and observed plan quality variation among participating centers. The computed tomography dataset containing contoured target volumes and OAR was provided to participating centers. This is to control variations in contouring the target volumes and OARs by oncologists. The planner at each center was instructed to complete the treatment plan based on clinical practice with a given prescription, and the plan was analyzed against the planning goals provided. The quality of completed treatment plans was analyzed using the plan quality index (PQI), in which a score of 0 indicated that all dose objectives and constraints were achieved. A total of 23 plans were received from all participating centers comprising 14 VMAT, 7 IMRT, and 2 tomotherapy plans. The PQI indexes of these plans ranged from 0 to 0.65, indicating a wide variation of plan quality nationwide. Results also reported 5 out of 21 plans achieved all dose objectives and constraints showing more professional training is needed for planners in Malaysia. Understanding of treatment planning system and computational physics could also help in improving the quality of treatment plans for IMRT delivery.

Evaluation of a highly refined prediction model in knowledge-based volumetric modulated arc therapy planning for cervical cancer

Background and purpose

To explore whether a highly refined dose volume histograms (DVH) prediction model can improve the accuracy and reliability of knowledge-based volumetric modulated arc therapy (VMAT) planning for cervical cancer.

Methods and materials

The proposed model underwent repeated refining through progressive training until the training samples increased from initial 25 prior plans up to 100 cases. The estimated DVHs derived from the prediction models of different runs of training were compared in 35 new cervical cancer patients to analyze the effect of such an interactive plan and model evolution method. The reliability and efficiency of knowledge-based planning (KBP) using this highly refined model in improving the consistency and quality of the VMAT plans were also evaluated.

Results

The prediction ability was reinforced with the increased number of refinements in terms of normal tissue sparing. With enhanced prediction accuracy, more than 60% of automatic plan-6 (AP-6) plans (22/35) can be directly approved for clinical treatment without any manual revision. The plan quality scores for clinically approved plans (CPs) and manual plans (MPs) were on average 89.02 ± 4.83 and 86.48 ± 3.92 (p < 0.001). Knowledge-based planning significantly reduced the D_mean and V_{18 Gy} for kidney (L/R), the D_mean, V_{30 Gy}, and V_{40 Gy} for bladder, rectum, and femoral head (L/R).

Conclusion

The proposed model evolution method provides a practical way for the KBP to enhance its prediction ability with minimal human intervene. This highly refined prediction model can better guide KBP in improving the consistency and quality of the VMAT plans.

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

Background

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

Materials and methods

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

Results

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

Conclusion

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

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

Purpose

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

Materials and Methods

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

Results

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

Conclusion

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

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.

A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning

PURPOSE

To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy treatment plans.

METHODS

Eighty cases of early-stage nasopharyngeal cancer (NPC) were included in the study. Seventy cases were chosen randomly as the training set and the remaining as the test set. The inputs were the images with structures, with each target and organs at risk (OARs) assigned a unique label. The outputs were dose maps, including coarse dose maps and converted fine dose maps (FDM) from convolution. Two types of input images with structures were used in the model building. One type of input included the images (with associated structures) without manipulation. The second type of input involved modifying the image gray label with information from radiation beam geometry. ResNet101 was chosen as the deep learning network for both. The accuracy of predicted dose distributions was evaluated against the corresponding dose as used in the clinic. A global three-dimensional gamma analysis was calculated for the evaluation.

RESULTS

The proposed model trained with the two different sets of input images and structures could both predict patient-specific dose distributions accurately. For the out-of-field dose distributions, the model obtained from the input with radiation geometry performed better (dose difference in %, 4.7 ± 6.1% vs 5.5 ± 7.9%, P < 0.05). The mean Gamma pass rates of dose distributions predicted with both types of input were comparable for most OARs (P > 0.05), except for the bilateral optic nerves and the optic chiasm.

CONCLUSIONS

The proposed system with radiation geometry added to the input is a promising method to generate patient-specific dose distributions for radiotherapy. It can be applied to obtain the dose distributions slice-by-slice for planning quality assurance and for guiding automated planning.

Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy

Background

Automatic multi-criteria optimization is necessary for intensity modulated radiation therapy (IMRT) because of low planning efficiency and large plan quality uncertainty in current clinical practice. Most studies focused on imitating dosimetrists’ planning procedures to automate this process and ignored the fact that organ-based objective functions typically used in commercial treatment planning systems (such as dose-volume function) usually lead to sub-optimal plans. To guarantee the optimum results and to automate this process, we incorporate an improved automation strategy and a voxel-based optimization algorithm to generate a novel automatic multi-criteria optimization framework. We then evaluate it in clinical cases.

Methods

This novel automatic multi-criteria optimization framework incorporates a ranked priority-list based automatic constraints adjustment strategy and an in-house developed voxel-based optimization algorithm. Constraints are sequentially adjusted following a pre-defined priority list. Afterward, a voxel-based fluence map optimization (FMO) with an orientation to the newly updated constraints is launched to find a Pareto optimal solution. Loops of constraints adjustment are repeated until each of them could not be relaxed or tightened. The feasibility of the framework is evaluated with 10 automatic generated gynecology (GYN) cancer IMRT cases by comparing the dosimetric performance with the original.

Results

Plan quality improvement is observed for our automatic multi-criteria optimization method. Comparable DVHs are found for the planning target volume (PTV), but with better organs-at-risk (OAR) dose sparing. Among 13 evaluated dosimetric endpoints, 5 of them show significant improvements in automatically generated plans compared with the original plans. Investigation of improvement tendency during optimization exhibits gradual change as the optimization stage proceeds. An initial voxel-based optimization stage and in-low-priority dosimetric criteria tighten can significantly contribute to the optimization procedure.

Conclusions

We have successfully developed an automatic multi-criteria optimization framework that can dramatically reduce the current trial-and-error patterned planning workload while affording an efficient method to assure high plan quality consistency. This optimization framework is expected to greatly facilitate precise radiation therapy because of its advantages of planning efficiency and plan quality improvement.

Evaluation of multiple institutions' models for knowledge-based planning of volumetric modulated arc therapy (VMAT) for prostate cancer

Background

The aim of this study was to evaluate the performance of a commercial knowledge-based planning system, in volumetric modulated arc therapy for prostate cancer at multiple radiation therapy departments.

Methods

In each institute, > 20 cases were assessed. For the knowledge-based planning, the estimated dose (ED) based on geometric and dosimetric information of plans was generated in the model. Lower and upper limits of estimated dose were saved as dose volume histograms for each organ at risk. To verify whether the models performed correctly, KBP was compared with manual optimization planning in two cases. The relationships between the EDs in the models and the ratio of the OAR volumes overlapping volume with PTV to the whole organ volume (V_overlap/V_whole) were investigated.

Results

There were no significant dosimetric differences in OARs and PTV between manual optimization planning and knowledge-based planning. In knowledge-based planning, the difference in the volume ratio of receiving 90% and 50% of the prescribed dose (V90 and V50) between institutes were more than 5.0% and 10.0%, respectively. The calculated doses with knowledge-based planning were between the upper and lower limits of ED or slightly under the lower limit of ED. The relationships between the lower limit of ED and V_overlap/V_whole were different among the models. In the V90 and V50 for the rectum, the maximum differences between the lower limit of ED among institutes were 8.2% and 53.5% when V_overlap/V_whole for the rectum was 10%. In the V90 and V50 for the bladder, the maximum differences of the lower limit of ED among institutes were 15.1% and 33.1% when V_overlap/V_whole for the bladder was 10%.

Conclusion

Organs’ upper and lower limits of ED in the models correlated closely with the V_overlap/V_whole. It is important to determine whether the models in KBP match a different institute’s plan design before the models can be shared.

Evaluating inter-campus plan consistency using a knowledge based planning model

BACKGROUND AND PURPOSE

We investigate whether knowledge based planning (KBP) can identify systematic variations in intensity modulated radiotherapy (IMRT) plans between multiple campuses of a single institution.

MATERIAL AND METHODS

A KBP model was constructed from 58 prior main campus (MC) esophagus IMRT radiotherapy plans and then applied to 172 previous patient plans across MC and 4 regional sites (RS). The KBP model predicts DVH bands for each organ at risk which were compared to the previously planned DVHs for that patient.

RESULTS

RS1's plans were the least similar to the model with less heart and stomach sparing, and more variation in liver dose, compared to MC. RS2 produced plans most similar to those expected from the model. RS3 plans displayed more variability from the model prediction but overall, the DVHs were no worse than those of MC. RS4 did not present any statistically significant results due to the small sample size (n=11).

CONCLUSIONS

KBP can retrospectively highlight subtle differences in planning practices, even between campuses of the same institution. This information can be used to identify areas needing increased consistency in planning output and subsequently improve consistency and quality of care.

Bulk evaluation and comparison of radiotherapy treatment plans for breast cancer

This study provides a bulk, retrospective analysis of 151 breast and chest wall radiotherapy treatment plans, as a small-scale demonstration of the potential breadth and value of the information that may be obtained from clinical data mining. The treatments were planned at three centres belonging to one organisation over a period of 3 months. All 151 plans were used to evaluate inter-centre consistency and compliance with a local planning protocol. A subset of 79 plans, from one centre, were used in a more detailed evaluation of the effects of anatomical asymmetry on heart and lung dose, the effects of a metallic temporary tissue expander port on dose homogeneity and the overall conformity and homogeneity achieved in routine breast treatment planning. Differences in anatomical structure contouring and nomenclature were identified between the three centres, with all centres showing some non-compliance with the local planning protocol. When evaluated against standard conformity indices, these breast plans performed relatively poorly. However, when evaluated against recommended organ-at-risk tolerances, all evaluated plans performed sufficiently well that tighter planning tolerances could be recommended for future planning. Heart doses calculated in left breast and chest wall treatments were significantly higher than heart doses calculated in right sided breast and chest wall treatments (p < 0.001). In the treatment involving a temporary tissue expander, the inflated implant effectively pushed the targeted breast tissue away from the healthy tissues, leading to a dose distribution that was relatively conformal, although attenuation through the tissue expander's metallic port may have been underestimated by the treatment planning system. The results of this study exemplify the use of bulk treatment planning data to evaluate clinical workloads and inform ongoing treatment planning.

Interobserver variability in radiation therapy plan output: Results of a single-institution study

PURPOSE

We investigated the sources of variability in radiation therapy treatment plan output between planners within a single institution.

METHODS AND MATERIALS

Forty treatment planners across 5 campuses of an institution created a plan on the same thoracic esophagus patient computed tomography scan and structure set. Plans were scored and ranked based on the planner's adherence to an ordered list of target dose coverage and normal tissue evaluation criteria. A runs test was used to identify whether any of the studied planner qualities influenced the ranking. Spearman rank correlation was used to investigate whether plan score correlated with years of experience or planned monitor units.

RESULTS

The distribution of scores, ranging from 80.24 to 135.89, was negatively skewed (mean, 128.7; median, 131.5). No statistically significant relationship between plan score and campus (P = .193), job title (P = .174), previous outside experience (P = .611), or number of gantry angles (P = .156) was discovered. No statistical correlation between plan score and monitor unit or years of experience was found.

CONCLUSIONS

Despite clear and established critical organ dose criteria and well-documented planning guidelines, planning variation still occurs, even among members of the same institution. Because plan consistency does not seem to significantly correlate with experience, career path, or campus, investigation into alternate methods beyond additional education and training to reduce this variation, such as knowledge-based planning or advanced optimization techniques, is necessary.

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

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

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

PURPOSE

To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning.

METHODS

The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans.

RESULTS

The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency.

CONCLUSIONS

A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment planning.

Quality assurance for the EORTC 22071-26071 study: dummy run prospective analysis

Purpose

The phase III 22071–26071 trial was designed to evaluate the addition of panitumumab to adjuvant chemotherapy plus intensity modulated radiotherapy (IMRT) in locally advanced resected squamous cell head and neck cancer. We report the results of the dummy run (DR) performed to detect deviations from protocol guidelines.

Methods and Materials

DR datasets consisting of target volumes, organs at risk (OAR) and treatment plans were digitally uploaded, then compared with reference contours and protocol guidelines by six central reviewers. Summary statistics and analyses of potential correlations between delineations and plan characteristics were performed.

Results

Of 23 datasets, 20 (87.0%) GTVs were evaluated as acceptable/borderline, along with 13 (56.5%) CTVs and 10 (43.5%) PTVs. All PTV dose requirements were met by 73.9% of cases. Dose constraints were met for 65.2-100% of mandatory OARs. Statistically significant correlations were observed between the subjective acceptability of contours and the ability to meet dose constraints for all OARs (p ≤ 0.01) except for the parotids and spinal cord. Ipsilateral parotid doses correlated significantly with CTV and PTV volumes (p ≤ 0.05).

Conclusions

The observed wide variations in treatment planning, despite strict guidelines, confirms the complexity of development and quality assurance of IMRT-based multicentre studies for head and neck cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-014-0248-9) contains supplementary material, which is available to authorized users.

How important is dosimetrist experience for intensity modulated radiation therapy? A comparative analysis of a head and neck case

PURPOSE

Treatment planning for IMRT is a complex process that requires additional training and expertise. The aim of this study was to compare and analyze IMRT plans generated by dosimetrists with varying levels of IMRT planning experience.

METHODS AND MATERIALS

The computed tomography (CT) data of a patient previously treated with IMRT for left tonsillar carcinoma were used. The patient's preexisting planning target volumes (PTVs) and all organs at risk were provided with the CT data set. Six dosimetrists with variable IMRT planning experience generated IMRT plans according to the department's protocol. Plan analysis included visual inspection and comparison of dose-volume histogram, conformity indices, treatment delivery efficiency, and dose delivery accuracy.

RESULTS

Visual review of the dose distribution showed that the 6 plans were comparable. However, only the 2 most experienced dosimetrists were able to meet the strict PTV aims and critical structure constraints. The least experienced dosimetrist had the worst planning outcome. Comparison of delivery efficiency showed that the number of segments, total monitor units, and treatment time increased as the IMRT planning experience decreased.

CONCLUSIONS

Dosimetrists with higher levels of IMRT planning experience produced a better quality head and neck IMRT plan. Different planning experience may need to be considered when organizing appropriate departmental resources.

Found in translation: Integrating laboratory and clinical oncology research

Translational research in medicine aims to inform the clinic and the laboratory with the results of each other’s work, and to bring promising and validated new therapies into clinical application. While laudable in intent, this is complicated in practice and the current state of translational research in cancer shows both striking success stories and examples of the numerous potential obstacles as well as opportunities for delays and errors in translation. This paper reviews the premises, promises, and problems of translational research with a focus on radiation oncology and suggests opportunities for improvements in future research design.