Rapid Planning Research Articles

Rapid Selection of Patients Suitable for Deep Inspiration Breath-Hold Using an Automatic Delineating System and RapidPlan Model in Patients With Left Breast Cancer Undergoing Adjuvant Radiation Therapy With IMRT

PurposeThis study aimed to determine whether radiotherapy plans created using an automatic delineating system and a RapidPlan (RP) module could rapidly and accurately predict heart doses and benefit from deep inspiratory breath-hold (DIBH)in left breast cancer patients. Methods and MaterialsOne hundred thirty-six clinically approved free breathing (FB) plans for patients with left breast cancer were included, defined as manual delineation–manual plan (MD–MP). A total of 104/136 plans were selected for RP model training. A total of 32/136 patients were automatically delineated by software, after which the RP generated plans, defined as automatic delineation–RapidPlan (AD–RP). In addition, 40 patients who used DIBH were included to analyze differences in heart benefits from DIBH. ResultsTwo RP models were established for post breast-conserving surgery (BCS) and post modified radical mastectomy (MRM). There were no significant differences in most of the dosimetric parameters between the MD–MP and AD–RP. The heart doses of the two plans were strongly correlated in patients after BCS (0.80 ≤ r ≤ 0.88, P < 0.05) and moderately correlated in patients after MRM (0.46 ≤ r ≤ 0.58, P < 0.05). The RP model predicted the mean heart dose (MHD) within ± 59.67 cGy and ± 63.32 cGy for patients who underwent the two surgeries described above. The heart benefits from DIBH were significantly greater in patients with FB-MHD ≥ 4 Gy than in those with FB-MHD < 4 Gy. ConclusionsThe combined automatic delineation RP model allows for the rapid and accurate prediction of heart dose under FB in patients with left breast cancer. FB-MHD ≥ 4 Gy can be used as a dose threshold to select patients suitable for DIBH.

Using dose volume histogram (DVH) predictions to improve the plan quality of helical tomotherapy (HT)

This study aimed to investigate whether the RapidPlan (RP) model configured by volumetric modulated arc therapy (VMAT) plans of nasopharyngeal carcinoma (NPC) could be used to assist in the optimization of HT plans and improve their quality.An RP model was trained using 100 clinically accepted VMAT plans of NPC patients. The predicted dose constraints of the VMAT trained RP model were used to reoptimize 25 consecutive clinically accepted HT plans (HT_clinical) and perform new VMAT plans based on the same computed topography (CT). The dosimetric quality of the reoptimized HT plans (HT_reoptimized), HT_clinical, and VMAT group were compared. The minimum dose encompassing 2% target (D2%), the minimum dose encompassing 98% target (D98%), homogeneity index (HI) and conformity index (CI) were similar for most targets between the HT_clinical and HT_reoptimized plans, although certain targets in the HT_reoptimized plans had higher D2% and HI and lower D98%. The HT_reoptimized plans outperformed the HT_clinical plans in the Dmax and D1cc of the spinal cord, V40Gy of the left temporal lobe, Dmean and V30Gy of the oral cavity, Dmean of the larynx and thyroid, and the differences were statistically significant. HT plans had higher CI and HI than VMAT plans. HT plans outperformed VMAT plans in the Dmax of the spinal cord and lenses, V30Gy of the oral cavity and parotids, and V40Gy of the temporal lobes, but underperformed in the Dmax and D1cc of the brainstem, D1cc of the spinal cord and Dmean of the oral cavity.The VMAT-based RP model can be used to assist in the planning of HT plans and improve the dosimetry quality of HT plans.

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

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. 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. 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. 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. 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.

Multi-planner validation of RapidPlan knowledge-based model for volumetric modulated arc therapy in prostate cancer.

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

Multi-Criteria Optimization and RapidPlan for Improved Organs at Risk Sparing in Treatment Planning of Malignant Pleural Mesothelioma

Treatment planning for malignant pleural mesothelioma is complex and time-consuming owing to the large volume of the target as well as its overlap and proximity to critical organs. Knowledge-based planning (KBP) model using RapidPlan (RP) was previously developed and clinically tested at our institution for treatment planning of these cases that had undergone pleurectomy-decortication, and therefore had two intact-lungs. The aim of this work is to investigate if multi-criteria optimization (MCO) can further improve the RP model with respect to organ at risk (OAR) sparing without compromising target coverage. The RP model was trained with clinically accepted plans of 57 patients that used Volumetric Modulated Arc Therapy (VMAT) with 2 partial arcs and 6 MV photons. The dose volume histogram (DVH) estimation model was trained to estimate doses to the heart, ipsilateral lung, total lung, contralateral lung, stomach, esophagus, kidneys and liver. Clinical treatment plans for 12 patients were re-planned using RP as well as a combination of RP and MCO. Application of MCO was after RP and used tradeoff exploration to navigate to the improved dose distribution on a Pareto surface. Selection of a plan that further improved OAR sparing while maintaining coverage constraints of PTV D95 and V95 ≥ 94% was made. Dosimetric parameters for clinical plans (CP), plans using RP as well as plans generated with RP and MCO were all compared for 12 new validation cases. Wilcoxon sign-rank test was used for statistical significance testing. The mean heart dose was reduced from 20.9 Gy ± 3.2 Gy for CP to 17.1 Gy ± 4.2 Gy with RP and further to 13.8 Gy ± 3.3 Gy with RP and MCO. The heart V30 Gy was reduced from 23.7% ± 8% to 18.2% ± 9.4% with RP and further to 13.6% ± 5.7% with RP and MCO. The corresponding results for contralateral lung V5 Gy were 68.6% ± 14.1% with CP, 50% ± 26.3% with RP and 45.3% ± 22.8% with RP and MCO, while the mean esophagus dose was reduced from 26.5 Gy ± 3.1 Gy to 21.5 Gy ± 4.7 Gy with RP and further to 19.1 Gy ± 4.2 Gy with RP and MCO. All these dosimetric improvements were statistically significant (p<0.001). However, improvements with RP and MCO for the total lung mean and V20 Gy, liver mean, stomach mean and kidney V18 Gy were marginal over the RP. Sparing of ipsilateral lung V20 Gy was maintained at ≥ 50 cc on average for all plans. PTV D95 and V95 were both normalized at 94%. Combination of RP and MCO significantly improved sparing of the OARs, especially the heart, contralateral lung and esophagus without compromising coverage or doses to other structures. Since doses to the heart, and contralateral lung are known to correlate with incidence of radiation pneumonitis, it would be prudent to consider planning with both RP and MCO to help determine the optimal treatment plan for the individual patient's anatomy.

Development and Assessment of a Multi-Purpose Knowledge-Based Planning Model (RapidPlan) for Prostate Radiation

RapidPlan (RP) is a knowledge-based planning (KBP) tool to improve plan quality, planning speed, and reduce inter-patient plan variability. For small to medium institutions, it is difficult to find sufficient patient numbers to configure a reliable KBP-RP model. This study aimed to build a multi-purpose KBP-RP model for prostate cancers. A total of 124 prostate plans from 2018-2022 at our institution were selected to configure the initial RP model. After model training, outliers were removed. 77 patients were used for the final RP model. Three fine-tuned RP sub-models with different optimized constraints corresponding to prostate bed, prostate + seminal vesicles (SV), and prostate boost plans were generated. RP models were validated by comparing plan quality with the original manually optimized plans (MP). 54 independent plans were selected to validate the RP models. Validation included: dose to 99% and 2% of PTV (D99%, D2%), PTV conformity index (CI); for organs at risk (OARs), volume receiving >70Gy and >60Gy (V70Gy, V60Gy) to bladder and rectum, maximum dose (Dmax) to femur heads (FHs) and small bowel. Statistical t-test analysis was performed with a significance of p<0.05. A total of 48/54 model-based plans were clinically approved after single RP optimization. 6/54 failed plans were prostate bed plans and were deemed acceptable after additional minor constraint adjustments. For PTV, D99%, D2%, and CI were comparable (p>0.05) to MP. Bladder and rectum V70Gy were comparable (p>0.05), and V60Gy were significantly lower to MP (p<0.05), with an average Dmean of 23.21±14.58Gy and 16.41±10.63Gy vs 26.36±16.89Gy and 18.24±12.81Gy for RP and MP. RP significantly reduced Dmax to FHs and small bowel (p<0.05), with average Dmax of 34.95±6.06Gy and 35.62±18.99Gy vs 36.81±7.05Gy and 38.14±17.81Gy, respectively. Multi-purpose prostate RP model was configured and approved plans were generated after single optimization. Prostate RP plans had equivalent PTV coverage with better or comparable OAR constraints.

Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer

Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer. Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board-approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review. Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were "per protocol" or met "variation acceptable" ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes. A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.

Training and validation of a knowledge-based dose-volume histogram predictive model in the optimisation of intensity-modulated proton and volumetric modulated arc photon plans for pleural mesothelioma patients

BackgroundTo investigate the performance of a narrow-scope knowledge-based RapidPlan (RP) model for optimisation of intensity-modulated proton therapy (IMPT) and volumetric modulated arc therapy (VMAT) plans applied to patients with pleural mesothelioma. Second, estimate the potential benefit of IMPT versus VMAT for this class of patients.MethodsA cohort of 82 patients was retrospectively selected; 60 were used to "train" a dose-volume histogram predictive model; the remaining 22 provided independent validation. The performance of the RP models was benchmarked, comparing predicted versus achieved mean and near-to-maximum dose for all organs at risk (OARs) in the training set and by quantitative assessment of some dose-volume metrics in the comparison of the validation RP-based data versus the manually optimised training datasets. Treatment plans were designed for a prescription dose of 44 Gy in 22 fractions (proton doses account for a fixed relative biological effectiveness RBE = 1.1).ResultsTraining and validation RP-based plans resulted dosimetrically similar for both VMAT and IMPT groups, and the clinical planning aims were met for all structures. The IMPT plans outperformed the VMAT ones for all OARs for the contra-lateral and the mean and low dose regions for the ipsilateral OARs. Concerning the prediction performance of the RP models, the linear regression for the near-to-maximum dose resulted in Dachieved = 1.03Dpredicted + 0.58 and Dachieved = 1.02Dpredicted + 1.46 for VMAT and IMPT, respectively. For the mean dose it resulted: Dachieved = 0.99Dpredicted + 0.34 and Dachieved = 1.05Dpredicted + 0.27 respectively. In both cases, the linear correlation between prediction and achievement is granted with an angular coefficient deviating from unity for less than 5%. Concerning the dosimetric comparison between manual plans in the training cohort and RP-based plans in the validation cohort, no clinical differences were observed for the target volumes in both the VMAT and IMPT groups. Similar consistency was observed for the dose-volume metrics analysed for the OAR. This proves the possibility of achieving the same quality of plans with manual procedures (the training set) or with automated RP-based methods (the validation set).ConclusionTwo models were trained and validated for VMAT and IMPT plans for pleural mesothelioma. The RP model performance resulted satisfactory as measured by the agreement between predicted and achieved (after full optimisation) dose-volume metrics. The IMPT plans outperformed the VMAT plans for all the OARs (with different intensities for contra- or ipsilateral structures). RP-based planning enabled the automation of part of the optimisation and the harmonisation of the dose-volume results between training and validation. The IMPT data showed a systematic significant dosimetric advantage over VMAT. In general, using an RP-based approach can simplify the optimisation workflow in these complex treatment indications without impacting the quality of plans.

A practical method to improve the performance of knowledge-based VMAT planning for endometrial and cervical cancer

Purpose The aim of this work was to demonstrate a practical and effective method to improve the performance of RapidPlan (RP) model. Methods 203 consecutive clinical VMAT plans (P0) for cervical and endometrial cancer were used to train an RP model (M0). The plans were then reoptimized by M0 to generate 203 new plans (P1). Compared with P0, 150 plans with a lower mean dose (MD) of bladder, rectum and PBM were selected from P1 to configure a new RP model (M1). A final RP model (M2) was trained using plans in M1 and the remaining 53 plans from P1 (excluding OARs with worse MD) and the corresponding plans from P0 (only including OARs with better MD). The models were validated on the mentioned 53 plans (closed-loop set) and 46 patient cohorts outside the training library (open-loop set). p < 0.05 was considered statistically significant. Results For closed-loop validation, the difference of D2%, D98% and CI95% between groups was of no statistical significance, the homogeneity index (HI) was lower in the groups of RP models (p < 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M0, M1 and M2, except the MD of bowel in M1 and MD of LFH in M2. Similarly, for open-loop validation, there was no significant difference in D2%, D98% and HI between groups, but CI95% was larger in the clinical group (p < 0.05). The MD of all OARs decreased monotonically in the sequence of the clinical group, group M0, M1 and M2, with the exception of bowel in M1. Conclusion The practical method of incorporating plan data of better-sparing OARs from both the clinical VMAT plans and the re-optimized plans could further improve the performance of the RP model.

Validation of RapidPlan Knowledge-Based Model for Volumetric-Modulated Arc Therapy in Prostate Cancer.

This study aims to investigate the viability of using RapidPlan (RP) knowledge-based (KB) treatment plans to initiate the new prostate volumetric-modulated arc therapy (VMAT) plans. The planning data for 120 prostate VMAT treatment plans were entered into the RP system's database. The database of previous VMAT plans was divided into four model groups for training in the RP system. The models were based on the numbers of 20, 60, and 120 prostate VMAT plans. The model of 120 plans used automated priority and manual priority for the optimization process. The models of 20 and 60 plans used only manual priority for optimization. Each model was validated on 15 cases of new prostate cancer patients by comparing RP model plans against manual clinical plans optimized according to the clinical dose constraints. The RP models can estimate the dose comparable target volume to the manual clinical plan, which evaluated values of Dmax, D95%, D98%, HI, and CI and showed comparable results. For the normal organ doses of the bladder, rectum, penile bulb, and femoral head, all RP models exhibited a comparable or better dose than the manual clinical plan, except for the RP models using the automated priority for the optimization process, which cannot control the rectum dose below the dose constraints. The Varian RP KB planning can produce comparable doses or better doses with the clinical manual in a single optimization, although the RP model uses a minimum requirement of the planning number for the model training. The RP models can enhance the efficacy and quality of plans, which depend on the number of VMAT plans used in RP model training for prostate cancer.

Fully automated planning and delivery of hippocampal-sparing whole brain irradiation.

The goal of this study is to fully automate the treatment planning and delivery process of hippocampal-sparing whole brain irradiation (HS-WBRT) by combining a RapidPlan (RP) knowledge-based planning model and HyperArc (HA) technology. Additionally, this study compares the dosimetric performance of RapidPlan-HyperArc (RP-HA) treatment plans with RP plans and volumetric modulated arc therapy (VMAT) plans. Ten patients previously treated with HS-WBRT using conventional VMAT were re-planned using RP-HA technique and RP model for HS-WBRT. Treatment plans were generated for 30Gy in 3Gy fractions using 6MV photon beam on a TrueBeam linear accelerator (Varian Medical Systems, Palo Alto, CA) equipped with high definition multileaf collimator (HDMLC). Target coverage, homogeneity index (HI), Paddick Conformity index (CI), dose to organs-at-risk (OARs) provided by the 3 planning modalities were compared, and a paired t-test was performed. Total number of monitor units (MU), effective planning time and beam-on-time time were reported and evaluated for each plan. RP-HA plans achieved on average a 4% increase in D98% of PTV, a 26% improvement in HI, a 2.3% increase in CI, when compared to RP plans. Furthermore, RP-HA plans provided on average 11% decrease in D100% of hippocampi when compared to VMAT plans. All RP-HA plans were generated in less than 30 minutes while RP plans took 40 minutes and VMAT plans required on average 9 hours to complete. Regarding beam-on-time time, it was estimated that RP-HA plans take on average 5 minutes to deliver while RP and VMAT plans require 6.5 and 10 minutes, respectively. RP-HA method provides fully automated planning and delivery for HS-WBRT. The auto-generated plans together with automated treatment delivery allow standardization of plan quality, increased efficiency and ultimately improved patient care.

Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study.

BackgroundThe aim of this study was to investigate the performance of the RapidPlan (RP ) using models registered pseudostructures, and to determine how many structures are required for automatic optimization of volumetric modulated arc therapy (VMAT) for postoperative uterine cervical cancer.Materials and methodsPseudo-structures around the PTV were retrospectively contoured for patients who had completed treatment at five institutions. For 22 common patients, plans were generated with a single optimization for models with two (RP_2), four (RP_4), and five (RP_5) registered structures, and the dosimetric parameters of these models were compared with a clinical plan with several optimizations.ResultsMost dosimetric parameters showed no major differences between each RP model. In particular, the rectum Dmax, V50Gy, and V40Gy with RP_2, RP_4, and RP_5 were not significantly different, and were lower than those of the clinical plan. The average proportions of plans achieving acceptable criteria for dosimetric parameters were close to 100% for all models. Using RP_2, the average time for the VMAT planning was reduced by 88 minutes compared with the clinical plan.ConclusionThe RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time.

Replacing Manual Planning of Whole Breast Irradiation With Knowledge-Based Automatic Optimization by Virtual Tangential-Fields Arc Therapy.

PurposeTo implement Knowledge Based (KB) automatic planning for right and left-sided whole breast treatment through a new volumetric technique (ViTAT, Virtual Tangential-fields Arc Therapy) mimicking conventional tangential fields (TF) irradiation.Materials and MethodA total of 193 clinical plans delivering TF with wedged or field-in-field beams were selected to train two KB-models for right(R) and left(L) sided breast cancer patients using the RapidPlan (RP) tool implemented in the Varian Eclipse system. Then, a template for ViTAT optimization, incorporating individual KB-optimized constraints, was interactively fine-tuned. ViTAT plans consisted of four arcs (6 MV) with start/stop angles consistent with the TF geometry variability within our population; the delivery was completely blocked along the arcs, apart from the first and last 20° of rotation for each arc. Optimized fine-tuned KB templates for automatic plan optimization were generated. Validation tests were performed on 60 new patients equally divided in R and L breast treatment: KB automatic ViTAT-plans (KB-ViTAT) were compared against the original TF plans in terms of OARs/PTVs dose-volume parameters. Wilcoxon-tests were used to assess the statistically significant differences.ResultsKB models were successfully generated for both L and R sides. Overall, 1(3%) and 7(23%) out of 30 automatic KB-ViTAT plans were unacceptable compared to TF for R and L side, respectively. After the manual refinement of the start/stop angles, KB-ViTAT plans well fitted TF-performances for these patients as well. PTV coverage was comparable, while PTV D1% was improved with KB-ViTAT by R:0.4/L:0.2 Gy (p < 0.05); ipsilateral OARs Dmean were similar with a slight (i.e., few % volume) improvement/worsening in the 15–35 Gy/2–15 Gy range, respectively. KB-ViTAT better spared contralateral OARs: Dmean of contralateral OARs was 0.1 Gy lower (p < 0.05); integral dose was R:5%/L:8% lower (p < 0.05) than TF. The overall time for the automatic plan optimization and final dose calculation was 12 ± 2 minutes.ConclusionsFully automatic KB-optimization of ViTAT can efficiently replace manually optimized TF planning for whole breast irradiation. This approach was clinically implemented in our institute and may be suggested as a large-scale strategy for efficiently replacing manual planning with large sparing of time, elimination of inter-planner variability and of, seldomly occurring, sub-optimal manual plans.

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

PurposeThe 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.MethodsA 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.ResultsSignificant differences (p < 0.05) between RP1 and RP2 were found in Dmean (101.5% vs. 101.9%), homogeneity index (3.90 vs. 4.44), 95% isodose conformity index (1.22 vs. 1.20) for the target, V40Gy (47.3% vs. 45.7%), V60Gy (27.9% vs. 27.1%), V70Gy (16.4% vs. 15.2%), and V78Gy (0.4% vs. 0.2%) for the rectal wall, and V40Gy (43.8% vs. 41.8%) and V70Gy (21.3% vs. 20.5%) for the bladder wall, whereas only V70Gy (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.ConclusionsThe 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.

Implementation of a Knowledge-Based Treatment Planning Model for Cardiac-Sparing Lung Radiation Therapy

PurposeHigh radiation doses to the heart have been correlated with poor overall survival in patients receiving radiation therapy for stage III non-small cell lung cancer (NSCLC). We built a knowledge-based planning (KBP) tool to limit the dose to the heart during creation of volumetric modulated arc therapy (VMAT) treatment plans for patients being treated to 60 Gy in 30 fractions for stage III NSCLC. Methods and MaterialsA previous study at our institution retrospectively delineated intracardiac volumes and optimized VMAT treatment plans to reduce dose to these substructures and to the whole heart. Two RapidPlan (RP) KBP models were built from this cohort, 1 model using the clinical plans and a separate model using the cardiac-optimized plans. Using target volumes and 6 organs at risk (OARs), models were trained to generate treatment plans in a semiautomated process. The cardiac-sparing KBP model was tested in the same cohort used for training, and both models were tested on an external validation cohort of 30 patients. ResultsBoth RP models produced clinically acceptable plans in terms of target coverage, dose uniformity, and dose to OARs. Compared with the previously created cardiac-optimized plans, cardiac-sparing RPs showed significant reductions in the mean dose to the esophagus and lungs while performing similarly or better in all evaluated heart dose metrics. When comparing the 2 models, the cardiac-sparing RP showed reduced (P < .05) heart mean and maximum doses as well as volumes receiving 60 Gy, 50 Gy, and 30 Gy. ConclusionsBy using a set of cardiac-optimized treatment plans for training, the proposed KBP model provided a means to reduce the dose to the heart and its substructures without the need to explicitly delineate cardiac substructures. This tool may offer reduced planning time and improved plan quality and might be used to improve patient outcomes.

Knowledge-based intensity-modulated proton planning for gastroesophageal carcinoma

Purpose To investigate the performance of a narrow-scope knowledge-based RapidPlan (RP) model, for optimisation of intensity-modulated proton therapy (IMPT) plans applied to patients with locally advanced carcinoma in the gastroesophageal junction. Methods A cohort of 60 patients was retrospectively selected; 45 were used to ‘train’ a dose-volume histogram predictive model; the remaining 15 provided independent validation. The performance of the RP model was benchmarked against manual optimisation. Quantitative assessment was based on several dose-volume metrics. Results Manual and RP-optimised IMPT plans resulted dosimetrically similar, and the planning dose-volume objectives were met for all structures. Concerning the validation set, the comparison of the manual vs RP-based plans, respectively, showed for the target (PTV): the homogeneity index was 6.3 ± 2.2 vs 5.9 ± 1.2, and V98% was 89.3 ± 2.9 vs 91.4 ± 2.2% (this was 97.2 ± 1.9 vs 98.8 ± 1.1 for the CTV). Regarding the organs at risk, no significant differences were reported for the combined lungs, the whole heart, the left anterior descending artery, the kidneys, the spleen and the spinal canal. The D0.1 cm3 for the left ventricle resulted in 40.3 ± 3.4 vs 39.7 ± 4.3 Gy(RBE). The mean dose to the liver was 3.4 ± 1.3 vs 3.6 ± 1.5 Gy(RBE). Conclusion A narrow-scope knowledge-based RP model was trained and validated for IMPT delivery in locally advanced cancer of the gastroesophageal junction. The results demonstrate that RP can create models for effective IMPT. Furthermore, the equivalence between manual interactive and unattended RP-based optimisation could be displayed. The data also showed a high correlation between predicted and achieved doses in support of the valuable predictive power of the RP method.

RapidPlan for Knowledge-Based Planning of Malignant Pleural Mesothelioma

PurposeTreatment planning for malignant pleural mesothelioma is a challenging task due to the relatively large size of the target and the need to spare critical organs that overlap with or are within the target volume. We aimed to develop a knowledge-based model using RapidPlan (RP) for patients with 2 intact lungs. Methods and MaterialsData from 57 patients treated with volumetric modulated arc therapy were chosen for training the dose estimation model at a single dose level. The prescription dose was 50.4 Gy in 1.8 Gy fractions. The model was validated on 23 new patients by comparing the clinical plan to the RP. Time taken to plan the RP was compared with that for the clinical plan. ResultsFor similar target coverage and plan inhomogeneity, RP significantly improved the sparing of the contralateral lung, heart, stomach, esophagus, and ipsilateral kidney. On average, the contralateral lung V5 Gy and V10 Gy were reduced by 13.9% (P < .001) and 7.9% (P < .001), respectively. The mean heart dose was reduced by 5 Gy (P < .001) and V30 Gy by 9.1% (P < .001). Mean dose to the stomach and esophagus were both reduced by 5 Gy (P < .001), and the ipsilateral kidney V18 Gy by 4.1% (P < .001). Mean total lung dose was reduced by 0.8 Gy with RP, which enabled an increase in prescription dose by 1 fraction Absolute volume of ipsilateral lung was adequately spared by both techniques, while sparing of all other organs, namely the cord, liver, and bowel, was not compromised with RP. Time taken with RP was 20 minutes, 45 seconds versus at least 4 hours for an experienced treatment planner. ConclusionsThe RP model for malignant pleural mesothelioma showed improved sparing of critical organs with a reduced treatment planning time and increased prescription dose.

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

AbstractPurpose: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 &lt; 0·001). V65 Gy, V70 Gy and the mean dose to bladder were lower in RP than in MP (p &lt; 0·001). RP had enhanced the sparing of both femurs (p &lt; 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 &lt; 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.

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

IntroductionDifferences 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.MethodTwo 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.ResultsPlans 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.ConclusionCombining 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.

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

PurposeTo 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.MethodsClinical 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.ResultsThe 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 D1% 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.ConclusionA refined RP model allowed the generation of plans with improved quality, mostly for parallel organs at risk and, possibly, also the intrinsic model quality.