Evaluation of knowledge-based planning models for male pelvic CBCT-based online adaptive radiotherapy on conventional linear accelerators.

PURPOSE: To assess the practicality of employing a commercial knowledge-based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone-beam computed tomography (iCBCT) datasets. METHODS AND MATERIALS: Intact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose-volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan-generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow. RESULTS: Model testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed). CONCLUSION: This study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

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

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

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

BACKGROUND: Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans. MATERIAL AND METHODS: Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans. RESULTS: The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients. CONCLUSION: RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients.

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

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

Dosimetric potential of knowledge-based planning model trained with HyperArc plans for brain metastases.

OBJECTIVE: Dosimetric potential of knowledge-based RapidPlan planning model trained with HyperArc plans (Model-HA) for brain metastases has not been reported. We developed a Model-HA and compared its performance with that of clinical volumetric modulated arc therapy (VMAT) plans. METHODS: From 67 clinical stereotactic radiosurgery (SRS) HyperArc plans for brain metastases, 47 plans were used to build and train a Model-HA. The other 20 clinical HyperArc plans were recalculated in RapidPlan system with Model-HA. The model performance was validated with the 20 plans by comparing dosimetric parameters for normal brain tissue between clinical plans and model-generated plans. The 20 clinical conventional VMAT-based SRS or stereotactic radiotherapy plans (CL-VMAT) were reoptimized with Model-HA (RP) and HyperArc system (HA), respectively. The dosimetric parameters were compared among three plans (CL-VMAT vs. RP vs. HA) in terms of planning target volume (PTV), normal brain excluding PTVs (Brain - PTV), brainstem, chiasm, and both optic nerves. RESULTS: In model validation, the optimization performance of Model-HA was comparable to that of HyperArc system. In comparison to CL-VMAT, there were no significant differences among three plans with respect to PTV coverage (p > 0.17) and maximum dose for brainstem, chiasm, and optic nerves (p > 0.40). RP provided significantly lower V20 Gy , V12 Gy , and V4 Gy for Brain - PTV than CL-VMAT (p < 0.01). CONCLUSION: The Model-HA has the potential to significantly reduce the normal brain dose of the original VMAT plans for brain metastases.

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.

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.

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 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. 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.

A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.

BACKGROUND AND PURPOSE: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique. METHODS: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. RESULTS: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. CONCLUSIONS: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.

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

PURPOSE: To develop a robust and adaptable knowledge-based planning (KBP) model with commercially available RapidPlanTM for early stage, centrally located non-small-cell lung tumors (NSCLC) treated with stereotactic body radiotherapy (SBRT) and improve a patient's"simulation to treatment" time. METHODS: The KBP model was trained using 86 clinically treated high-quality non-coplanar volumetric modulated arc therapy (n-VMAT) lung SBRT plans with delivered prescriptions of 50 or 55 Gy in 5 fractions. Another 20 independent clinical n-VMAT plans were used for validation of the model. KBP and n-VMAT plans were compared via Radiation Therapy Oncology Group (RTOG)-0813 protocol compliance criteria for conformity (CI), gradient index (GI), maximal dose 2 cm away from the target in any direction (D2cm), dose to organs-at-risk (OAR), treatment delivery efficiency, and accuracy. KBP plans were re-optimized with larger calculation grid size (CGS) of 2.5 mm to assess feasibility of rapid adaptive re-planning. RESULTS: Knowledge-based plans were similar or better than n-VMAT plans based on a range of target coverage and OAR metrics. Planning target volume (PTV) for validation cases was 30.5 ± 19.1 cc (range 7.0-71.7 cc). KBPs provided an average CI of 1.04 ± 0.04 (0.97-1.11) vs. n-VMAT plan'saverage CI of 1.01 ± 0.04 (0.97-1.17) (P < 0.05) with slightly improved GI with KBPs (P < 0.05). D2cm was similar between the KBPs and n-VMAT plans. KBPs provided lower lung V10Gy (P = 0.003), V20Gy (P = 0.007), and mean lung dose (P < 0.001). KBPs had overall better sparing of OAR at the minimal increased of average total monitor units and beam-on time by 460 (P < 0.05) and 19.2 s, respectively. Quality assurance phantom measurement showed similar treatment delivery accuracy. Utilizing a CGS of 2.5 mm in the final optimization improved planning time (mean, 5 min) with minimal or no cost to the plan quality. CONCLUSION: The RTOG-compliant adaptable RapidPlan model for early stage SBRT treatment of centrally located lung tumors was developed. All plans met RTOG dosimetric requirements in less than 30 min of planning time, potentially offering shorter "simulation to treatment" times. OAR sparing via KBPs may permit tumorcidal dose escalation with minimal penalties. Same day adaptive re-planning is plausible with a 2.5-mm CGS optimizer setting.

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

BACKGROUND: The 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 METHODS: Pseudo-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. RESULTS: Most 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. CONCLUSION: The RapidPlan model with two registered pseudo-structures could generate clinically acceptable plans while saving time.

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 (Voverlap/Vwhole) were analyzed for each LP and VP. Regression lines between dose-volume parameters (V90, V75, and V50) and Voverlap/Vwhole 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 Voverlap/Vwhole 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 Voverlap/Vwhole. The relationship between dose-volume parameters and Voverlap/Vwhole of OARs in LP predicts the KBP models' performance sparing OARs.

Can automated treatment plans gain traction in the clinic?

Recently, there has been an increased interest in the feasibility and impact of automation within the field of medical dosimetry. While there have been many commercialized solutions for automatic treatment planning, the use of an application programming interface to achieve complete plan generation for specific treatment sites is a process only recently available for certain commercial vendors. Automatic plan generation for 20 prostate patients was achieved via a stand-alone automated planning script that accessed a knowledge-based planning solution. Differences between the auto plans and clinically treated, baseline plans were analyzed and compared. The planning script successfully initialized a treatment plan, accessed the knowledge-based planning model, optimized the plan, assessed for constraint compliance, and normalized the treatment plan for maximal coverage while meeting constraints. Compared to baseline plans, the auto-generated plans showed significantly improved rectal sparing with similar coverage for targets and comparable doses to the remaining organs-at-risk. Utilization of a script, with its associated time saving and integrated process management, can quickly and automatically generate an acceptable clinical treatment plan for prostate cancer with either improved or similar results compared to a manually created plan.

[Impact of DVH Outliers Registered in Knowledge-based Planning on Volumetric Modulated Arc Therapy Treatment Planning for Prostate Cancer].

RapidPlan, a knowledge-based planning software, uses a model library containing the dose-volume histogram (DVH) of previous treatment plans, and it automatically provides optimization objectives based on a trained model to future patients for volumetric modulated arc therapy treatment planning. However, it is unknown how DVH outliers registered in models influence the resulting plans. The purpose of this study was to investigate the effect of DVH outliers on the resulting quality of RapidPlan knowledge-based plans generated for patients with prostate cancer. First, 123 plans for patients with prostate cancer were used to populate the initial model (modelall). Next, modelall-20 and modelall-40 were created by excluding DVH outliers of bladder optimization contours 20 and 40 patients from modelall, respectively. These models were used to create plans for a 20-patient. The plans created using modelall-40 showed reductions of D30% and D50% in the bladder wall dose, and the DVH shape excluding outliers were affected. However, there were no significant differences in monitor units, target doses, or bladder wall doses between each treatment plan. Thus, we have shown that removal of DVH outliers from models does not affect the quality of plans created by the model.

Fully automated searching for the optimal VMAT jaw settings based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge-based planning.

PURPOSE: Eclipse treatment planning system has not been able to optimize the jaw positions for Volumetric Modulated Arc Therapy (VMAT). The arbitrary and planner-dependent jaw placements define the maximum field size within which multi-leaf-collimator (MLC) sequences can be optimized to modulate the beam. Considering the mechanical constraints of MLC transitional speed and range, suboptimal X jaw settings may impede the optimization or undermine the deliverability. This work searches optimal VMAT jaw settings automatically based on Eclipse Scripting Application Programming Interface (ESAPI) and RapidPlan knowledge-based planning. METHODS AND MATERIALS: Using an ESAPI script, the X jaws of rectal VMAT plans were initially set to conform the planning-target-volume (PTV), and were gradually extended toward the isocenter (PTV center) in 5-7 mm increments. Using these jaw pairs, 592 plans were automatically created for 10 patients and quantitatively evaluated using a comprehensive scoring function. A published RapidPlan model was evoked by ESAPI to generate patient-specific optimization objectives without manual intervention. All candidate plans were first stored as text files to save storage space, and only the best, worst, and conformal plans were consequently recreated for comparison. RESULTS: Although RapidPlan estimates dose-volume histogram (DVH) based on individual anatomy, the geometry-based expected dose (GED) algorithm does not recognize different jaw settings but uses PTV-conformal jaws as default; hence, identical DVHs were observed regardless of planner-defined jaws. Therefore, ESAPI finalized dose-volume calculation and eliminated the plans with unacceptable hotspots before comparison. The plan quality varied dramatically with different jaw settings. Trade-offs among different organs-at-risk (OARs) were collectively considered by the proposed scoring method, which identified the best and worst plans correctly. The plans using conformal jaws were neither the best nor the worst of all candidates. CONCLUSIONS: VMAT plans using optimal jaw locations can be created automatically using ESAPI and RapidPlan. Conformal jaws are not the optimal choice.

An interactive plan and model evolution method for knowledge-based pelvic VMAT planning.

PURPOSE: To test if a RapidPlan DVH estimation model and its training plans can be improved interactively through a closed-loop evolution process. METHODS AND MATERIALS: Eighty-one manual plans (P0 ) that were used to configure an initial rectal RapidPlan model (M0 ) were reoptimized using M0 (closed-loop), yielding 81 P1 plans. The 75 improved P1 (P1+ ) and the remaining 6 P0 were used to configure model M1 . The 81 training plans were reoptimized again using M1 , producing 23 P2 plans that were superior to both their P0 and P1 forms (P2+ ). Hence, the knowledge base of model M2 composed of 6 P0 , 52 P1+ , and 23 P2+ . Models were tested dosimetrically on 30 VMAT validation cases (Pv ) that were not used for training, yielding Pv (M0 ), Pv (M1 ), and Pv (M2 ) respectively. The 30 Pv were also optimized by M2_new as trained by the library of M2 and 30 Pv (M0 ). RESULTS: Based on comparable target dose coverage, the first closed-loop reoptimization significantly (P < 0.01) reduced the 81 training plans' mean dose to femoral head, urinary bladder, and small bowel by 2.65 Gy/15.63%, 2.06 Gy/8.11%, and 1.47 Gy/6.31% respectively, which were further reduced significantly (P < 0.01) in the second closed-loop reoptimization by 0.04 Gy/0.28%, 0.18 Gy/0.77%, 0.22 Gy/1.01% respectively. However, open-loop VMAT validations displayed more complex and intertwined plan quality changes: mean dose to urinary bladder and small bowel decreased monotonically using M1 (by 0.34 Gy/1.47%, 0.25 Gy/1.13%) and M2 (by 0.36 Gy/1.56%, 0.30 Gy/1.36%) than using M0 . However, mean dose to femoral head increased by 0.81 Gy/6.64% (M1 ) and 0.91 Gy/7.46% (M2 ) than using M0 . The overfitting problem was relieved by applying model M2_new . CONCLUSIONS: The RapidPlan model and its constituent plans can improve each other interactively through a closed-loop evolution process. Incorporating new patients into the original training library can improve the RapidPlan model and the upcoming plans interactively.

Photon Optimizer (PO) prevails over Progressive Resolution Optimizer (PRO) for VMAT planning with or without knowledge-based solution.

The enhanced dosimetric performance of knowledge-based volumetric modulated arc therapy (VMAT) planning might be jointly contributed by the patient-specific optimization objectives, as estimated by the RapidPlan model, and by the potentially improved Photon Optimizer (PO) algorithm than the previous Progressive Resolution Optimizer (PRO) engine. As PO is mandatory for RapidPlan estimation but optional for conventional manual planning, appreciating the two optimizers may provide practical guidelines for the algorithm selection because knowledge-based planning may not replace the current method completely in a short run. Using a previously validated dose-volume histogram (DVH) estimation model which can produce clinically acceptable plans automatically for rectal cancer patients without interactive manual adjustment, this study reoptimized 30 historically approved plans (referred as clinical plans that were created manually with PRO) with RapidPlan solution (PO plans). Then the PRO algorithm was utilized to optimize the plans again using the same dose-volume constraints as PO plans, where the line objectives were converted as a series of point objectives automatically (PRO plans). On the basis of comparable target dose coverage, the combined applications of new objectives and PO algorithm have significantly reduced the organs-at-risk (OAR) exposure by 23.49-32.72% than the clinical plans. These discrepancies have been largely preserved after substituting PRO for PO, indicating the dosimetric improvements were mostly attributable to the refined objectives. Therefore, Eclipse users of earlier versions may instantly benefit from adopting the model-generated objectives from other RapidPlan-equipped centers, even with PRO algorithm. However, the additional contribution made by the PO relative to PRO accounted for 1.54-3.74%, suggesting PO should be selected with priority whenever available, with or without RapidPlan solution as a purchasable package. Significantly increased monitor units were associated with the model-generated objectives but independent from the optimizers, indicating higher modulation in these plans. As a summary, PO prevails over PRO algorithm for VMAT planning with or without knowledge-based technique.

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.

Investigating the impact of rapidplan on ethos automated planning.

Automated planning has surged in popularity within external beam radiation therapy in recent times. Leveraging insights from previous clinical knowledge could enhance auto-planning quality. In this work, we evaluated the performance of Ethos automated planning with knowledge-based guidance, specifically using Rapidplan (RP). Seventy-four patients with head-and-neck (HN) cancer and 37 patients with prostate cancer were used to construct separate RP models. Additionally, 16 patients from each group (HN and prostate) were selected to assess the performance of Ethos auto-planning results. Initially, a template-based Ethos plan (Non-RP plan) was generated, followed by integrating the corresponding RP model's DVH estimates into the optimization process to generate another plan (RP plan). We compared the target coverage, OAR doses, and total monitor units between the non-RP and RP plans. Both RP and non-RP plans achieved comparable target coverage in HN and Prostate cases, with a negligible difference of less than 0.5% (p > 0.2). RP plans consistently demonstrated lower doses of OARs in both HN and prostate cases. Specifically, the mean doses of OARs were significantly reduced by 9% (p < 0.05). RP plans required slightly higher monitor units in both HN and prostate sites (p < 0.05), however, the plan generation time was almost similar (p > 0.07). The inclusion of the RP model reduced the OAR doses, particularly reducing the mean dose to critical organs compared to non-RP plans while maintaining similar target coverage. Our findings provide valuable insights for clinics adopting Ethos planning, potentially enhancing the auto-planning to operate optimally.

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

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