Alexander R. Delaney

Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer

PURPOSE Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries. METHODS AND MATERIALS Volumetric modulated arc therapy plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles, and oral cavity were evenly divided between 2 models, Model(30A) and Model(30B), and were combined in a third model, Model60. Knowledge-based plans were created for 2 evaluation groups: evaluation group 1 (EG1), consisting of 15 recent patients, and evaluation group 2 (EG2), consisting of 15 older patients in whom only the salivary glands were spared. RapidPlan results were compared with clinical plans (CP) for boost and/or elective planning target volume homogeneity index, using HI(B)/HI(E) = 100 × (D2% - D98%)/D50%, and mean dose to composite salivary glands, swallowing muscles, and oral cavity (D(sal), D(swal), and D(oc), respectively). RESULTS For EG1, RapidPlan improved HI(B) and HI(E) values compared with CP by 1.0% to 1.3% and 1.0% to 0.6%, respectively. Comparable D(sal) and D(swal) values were seen in Model(30A), Model(30B), and Model60, decreasing by an average of 0.1, 1.0, and 0.8 Gy and 4.8, 3.7, and 4.4 Gy, respectively. However, differences were noted between individual organs at risk (OARs), with Model(30B) increasing D(oc) by 0.1, 3.2, and 2.8 Gy compared with CP, Model(30A), and Model60. Plan quality was less consistent when the patient was flagged as an outlier. For EG2, RapidPlan decreased D(sal) by 4.1 to 4.9 Gy on average, whereas HI(B) and HI(E) decreased by 1.1% to 1.5% and 2.3% to 1.9%, respectively. CONCLUSIONS RapidPlan knowledge-based treatment plans were comparable to CP if the patients OAR-planning target volume geometry was within the range of those included in the models. EG2 results showed that a model including swallowing-muscle and oral-cavity sparing can be applied to patients with only salivary gland sparing. This may allow model library sharing between institutes. Optimal detection of inadequate plans and population of model libraries requires further investigation.

Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans?

BackgroundTreatment plan quality assurance (QA) is important for clinical studies and for institutions aiming to generate near-optimal individualized treatment plans. However, determining how good a given plan is for that particular patient (individualized patient/plan QA, in contrast to running through a checklist of generic QA parameters applied to all patients) is difficult, time consuming and operator-dependent. We therefore evaluated the potential of RapidPlan, a commercial knowledge-based planning solution, to automate this process, by predicting achievable OAR doses for individual patients based on a model library consisting of historical plans with a range of organ-at-risk (OAR) to planning target volume (PTV) geometries and dosimetries.MethodsA 90-plan RapidPlan model, generated using previously created automatic interactively optimized (AIO) plans, was used to predict achievable OAR dose-volume histograms (DVHs) for the parotid glands, submandibular glands, individual swallowing muscles and oral cavities of 20 head and neck cancer (HNC) patients using a volumetric modulated (RapidArc) simultaneous integrated boost technique. Predicted mean OAR doses were compared with mean doses achieved when RapidPlan was used to make a new plan. Differences between the achieved and predicted DVH-lines were analyzed. Finally, RapidPlan predictions were used to evaluate achieved OAR sparing of AIO and manual interactively optimized plans.ResultsFor all OARs, strong linear correlations (R2 = 0.94–0.99) were found between predicted and achieved mean doses. RapidPlan generally overestimated the amount of achievable sparing for OARs with a large degree of OAR-PTV overlap. RapidPlan QA using predicted doses alone identified that for 50 % (10/20) of the manually optimized plans, sparing of the composite salivary glands, oral cavity or composite swallowing muscles could be improved by at least 3 Gy, 5 Gy or 7 Gy, respectively, while this was the case for 20 % (4/20) AIO plans. These predicted gains were validated by replanning the identified patients using RapidPlan.ConclusionsStrong correlations between predicted and achieved mean doses indicate that RapidPlan could accurately predict achievable mean doses. This shows the feasibility of using RapidPlan DVH prediction alone for automated individualized head and neck plan QA. This has applications in individual centers and clinical trials.

Effect of Dosimetric Outliers on the Performance of a Commercial Knowledge-Based Planning Solution

PURPOSE RapidPlan, a commercial knowledge-based planning solution, uses a model library containing the geometry and associated dosimetry of existing plans. This model predicts achievable dosimetry for prospective patients that can be used to guide plan optimization. However, it is unknown how suboptimal model plans (outliers) influence the predictions or resulting plans. We investigated the effect of, first, removing outliers from the model (cleaning it) and subsequently adding deliberate dosimetric outliers. METHODS AND MATERIALS Clinical plans from 70 head and neck cancer patients comprised the uncleaned (UC) ModelUC, from which outliers were cleaned (C) to create ModelC. The last 5 to 40 patients of ModelC were replanned with no attempt to spare the salivary glands. These substantial dosimetric outliers were reintroduced to the model in increments of 5, creating Model5 to Model40 (Model5-40). These models were used to create plans for a 10-patient evaluation group. Plans from ModelUC and ModelC, and ModelC and Model5-40 were compared on the basis of boost (B) and elective (E) target volume homogeneity indexes (HIB/HIE) and mean doses to oral cavity, composite salivary glands (compsal) and swallowing (compswal) structures. RESULTS On average, outlier removal (ModelC vs ModelUC) had minimal effects on HIB/HIE (0%-0.4%) and sparing of organs at risk (mean dose difference to oral cavity and compsal/compswal were ≤0.4 Gy). Model5-10 marginally improved compsal sparing, whereas adding a larger number of outliers (Model20-40) led to deteriorations in compsal up to 3.9 Gy, on average. These increases are modest compared to the 14.9 Gy dose increases in the added outlier plans, due to the placement of optimization objectives below the inferior boundary of the dose-volume histogram-predicted range. CONCLUSIONS Overall, dosimetric outlier removal from or addition of 5 to 10 outliers to a 70-patient model had marginal effects on resulting plan quality. Although the addition of >20 outliers deteriorated plan quality, the effect was modest. In this study, RapidPlan demonstrated robustness for moderate proportions of salivary gland dosimetric outliers.

Using a knowledge-based planning solution to select patients for proton therapy

BACKGROUND AND PURPOSE Patient selection for proton therapy by comparing proton/photon treatment plans is time-consuming and prone to bias. RapidPlan™, a knowledge-based-planning solution, uses plan-libraries to model and predict organ-at-risk (OAR) dose-volume-histograms (DVHs). We investigated whether RapidPlan, utilizing an algorithm based only on photon beam characteristics, could generate proton DVH-predictions and whether these could correctly identify patients for proton therapy. MATERIAL AND METHODS ModelPROT and ModelPHOT comprised 30 head-and-neck cancer proton and photon plans, respectively. Proton and photon knowledge-based-plans (KBPs) were made for ten evaluation-patients. DVH-prediction accuracy was analyzed by comparing predicted-vs-achieved mean OAR doses. KBPs and manual plans were compared using salivary gland and swallowing muscle mean doses. For illustration, patients were selected for protons if predicted ModelPHOT mean dose minus predicted ModelPROT mean dose (ΔPrediction) for combined OARs was ≥6Gy, and benchmarked using achieved KBP doses. RESULTS Achieved and predicted ModelPROT/ModelPHOT mean dose R2 was 0.95/0.98. Generally, achieved mean dose for ModelPHOT/ModelPROT KBPs was respectively lower/higher than predicted. Comparing ModelPROT/ModelPHOT KBPs with manual plans, salivary and swallowing mean doses increased/decreased by <2Gy, on average. ΔPrediction≥6Gy correctly selected 4 of 5 patients for protons. CONCLUSIONS Knowledge-based DVH-predictions can provide efficient, patient-specific selection for protons. A proton-specific RapidPlan-solution could improve results.

Knowledge-based planning for stereotactic radiotherapy of peripheral early-stage lung cancer

Peripheral early-stage non-small cell lung cancer in medically inoperable patients is a guideline-recommended indication for stereotactic body radiotherapy (SBRT) [1]. However, peripheral lesions present varying tumor geometries and overlap with organs at risk (OARs) such as the thoracic wall (TW); treatment planning is prone to variation, leading to inconsistencies in treatment plan (TP) quality [2,3]; and implementing a lung SBRT program is resource intensive [4]. Automated solutions have been devised to help address these problems, including knowledge-based planning (KBP) [5–10]. One commercial KBP solution utilizes a model based on previous TPs to generate dose-volume histogram (DVH) prediction ranges which position optimization objectives for the OARs of prospective patients. Pre-clinical evaluation has yielded clinically acceptable results for a number of disease sites [11–13]. However, detailed investigations for lung SBRT are lacking. We therefore investigated the performance of this KBP solution for 3 and 5 fraction lung SBRT using volumetric modulated arc therapy (VMAT); whether TPs from these fractionation schemes could be combined into a single model; and how the models performed when the planning target volume (PTV) overlapped with OARs including the TW.

Is accurate contouring of salivary and swallowing structures necessary to spare them in head and neck VMAT plans

BACKGROUND AND PURPOSE Current standards for organ-at-risk (OAR) contouring encourage anatomical accuracy which can be resource intensive. Certain OARs may be suitable for alternative delineation strategies. We investigated whether simplified salivary and swallowing structure contouring can still lead to good OAR sparing in automated head and neck cancer (HNC) plans. MATERIALS AND METHODS For 15 HNC patients, knowledge-based plans (KBPs) using RapidPlan™ were created using: (1) standard clinical contours for all OARs (benchmark-plans), (2) automated knowledge-based contours for the salivary glands, with standard contours for the remaining OARs (SS-plans) and (3) simplified contours (SC-plans) consisting of quick-to-draw tubular structures to account for the oral cavity, salivary glands and swallowing muscles. Individual clinical OAR contours in a RapidPlan™ model were combined to create composite salivary/swallowing structures. These were matched to tube-contours to create SC-plans. All plans were compared based on dose to anatomically accurate clinical OAR contours. RESULTS Salivary gland delineation in SS-plans required on average 2 min, compared with 7 min for manual delineation of all tubular-contours. Automated atlas-based contours overlapped with, on average, 71% of clinical salivary gland contours while tube-contours overlapped with 95%/75%/93% of salivary gland/oral cavity/swallowing structure contours. On average, SC-plans were comparable to benchmark-plans and SS-plans, with average differences in composite salivary and swallowing structure dose ≤2 Gy and <1 Gy respectively. CONCLUSIONS Simplified-contours could be created quickly and resulted in clinically acceptable HNC VMAT plans. They can be combined with automated planning to facilitate the implementation of advanced radiotherapy, even when resources are limited.

PO-0881: Evaluation of a knowledge-based planning solution for head and neck cancer

Purpose/Objective: Knowledge-based planning (KBP) aims to automate plan optimization, increase efficiency and reduce inter and intra-planner variation. RapidPlanTM (Varian Medical Systems) uses a library of patient plans to create a model that predicts a range of achievable dose-volume histograms (DVHs) for new patients and uses these for setting optimization objectives. We benchmarked RapidPlan KBP versus clinical plans for two patient groups, using three different libraries. Materials and Methods: Volumetric modulated arc therapy (VMAT) plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles and oral cavity, were evenly divided between two libraries, 30A and 30B. Three models were created, Model30A, Model30B and Model60, the latter by combining libraries 30A and 30B. Knowledge-based RapidArc plans were created for two evaluation groups; EG1, consisting of 15 recent patients where salivary glands, swallowing structures and oral cavity were spared, and EG2, consisting of 15 patients from 20082009, shortly after starting our VMAT program, in which only the salivary glands were spared. KBP results were compared against clinical plans (CP) on the basis of the boost/elective planning target volume homogeneity index (HIB/HIE=100*[D2%D98%]/D50%) and mean dose to composite salivary glands, swallowing muscles and oral cavity (Dsal, Dswal and Doc, respectively). Results: For EG1, KBP improved HIB/HIE values over CP by 1.0-1.3%/1.0-0.6%. Comparable Dsal and Dswal values were seen in Model30A/Model30B/Model60, an average 0.1/1.0/0.8Gy and 4.8/3.7/4.4Gy less than CP Dsal and Dswal, respectively. However, differences were noted between individual OARs, with Model30B increasing DOC by 0.1/3.2/2.8Gy over CP/Model30A/Model60. Plan quality was less consistent when the patient was flagged as an ‘outlier’, compared to the range of OAR/PTV metrics in the plan library. For EG2, KBP decreased Dsal by 4.1-4.9Gy on average, while HIB/HIE decreased by 1.1-1.5%/2.3-1.9%. Generating DVH predictions and optimizing a KB plan took <36 s and <15 minutes, respectively. Conclusions: RapidPlan knowledge-based treatment plans were comparable to CP if the patient’s OAR/PTV geometry was within the range of those included in the models. EG2 results showed that KBP libraries comprised of plans made by an experienced team can substantially improve OAR sparing compared to CP made at the start of a VMAT program. The present data support the KBP concept, model library sharing between institutes using similar clinical plan protocols and the use of KBP to shortcut the learning curve for complex VMAT planning.