Jim P. Tol

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.

Toward optimal organ at risk sparing in complex volumetric modulated arc therapy: An exponential trade‐off with target volume dose homogeneity

PURPOSE Conventional radiotherapy typically aims for homogenous dose in the planning target volume (PTV) while sparing organs at risk (OAR). The authors quantified and characterized the trade-off between PTV dose inhomogeneity (IH) and OAR sparing in complex head and neck volumetric modulated arc therapy plans. METHODS Thirteen simultaneous integrated boost plans were created per patient, for ten patients. PTV boost(B)/elective(E) optimization priorities were systematically increased. IHB and IHE, defined as (100% - V95%) + V107%, were evaluated against the average of the mean dose to the combined composite swallowing and combined salivary organs (D-OAR(comp)). To investigate the influence of OAR size and position with respect to PTVB/E, OAR dose was evaluated against a modified Euclidean distance (DMB/DME) between OAR and PTV. RESULTS Although the achievable D-OAR(comp) for a given level of PTV IH differed between patients, excellent logarithmic fits described the D-OAR(comp)/IHB and IHE relationship in all patients (mean R(2) of 0.98 and 0.97, respectively). Allowing an increase in average IHB and IHE over a clinically acceptable range, e.g., from 0.4% ± 0.5% to 2.0% ± 2.0% and 6.9% ± 2.8% to 14.8% ± 2.7%, respectively, corresponded to a decrease in average dose to the composite salivary and swallowing structures from 30.3 ± 6.5 to 23.6 ± 4.7 Gy and 32.5 ± 8.3 to 26.8 ± 9.3 Gy. The increase in PTVE IH was mainly accounted for by an increase in V107, by on average 5.9%, rather than a reduction in V95, which was on average only 2%. A linear correlation was found between the OAR dose to composite swallowing structures and contralateral parotid and submandibular gland, with DME (R(2) = 0.83, 0.88, 0.95). Only mean ipsilateral parotid dose correlated with DMB (R(2) = 0.87). CONCLUSIONS OAR sparing is highly dependent on the permitted PTVB/E IH. PTVE IH substantially influences OAR doses. These results are relevant for clinical practice and for future automated treatment-planning strategies.

Automatic interactive optimization for volumetric modulated arc therapy planning.

BackgroundIntensity modulated radiotherapy treatment planning for sites with many different organs-at-risk (OAR) is complex and labor-intensive, making it hard to obtain consistent plan quality. With the aim of addressing this, we developed a program (automatic interactive optimizer, AIO) designed to automate the manual interactive process for the Eclipse treatment planning system. We describe AIO and present initial evaluation data.MethodsOur current institutional volumetric modulated arc therapy (RapidArc) planning approach for head and neck tumors places 3-4 adjustable OAR optimization objectives along the dose-volume histogram (DVH) curve that is displayed in the optimization window. AIO scans this window and uses color-coding to differentiate between the DVH-lines, allowing it to automatically adjust the location of the optimization objectives frequently and in a more consistent fashion. We compared RapidArc AIO plans (using 9 optimization objectives per OAR) with the clinical plans of 10 patients, and evaluated optimal AIO settings. AIO consistency was tested by replanning a single patient 5 times.ResultsAverage V95&V107 of the boost planning target volume (PTV) and V95 of the elective PTV differed by ≤0.5%, while average elective PTV V107 improved by 1.5%. Averaged over all patients, AIO reduced mean doses to individual salivary structures by 0.9-1.6Gy and provided mean dose reductions of 5.6Gy and 3.9Gy to the composite swallowing structures and oral cavity, respectively. Re-running AIO five times, resulted in the aforementioned parameters differing by less than 3%.ConclusionsUsing the same planning strategy as manually optimized head and neck plans, AIO can automate the interactive Eclipse treatment planning process and deliver dosimetric improvements over existing clinical plans.

Comparison of organ-at-risk sparing and plan robustness for spot-scanning proton therapy and volumetric modulated arc photon therapy in head-and-neck cancer.

PURPOSE Proton radiotherapy for head-and-neck cancer (HNC) aims to improve organ-at-risk (OAR) sparing over photon radiotherapy. However, it may be less robust for setup and range uncertainties. The authors investigated OAR sparing and plan robustness for spot-scanning proton planning techniques and compared these with volumetric modulated arc therapy (VMAT) photon plans. METHODS Ten HNC patients were replanned using two arc VMAT (RapidArc) and spot-scanning proton techniques. OARs to be spared included the contra- and ipsilateral parotid and submandibular glands and individual swallowing muscles. Proton plans were made using Multifield Optimization (MFO, using three, five, and seven fields) and Single-field Optimization (SFO, using three fields). OAR sparing was evaluated using mean dose to composite salivary glands (CompSal) and composite swallowing muscles (CompSwal). Plan robustness was determined for setup and range uncertainties (±3 mm for setup, ±3% HU) evaluating V95% and V107% for clinical target volumes. RESULTS Averaged over all patients CompSal/CompSwal mean doses were lower for the three-field MFO plans (14.6/16.4 Gy) compared to the three-field SFO plans (20.0/23.7 Gy) and VMAT plans (23.0/25.3 Gy). Using more than three fields resulted in differences in OAR sparing of less than 1.5 Gy between plans. SFO plans were significantly more robust than MFO plans. VMAT plans were the most robust. CONCLUSIONS MFO plans had improved OAR sparing but were less robust than SFO and VMAT plans, while SFO plans were more robust than MFO plans but resulted in less OAR sparing. Robustness of the MFO plans did not increase with more fields.

Different treatment planning protocols can lead to large differences in organ at risk sparing

BACKGROUND AND PURPOSE Different planning protocols may define varying planning target volume (PTV) dose criteria. We investigated the hypothesis that this could result in differences in organ-at-risk (OAR) sparing. MATERIAL AND METHODS Volumetric modulated arc therapy plans were created for ten locally advanced head and neck cancer patients following PTV criteria specified by the RTOG, EORTC and institutional (VUmc) protocols. Resulting plans were evaluated on the basis of the homogeneity index, calculated for the boost/elective PTVs as HIB/HIE=100%*(D2%-D98%)/D50% and mean dose to individual and composite salivary (compsal) and swallowing (compswal) OARs. RESULTS RTOG plans were the most homogeneous, with mean HIB of 8.2±0.9%, compared to 9.5±1.0%/11.6±1.5% for the VUmc/EORTC plans. EORTC plans provided most OAR sparing, with compsal/compswal doses of 24.6±7.7/22.9±4.2Gy, compared to 32.2±9.7/29.9±4.2Gy and 28.4±8.1/24.7±5.3Gy for RTOG and VUmc, respectively. EORTC provided 7.2/7.7Gy mean dose reductions to the contra/ipsilateral parotid glands compared to RTOG. CONCLUSIONS Different planning protocols resulted in different levels of PTV dose homogeneity. We observed differences of up to ⩾7Gy in composite and individual mean OAR doses. This could influence rates of toxicity and should be taken into account when comparing clinical studies. A consensus should be reached between major trial groups on appropriate PTV parameters.

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.

Increasing the number of arcs improves head and neck volumetric modulated arc therapy plans

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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.