Kevin Lawrence Moore

Purpose: Single‐isocenter, volumetric‐modulated arc therapy (VMAT) stereotactic radiosurgery (SRS) for multiple brain metastases (multimets) can deliver highly conformal dose distributions and reduce overall patient treatment time compared to other techniques. However, treatment planning for multimet cases is highly complex due to variability in numbers and sizes of brain metastases, as well as their relative proximity to organs‐at‐risk (OARs). The purpose of this study was to automate the VMAT planning of multimet cases through a knowledge‐based planning (KBP) approach that adapts single‐target SRS dose predictions to multiple target predictions.Methods: Using a previously published artificial neural network (ANN) KBP system trained on single‐target, linac‐based SRS plans, 3D dose distribution predictions for multimet patients were obtained by treating each brain lesion as a solitary target and subsequently combining individual dose predictions into a single distribution. Spatial dose distributions for each of the i = 1…N lesions were merged using the combination function . The optimal value of n was determined by minimizing root‐mean squared (RMS) difference between clinical multimet plans and predicted dose per unit length along the line profile joining each lesion in the clinical cohort. The gradient measure is the primary quality metric for SRS plan evaluation at our institution and served as the main comparative metric between clinical plans and the KBP results. A total of 41 previously treated multimet plans, with target numbers ranging from N = 2–10, were used to validate the ANN predictions and subsequent KBP auto‐planning routine. Fully deliverable KBP plans were developed by converting predicted dose distribution into patient‐specific optimization objectives for the clinical treatment planning system (TPS). Plan parity was maintained through identical arc configuration and target normalization. Overall plan quality improvements were quantified by calculating the difference between SRS quality metrics (QMs): ΔQM = QM_clinical − QM_KBP. In addition to GM, investigated QMs were: volume of brain receiving ≥ 10 Gy (V_10 Gy), volume of brain receiving ≥ 5 Gy (ΔV_5 Gy), heterogeneity index (HI), dose to 0.1 cc of the brainstem (D_0.1 cc), dose to 1% of the optic chiasm (D_1%), and interlesion dose (D_IL). In addition to this quantitative analysis, overall plan quality was assessed via blinded plan comparison of the manual and KBP treatment plans by SRS‐specializing physicians.Results: A dose combination factor of n = 8 yielded an integrated dose profile RMS difference of 2.9% across the 41‐patient cohort. Multimet dose predictions exhibited ΔGM = 0.07 ± 0.10 cm against the clinical sample, implying either further normal tissue sparing was possible or that dose predictions were slightly overestimating achievable dose gradients. The latter is the more likely explanation, as this bias vanished when dose predictions were converted to deliverable KBP plans ΔGM = 0.00 ± 0.08 cm. Remaining QMs were nearly identical or showed modest improvements in the KBP sample. Equivalent QMs included: ΔV_10 Gy = 0.37 ± 3.78 cc, ΔHI = 0.02 ± 0.08 and ΔD_IL = −2.22 ± 171.4 cGy. The KBP plans showed a greater degree of normal tissue sparing as indicated by brain ΔV_5 Gy = 4.11± 24.05 cc, brainstem ΔD_0.1 cc = 42.8 ± 121.4 cGy, and chiasm ΔD_1% = 50.8 ± 83.0 cGy. In blinded review by SRS‐specializing physicians, KBP‐generated plans were deemed equivalent or superior in 32/41(78.1%) of the cases.Conclusion: Heuristic KBP‐driven automated planning in linac‐based, single‐isocenter treatments for multiple brain metastases maintained or exceeded overall plan quality.

Purpose: To demonstrate knowledge-based 3Ddose prediction for external beam radiotherapy. Methods: Using previously treated plans as training data, an artificial neural network (ANN) was trained to predict a dose matrix based on patient-specific geometric and planning parameters, such as the closest distance (r) to planning target volume (PTV) and organ-at-risks (OARs). Twenty-three prostate and 43 stereotactic radiosurgery/radiotherapy (SRS/SRT) cases with at least one nearby OAR were studied. All were planned with volumetric-modulated arc therapy to prescription doses of 81 Gy for prostate and 12–30 Gy for SRS. Using these clinically approved plans, ANNs were trained to predict dose matrix and the predictive accuracy was evaluated using the dose difference between the clinical plan and prediction, δD = Dclin − Dpred. The mean (〈δDr〉), standard deviation (σδDr), and their interquartile range (IQR) for the training plans were evaluated at a 2–3 mm interval from the PTV boundary (rPTV) to assess prediction bias and precision. Initially, unfiltered models which were trained using all plans in the cohorts were created for each treatment site. The models predict approximately the average quality of OAR sparing. Emphasizing a subset of plans that exhibited superior to the average OAR sparing during training, refined models were created to predict high-quality rectum sparing for prostate and brainstem sparing for SRS. Using the refined model, potentially suboptimal plans were identified where the model predicted further sparing of the OARs was achievable. Replans were performed to test if the OAR sparing could be improved as predicted by the model. Results: The refined models demonstrated highly accurate dose distribution prediction. For prostate cases, the average prediction bias for all voxels irrespective of organ delineation ranged from −1% to 0% with maximum IQR of 3% over rPTV ∈ [ − 6, 30] mm. The average prediction error was less than 10% for the same rPTV range. For SRS cases, the average prediction bias ranged from −0.7% to 1.5% with maximum IQR of 5% over rPTV ∈ [ − 4, 32] mm. The average prediction error was less than 8%. Four potentially suboptimal plans were identified for each site and subsequent replanning demonstrated improved sparing of rectum and brainstem. Conclusions: The study demonstrates highly accurate knowledge-based 3Ddose predictions for radiotherapy plans.

Purpose: The objective of this work was to develop a comprehensive knowledge‐based methodology for predicting achievable dose–volume histograms (DVHs) and highly precise DVH‐based quality metrics (QMs) in stereotactic radiosurgery/radiotherapy (SRS/SRT) plans. Accurate QM estimation can identify suboptimal treatment plans and provide target optimization objectives to standardize and improve treatment planning.Methods: Correlating observed dose as it relates to the geometric relationship of organs‐at‐risk (OARs) to planning target volumes (PTVs) yields mathematical models to predict achievable DVHs. In SRS, DVH‐based QMs such as brain V_10Gy (volume receiving 10 Gy or more), gradient measure (GM), and conformity index (CI) are used to evaluate plan quality. This study encompasses 223 linear accelerator‐based SRS/SRT treatment plans (SRS plans) using volumetric‐modulated arc therapy (VMAT), representing 95% of the institution's VMAT radiosurgery load from the past four and a half years. Unfiltered models that use all available plans for the model training were built for each category with a stratification scheme based on target and OAR characteristics determined emergently through initial modeling process. Model predictive accuracy is measured by the mean and standard deviation of the difference between clinical and predicted QMs, δQM = QM_clin − QM_pred, and a coefficient of determination, R². For categories with a large number of plans, refined models are constructed by automatic elimination of suspected suboptimal plans from the training set. Using the refined model as a presumed achievable standard, potentially suboptimal plans are identified. Predictions of QM improvement are validated via standardized replanning of 20 suspected suboptimal plans based on dosimetric predictions. The significance of the QM improvement is evaluated using the Wilcoxon signed rank test.Results: The most accurate predictions are obtained when plans are stratified based on proximity to OARs and their PTV volume sizes. Volumes are categorized into small (V_PTV < 2 cm³), medium (2 cm³ < V_PTV < 25 cm³), and large (25 cm³ < V_PTV). The unfiltered models demonstrate the ability to predict GMs to ∼1 mm and fractional brain V_10Gy to ∼25% for plans with large V_PTV and critical OAR involvements. Increased accuracy and precision of QM predictions are obtained when high quality plans are selected for the model training. For the small and medium V_PTV plans without critical OAR involvement, predictive ability was evaluated using the refined model. For training plans, the model predicted GM to an accuracy of 0.2 ± 0.3 mm and fractional brain V_10Gy to 0.04 ± 0.12, suggesting highly accurate predictive ability. For excluded plans, the average δGM was 1.1 mm and fractional brain V_10Gy was 0.20. These δQM are significantly greater than those of the model training plans (p < 0.001). For CI, predictions are close to clinical values and no significant difference was observed between the training and excluded plans (p = 0.19). Twenty outliers with δGM > 1.35 mm were identified as potentially suboptimal, and replanning these cases using predicted target objectives demonstrates significant improvements on QMs: on average, 1.1 mm reduction in GM (p < 0.001) and 23% reduction in brain V_10Gy (p < 0.001). After replanning, the difference of δGM distribution between the 20 replans and the refined model training plans was marginal.Conclusions: The results demonstrate the ability to predict SRS QMs precisely and to identify suboptimal plans. Furthermore, the knowledge‐based DVH predictions were directly used as target optimization objectives and allowed a standardized planning process that bettered the clinically approved plans. Full clinical application of this methodology can improve consistency of SRS plan quality in a wide range of PTV volume and proximity to OARs and facilitate automated treatment planning for this critical treatment site.