Multi-Institutional Validation of a Knowledge-Based Planning Model for Patients Enrolled in RTOG 0617: Implications for Plan Quality Controls in Cooperative Group Trials.

PURPOSE: This study aimed to evaluate the feasibility of using a single-institution, knowledge-based planning (KBP) model as a dosimetric plan quality control (QC) for multi-institutional clinical trials. The efficacy of this QC tool was retrospectively evaluated using a subset of plans submitted to Radiation Therapy Oncology Group (RTOG) study 0617. METHODS AND MATERIALS: A single KBP model was created using commercially available software (RapidPlan; Varian Medical Systems, Palo Alto, CA) and data from 106 patients with non-small cell lung cancer who were treated at a single institution. All plans had prescriptions that ranged from 60 Gy in 30 fractions to 74 Gy in 37 fractions and followed the planning guidelines from RTOG 0617. Two sets of optimization objectives were created to produce different trade-offs using the single KBP model predictions: one prioritizing target coverage and a second prioritizing lung sparing (LS) while allowing an acceptable variation in target coverage. Three institutions submitted a high volume of clinical plans to RTOG 0617 and provided data on 25 patients, which were replanned using both sets of optimization objectives. Model-generated, dose-volume histogram predictions were used to identify patients who exceeded the lung clinical target volume (CTV) V20Gy >37% and would benefit from the LS objectives. Overall plan quality differences between KBP-generated plans and clinical plans were evaluated at RTOG 0617-defined dosimetric endpoints. RESULTS: Target coverage and organ at risk sparing was significantly improved for most KBP-generated plans compared with those from clinical trial data. The KBP model using prioritized target coverage objectives reduced heart Dmean and V40Gy by 2.1 Gy and 5.2%, respectively. Similarly, using LS objectives reduced the lung CTV Dmean and V20Gy by 2.0 Gy and 2.9%, respectively. The KBP predictions correctly identified all patients with lung CTV V20Gy > 37% (5 of 25 patients) and significantly reduced the dose to the lung CTV by applying the LS optimization objectives. CONCLUSIONS: A single-institution KBP model can be applied as a QC tool for multi-institutional clinical trials to improve overall plan quality and provide decision-support to determine the need for anatomy-based dosimetric trade-offs.

Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery.

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 V10Gy (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 = QMclin - QMpred, and a coefficient of determination, R(2). 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 (VPTV < 2 cm(3)), medium (2 cm(3) < VPTV < 25 cm(3)), and large (25 cm(3) < VPTV). The unfiltered models demonstrate the ability to predict GMs to ∼1 mm and fractional brain V10Gy to ∼25% for plans with large VPTV 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 VPTV 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 V10Gy to 0.04 ± 0.12, suggesting highly accurate predictive ability. For excluded plans, the average δGM was 1.1 mm and fractional brain V10Gy 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 V10Gy (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.

Contouring variations and the role of atlas in non-small cell lung cancer radiation therapy: Analysis of a multi-institutional preclinical trial planning study.

PURPOSE: To quantify variations in target and normal structure contouring and evaluate dosimetric impact of these variations in non-small cell lung cancer (NSCLC) cases. To study whether providing an atlas can reduce potential variation. METHODS AND MATERIALS: Three NSCLC cases were distributed sequentially to multiple institutions for contouring and radiation therapy planning. No segmentation atlas was provided for the first 2 cases (Case 1 and Case 2). Contours were collected from submitted plans and consensus contour sets were generated. The volume variation among institution contours and the deviation of them from consensus contours were analyzed. The dose-volume histograms for individual institution plans were recalculated using consensus contours to quantify the dosimetric changes. An atlas containing targets and critical structures was constructed and was made available when the third case (Case 3) was distributed for planning. The contouring variability in the submitted plans of Case 3 was compared with that in first 2 cases. RESULTS: Planning target volume (PTV) showed large variation among institutions. The PTV coverage in institutions' plans decreased dramatically when reevaluated using the consensus PTV contour. The PTV contouring consistency did not show improvement with atlas use in Case 3. For normal structures, lung contours presented very good agreement, while the brachial plexus showed the largest variation. The consistency of esophagus and heart contouring improved significantly (t test; P < .05) in Case 3. Major factors contributing to the contouring variation were identified through a survey questionnaire. CONCLUSIONS: The amount of contouring variations in NSCLC cases was presented. Its impact on dosimetric parameters can be significant. The segmentation atlas improved the contour agreement for esophagus and heart, but not for the PTV in this study. Quality assurance of contouring is essential for a successful multi-institutional clinical trial.

Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126.

PURPOSE: The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity modulated radiation therapy (IMRT) planning in the Radiation Therapy Oncology Group 0126 protocol. METHODS AND MATERIALS: A total of 219 IMRT patients from the high-dose arm (79.2 Gy) of RTOG 0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific dose-volume histogram (DVH) prediction of organs at risk and a Lyman-Kutcher-Burman (LKB) model for grade ≥2 rectal complications to convert DVHs into normal tissue complication probabilities (NTCPs). The LKB model was validated by fitting dose-response parameters relative to observed toxicities. The 90th percentile (22 of 219) of plans with the lowest excess risk (difference between clinical and model-predicted NTCP) were used to create a model for the presumed best practices in the protocol (pDVH0126,top10%). Applying the resultant model to the entire sample enabled comparisons between DVHs that patients could have received to DVHs they actually received. Excess risk quantified the clinical impact of suboptimal planning. Accuracy of pDVH predictions was validated by replanning 30 of 219 patients (13.7%), including equal numbers of presumed "high-quality," "low-quality," and randomly sampled plans. NTCP-predicted toxicities were compared to adverse events on protocol. RESULTS: Existing models showed that bladder-sparing variations were less prevalent than rectum quality variations and that increased rectal sparing was not correlated with target metrics (dose received by 98% and 2% of the PTV, respectively). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCPs, we observed 94 of 219 patients (42.9%) with ≥5% excess risk, 20 of 219 patients (9.1%) with ≥10% excess risk, and 2 of 219 patients (0.9%) with ≥15% excess risk. Replanning demonstrated the predicted NTCP reductions while maintaining the volume of the PTV receiving prescription dose. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6 of 73 versus 10 of 73 respectively, although these differences were not significant (P=.21) due to insufficient statistical power in this retrospective study. CONCLUSIONS: Plan quality deficiencies in RTOG 0126 exposed patients to substantial excess risk for rectal complications.

Automated radiation therapy treatment plan workflow using a commercial application programming interface.

PURPOSE: The objective of this study was to create a workflow for the automation and standardization of treatment plan generation and evaluation using an application programming interface (API) to access data from a commercial treatment planning system (Varian Medical Systems, Inc, Palo Alto, CA). METHODS AND MATERIALS: The automation workflow begins with converting electronic patient-specific physician treatment planning orders that specify demographics, simulation instructions, and dosimetric objectives for targets and organs at risk into XML files. These XML files are used to generate standard contour names, beam, and patient-specific intensity modulated radiation therapy (IMRT) optimization templates to be executed in a commercial treatment planning system (TPS) by the user. A set of computer programs have been developed to provide quality control (QC) reports that verify demographic information in the TPS against the treatment planning orders, ensure the existence and proper naming of organs at risk, and generate patient-specific plan evaluation reports that provide real-time feedback on the concordance of an active treatment plan to the physician-specified treatment planning goals. RESULTS: A workflow for lung IMRT was chosen as a test scenario. Contour, beam, and patient-specific IMRT optimization templates were automatically generated from the physician treatment planning orders and loaded into the planning system. The QC reports were developed for lung IMRT, including the option of patient-specific modifications to the standard templates. The API QC reporting includes a dynamic program that runs in parallel to the TPS during the planning process, providing real-time feedback as to whether physician-specified treatment plan parameters have improved or worsened from previous iterations. CONCLUSIONS: User-created computer programs to access information in the TPS database by means of a commercial TPS API enable automation and standardization of treatment plan generation and evaluation.