No abstract

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.

Purpose/Objectives Stereotactic body radiotherapy (SBRT) is increasingly used to treat oligometastatic or unresectable primary malignancy, although proximity of organs-at-risk (OAR) may limit delivery of sufficiently ablative dose. Magnetic resonance (MR)-based online-adaptive radiotherapy (ART) has potential to improve SBRT’s therapeutic ratio. This study characterizes potential advantages of online-adaptive MR-guided SBRT to treat oligometastatic disease of the non-liver abdomen and central thorax. Materials/Methods Ten patients treated with RT for unresectable primary or oligometastatic disease of the non-liver abdomen (n=5) or central thorax (n=5) underwent imaging throughout treatment on a clinical MR-IGRT system. SBRT plans were created based on tumor/OAR anatomy at initial CT simulation (PI) and simulated adaptive plans were created based on observed MR-image set tumor/OAR “anatomy-of-the-day” (PA). Each PA was planned under workflow constraints to simulate online-ART. Prescribed dose was 50Gy/5fractions with goal coverage of 95% PTV by 95% of the prescription, subject to hard OAR constraints. PI was applied to each MR dataset and compared to PA to evaluate changes in dose delivered to tumor/OARs, with dose escalation when possible. Results Hard OAR constraints were met for all PI based on anatomy from initial CT simulation, and all PA based on anatomy from each daily MR-image set. Application of the PI to anatomy-of-the-day caused OAR constraint violation in 19/30 cases. Adaptive planning increased PTV coverage in 21/30 cases, including 14 cases where hard OAR constraints were violated by the non-adaptive plan. For 9 PA cases, decreased PTV coverage was required to meet hard OAR constraints that would have been violated in a non-adaptive setting. Conclusions Online-adaptive MRI-guided SBRT may allow PTV dose escalation and/or simultaneous OAR sparing compared to non-adaptive SBRT. A prospective clinical trial is underway at our institution to evaluate clinical outcomes of this technique.

Purpose The purpose of this study was to quantify the frequency and clinical severity of quality deficiencies in intensity-modulated radiotherapy (IMRT) planning on the RTOG0126 protocol. Methods and Materials 219 IMRT patients from the high-dose arm (79.2Gy) of RTOG0126 were analyzed. To quantify plan quality, we used established knowledge-based methods for patient-specific 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 against observed toxicities. The 90th-percentile (22/219) 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 sub-optimal planning. Accuracy of pDVH predictions was validated by re-planning 30/219 (13.7%) patients, 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 increased rectal sparing was not correlated with target metrics (D98%,D2%). Observed toxicities were consistent with current LKB parameters. Converting DVH and pDVH0126,top10% to rectal NTCP, we observed 94/219 (42.9%) with ≥5% excess risk, 20/219 (9.1%) with ≥10% excess risk, and 2/219 (0.9%) with ≥15% excess risk. Re-planning demonstrated the predicted NTCP reductions while maintaining target V100%. An equivalent sample of high-quality plans showed fewer toxicities than low-quality plans, 6/73 vs. 10/73 respectively, though these differences were not significant (p=0.21) due to insufficient statistical power in this retrospective study. Conclusions Plan quality deficiencies in RTOG0126 exposed patients to substantial excess risk for rectal complications.

Purpose Stereotactic body radiation therapy (SBRT) for pancreatic cancer requires a skillful approach to deliver ablative doses to the tumor while limiting dose to the highly sensitive duodenum, stomach, and small bowel. Here, we develop knowledge-based artificial neural network dose models (ANN-DMs) to predict dose distributions that would be approved by experienced physicians. Methods Arc-based SBRT treatment plans for 43 pancreatic cancer patients were planned, delivering 30–33 Gy in five fractions. Treatments were overseen by one of two physicians with individual treatment approaches, with variations in prescribed dose, target volume delineation, and primary organs-at-risk. Using dose distributions calculated by a commercial treatment planning system (TPS), physician-approved treatment plans were used to train ANN-DMs that could predict physician-approved dose distributions based on a set of geometric parameters (vary from voxel to voxel) and plan parameters (constant across all voxels for a given patient). Patient datasets were randomly allocated, with 2/3rds used for training, and 1/3rd used for validation. Differences between TPS and ANN-DM dose distributions were used to evaluate model performance. ANN-DM design, including neural network structure and parameter choices, were evaluated to optimize dose model performance. Results Remarkable improvements in ANN-DM accuracy (i.e., from >30% to <5% mean absolute dose error, relative to the prescribed dose) were achieved by training separate dose models for the treatment style of each physician. Increased neural network complexity (i.e., more layers, more neurons per layer) did not improve dose model accuracy. Mean dose errors were less than 5% at all distances from the PTV, and mean absolute dose errors were on the order of 5%, but no more than 10%. Dose-volume histogram errors (in cm3) demonstrated good model performance above 25 Gy, but much larger errors were seen at lower doses. Conclusions ANN-DM dose distributions showed excellent overall agreement with TPS dose distributions, and accuracy was substantially improved when each physician’s treatment approach was taken into account by training their own dedicated models. In this manner, one could feasibly train ANN-DMs that could predict the dose distribution desired by a given physician for a given treatment site.