Stereotactic body radiation therapy (SBRT) following Yttrium-90 (90Y) selective internal radiation therapy (SIRT): a feasibility planning study using90Y delivered dose.

Objective.90Y selective internal radiation therapy (SIRT) treatment of hepatocellular carcinoma (HCC) can potentially underdose lesions, as identified on post-therapy PET/CT imaging. This study introduces a methodology and explores the feasibility for selectively treating SIRT-underdosed HCC lesions, or lesion subvolumes, with stereotactic body radiation therapy (SBRT) following post-SIRT dosimetry.Approach. We retrospectively analyzed post-treatment PET/CT images of 20 HCC patients after90Y SIRT. Predicted tumor response from SIRT was quantified based on personalized post-therapy dosimetry and corresponding response models. Predicted non-responding tumor regions were then targeted with a hypothetical SBRT boost plan using a framework for selecting eligible tumors and tumor subregions. SBRT boost plans were compared to SBRT plans targeting all tumors irrespective of SIRT dose with the same prescription and organ-at-risk (OAR) objectives. The potential benefit of SIRT followed by a SBRT was evaluated based on OAR dose and predicted toxicity compared to the independent SBRT treatment.Main results. Following SIRT, 14/20 patients had at least one predicted non-responding tumor considered eligible for a SBRT boost. When comparing SBRT plans, 10/14 (71%) SBRTboostand 12/20 (60%) SBRTaloneplans were within OAR dose constraints. For three patients, SBRTboostplans were within OAR constraints while SBRTaloneplans were not. Across the 14 eligible patients, SBRTboostplans had significantly less dose to the healthy liver (decrease in mean dose was on average ± standard deviation, 2.09 Gy ± 1.99 Gy, ) and reduced the overall targeted PTV volume (39% ± 21%) compared with SBRTalone.Significance. A clinical methodology for treating HCC using a synergized SIRT and SBRT approach is presented, demonstrating that it could reduce normal tissue toxicity risk in a majority of our retrospectively evaluated cases. Selectively targeting SIRT underdosed HCC lesions, or lesion subvolumes, with SBRT could improve tumor control and patient outcomes post-SIRT and allow SIRT to function as a target debulking tool for cases when SBRT is not independently feasible.

Direct incorporation of patient-specific efficacy and toxicity estimates in radiation therapy plan optimization.

PURPOSE: Current radiation therapy (RT) treatment planning relies mainly on pre-defined dose-based objectives and constraints to develop plans that aim to control disease while limiting damage to normal tissues during treatment. These objectives and constraints are generally population-based, in that they are developed from the aggregate response of a broad patient population to radiation. However, correlations of new biologic markers and patient-specific factors to treatment efficacy and toxicity provide the opportunity to further stratify patient populations and develop a more individualized approach to RT planning. We introduce a novel intensity-modulated radiation therapy (IMRT) optimization strategy that directly incorporates patient-specific dose response models into the planning process. In this strategy, we integrate the concept of utility-based planning where the optimization objective is to maximize the predicted value of overall treatment utility, defined by the probability of efficacy (e.g., local control) minus the weighted sum of toxicity probabilities. To demonstrate the feasibility of the approach, we apply the strategy to treatment planning for non-small cell lung cancer (NSCLC) patients. METHODS AND MATERIALS: We developed a prioritized approach to patient-specific IMRT planning. Using a commercial treatment planning system (TPS), we calculate dose based on an influence matrix of beamlet-dose contributions to regions-of-interest. Then, outside of the TPS, we hierarchically solve two optimization problems to generate optimal beamlet weights that can then be imported back to the TPS. The first optimization problem maximizes a patient's overall plan utility subject to typical clinical dose constraints. In this process, we facilitate direct optimization of efficacy and toxicity trade-off based on individualized dose-response models. After optimal utility is determined, we solve a secondary optimization problem that minimizes a conventional dose-based objective subject to the same clinical dose constraints as the first stage but with the addition of a constraint to maintain the optimal utility from the first optimization solution. We tested this method by retrospectively generating plans for five previously treated NSCLC patients and comparing the prioritized utility plans to conventional plans optimized with only dose metric objectives. To define a plan utility function for each patient, we utilized previously published correlations of dose to local control and grade 3-5 toxicities that include patient age, stage, microRNA levels, and cytokine levels, among other clinical factors. RESULTS: The proposed optimization approach successfully generated RT plans for five NSCLC patients that improve overall plan utility based on personalized efficacy and toxicity models while accounting for clinical dose constraints. Prioritized utility plans demonstrated the largest average improvement in local control (16.6%) when compared to plans generated with conventional planning objectives. However, for some patients, the utility-based plans resulted in similar local control estimates with decreased estimated toxicity. CONCLUSION: The proposed optimization approach, where the maximization of a patient's RT plan utility is prioritized over the minimization of standardized dose metrics, has the potential to improve treatment outcomes by directly accounting for variability within a patient population. The implementation of the utility-based objective function offers an intuitive, humanized approach to biological optimization in which planning trade-offs are explicitly optimized.

Biomechanical modeling of radiation dose-induced volumetric changes of the parotid glands for deformable image registration.

PURPOSE: Early animal studies suggest that parotid gland (PG) toxicity prediction could be improved by an accurate estimation of the radiation dose to sub-regions of the PG. Translation to clinical investigation requires voxel-level dose accumulation in this organ that responds volumetrically throughout treatment. To date, deformable image registration (DIR) has been evaluated for the PG using only surface alignment. We sought to develop and evaluate an advanced DIR technique capable of modeling these complex PG volume changes over the course of radiation therapy. MATERIALS AND METHODS: Planning and mid-treatment magnetic resonance images from 19 patients and computed tomography images from nine patients who underwent radiation therapy for head and neck cancer were retrospectively evaluated. A finite element model (FEM)-based DIR algorithm was applied between the corresponding pairs of images, based on boundary conditions on the PG surfaces only (Morfeus-spatial). To investigate an anticipated improvement in accuracy, we added a population model-based thermal expansion coefficient to simulate the dose distribution effect on the volume change inside the glands (Morfeus-spatialDose). The model accuracy was quantified using target registration error for magnetic resonance images, where corresponding anatomical landmarks could be identified. The potential clinical impact was evaluated using differences in mean dose, median dose, D98, and D50 of the PGs. RESULTS: In the magnetic resonance images, the mean (±standard deviation) target registration error significantly reduced by 0.25 ± 0.38 mm (p = 0.01) when using Morfeus-spatialDose instead of Morfeus-spatial. In the computed tomography images, differences in the mean dose, median dose, D98, and D50 of the PGs reached 2.9 ± 0.8, 3.8, 4.1, and 3.8 Gy, respectively, between Morfeus-spatial and Morfeus-spatialDose. CONCLUSION: Differences between Morfeus-spatial and Morfeus-spatialDose may be impactful when considering high-dose gradients of radiation in the PGs. The proposed DIR model can allow more accurate PG alignment than the standard model and improve dose estimation and toxicity prediction modeling.

Predictive Models to Determine Clinically Relevant Deviations in Delivered Dose for Head and Neck Cancer.

PURPOSE: This study aimed to improve the understanding of deviations between planned and accumulated doses and to establish metrics to predict clinically significant dosimetric deviations midway through treatment to evaluate the potential need to re-plan during fractionated radiation therapy (RT). METHODS AND MATERIALS: A total of 100 patients with head and neck cancer were retrospectively evaluated. Contours were mapped from the planning computed tomography (CT) scan to each fraction cone beam CT via deformable image registration. The dose was calculated on each cone beam CT and evaluated based on the mapped contours. The mean dose at each fraction was averaged to approximate the accumulated dose for structures with mean dose constraints, and the daily maximum dose was summed to approximate the accumulated dose for structures with maximum dose constraints. A threshold deviation value was calculated to predict for patients needing midtreatment re-planning. This predictive model was applied to 52 patients treated at a separate institution. RESULTS: Dose was accumulated on 10 organs over 100 patients. To generate a threshold deviation that predicted the need to re-plan with 100% sensitivity, the submandibular glands required re-planning if the delivered dose was at least 3.5 Gy higher than planned by fraction 15. This model predicts the need to re-plan the submandibular glands with 98.7% specificity. In the independent evaluation cohort, this model predicts the need to re-plan the submandibular glands with 100% sensitivity and 98.0% specificity. The oral cavity, intermediate clinical target volume, left parotid, and inferior constrictor patient groups each had 1 patient who exceeded the threshold deviation by the end of RT. By fraction 15 of 30 to 35 total fractions, the left parotid gland, inferior constrictor, and intermediate clinical target volume had a dose deviation of 3.1 Gy, 5.9 Gy, and 4.8 Gy, respectively. When a deformable image registration failure was observed, the dose deviation exceeded the threshold for at least 1 organ, demonstrating that an automated deformable image registration-based dose assessment process could be developed with user evaluation for cases that result in dose deviations. CONCLUSIONS: A midtreatment threshold deviation was determined to predict the need to replan for the submandibular glands by fraction 15 of 30 to 35 total fractions of RT.

Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy.

PURPOSE: To determine whether serial cone beam computed tomography (CBCT) images taken during head and neck radiation therapy (HNR) can improve chronic xerostomia prediction. METHODS AND MATERIALS: In a retrospective analysis, parotid glands (PGs) were delineated on daily kV CBCT images using deformable image registration for 119 HNR patients (60 or 70 Gy in 2 Gy fractions over 6 or 7 weeks). Deformable image registration accuracy for a subset of deformed contours was quantified using the Dice similarity coefficient and mean distance to agreement in comparison with manually drawn contours. Average weekly changes in CBCT-measured mean Hounsfield unit intensity and volume were calculated for each PG relative to week 1. Dose-volume histogram statistics were extracted from each plan, and interactions among dose, volume, and intensity were investigated. Univariable analysis and penalized logistic regression were used to analyze association with observer-rated xerostomia at 1 year after HNR. Models including CBCT delta imaging features were compared with clinical and dose-volume histogram-only models using area under the receiver operating characteristic curve (AUC) for grade ≥1 and grade ≥2 xerostomia prediction. RESULTS: All patients experienced end-treatment PG volume reduction with mean (range) ipsilateral and contralateral PG shrinkage of 19.6% (0.9%-58.4%) and 17.7% (4.4%-56.3%), respectively. Midtreatment volume change was highly correlated with mean PG dose (r = -0.318, P < 1e-6). Incidence of grade ≥1 and grade ≥2 xerostomia was 65% and 16%, respectively. For grade ≥1 xerostomia prediction, the delta-imaging model had an AUC of 0.719 (95% confidence interval [CI], 0.603-0.830), compared with 0.709 (95% CI, 0.603-0.815) for the dose/clinical model. For grade ≥2 xerostomia prediction, the dose/clinical model had an AUC of 0.692 (95% CI, 0.615-0.770), and the addition of contralateral PG changes modestly improved predictive performance, with an AUC of 0.776 (0.643-0.912). CONCLUSIONS: The rate of CBCT-measured PG image feature changes improves prediction over dose alone for chronic xerostomia prediction. Analysis of CBCT images acquired for treatment positioning may provide an inexpensive monitoring system to support toxicity-reducing adaptive radiation therapy.

Implementing Radiation Dose-Volume Liver Response in Biomechanical Deformable Image Registration.

PURPOSE: Understanding anatomic and functional changes in the liver resulting from radiation therapy is fundamental to the improvement of normal tissue complication probability models needed to advance personalized medicine. The ability to link pretreatment and posttreatment imaging is often compromised by significant dose-dependent volumetric changes within the liver that are currently not accounted for in deformable image registration (DIR) techniques. This study investigated using delivered dose, in combination with other patient factors, to biomechanically model longitudinal changes in liver anatomy for follow-up care and re-treatment planning. METHODS AND MATERIALS: Population models describing the relationship between dose and hepatic volume response were produced using retrospective data from 33 patients treated with focal radiation therapy. A DIR technique was improved by implementing additional boundary conditions associated with the dose-volume response in series with a previously developed biomechanical DIR algorithm. Evaluation of this DIR technique was performed on computed tomography imaging from 7 patients by comparing the model-predicted volumetric change within the liver with the observed change, tracking vessel bifurcations within the liver through the deformation process, and then determining target registration error between the predicted and identified posttreatment bifurcation points. RESULTS: Evaluation of the proposed DIR technique showed that all lobes were volumetrically deformed to within the respective contour variability of each lobe. The average target registration error achieved was 7.3 mm (2.8 mm left-right and anterior-posterior and 5.1 mm superior-inferior), with the superior-inferior component within the average limiting slice thickness (6.0 mm). This represented a significant improvement (P<.01, Wilcoxon test) over the application of the previously published biomechanical DIR algorithm (10.9 mm). CONCLUSIONS: This study demonstrates the feasibility of implementing dose-driven volumetric response in deformable registration, enabling improved accuracy of modeling liver anatomy changes, which could allow for improved dose accumulation, particularly for patients who require additional liver radiation therapy.

Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

There is a need for robust, fully automated whole body organ segmentation for diagnostic CT. This study investigates and optimizes a Random Forest algorithm for automated organ segmentation; explores the limitations of a Random Forest algorithm applied to the CT environment; and demonstrates segmentation accuracy in a feasibility study of pediatric and adult patients. To the best of our knowledge, this is the first study to investigate a trainable Weka segmentation (TWS) implementation using Random Forest machine-learning as a means to develop a fully automated tissue segmentation tool developed specifically for pediatric and adult examinations in a diagnostic CT environment. Current innovation in computed tomography (CT) is focused on radiomics, patient-specific radiation dose calculation, and image quality improvement using iterative reconstruction, all of which require specific knowledge of tissue and organ systems within a CT image. The purpose of this study was to develop a fully automated Random Forest classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT examinations based on pediatric and adult CT protocols. Seven materials were classified: background, lung/internal air or gas, fat, muscle, solid organ parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the TWS plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 (n) , (n from 0 to 4), along with noise reduction and edge preserving filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random Forest algorithm used 200 trees with 2 features randomly selected per node. The optimized auto-segmentation algorithm resulted in 16 image features including features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice similarity coefficient (DSC) calculations between manually segmented and Random Forest algorithm segmented images from 21 patient image sections, were analyzed. The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86 ± 0.03 for pediatric patient protocols, and 0.85 ± 0.04 for adult patient protocols. Additionally, 100 randomly selected patient examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range: 0.82-0.98), specificity of 0.89 (range: 0.70-0.98), and accuracy of 0.90 (range: 0.76-0.98) were demonstrated. In this study, we demonstrate that this fully automated segmentation tool was able to produce fast and accurate segmentation of the neck and trunk of the body over a wide range of patient habitus and scan parameters.