Search

Purpose Recent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. Methods and materials A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient. Results PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p

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.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.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.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.

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 after 90Y 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%) SBRTboost and 12/20 (60%) SBRTalone plans were within OAR dose constraints. For three patients, SBRTboost plans were within OAR constraints while SBRTalone plans were not. Across the 14 eligible patients, SBRTboost plans 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.

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.

Purpose/Objective(s) Head and neck cancer (HNC) RT typically results in high doses to organs-at-risk (OARs), contributing to decreased quality-of-life (QOL) seen in patient reported outcomes (PRO). Current RT planning methods are driven by singular dosimetric trade-offs that may be inconsistent with improving a patient's overall predicted total toxicity burden (TTB). We developed an aggregate toxicity assessment over time including PROs that represents a paradigm shift over informal summary of individual toxicity. Here we introduce a treatment planning approach to directly optimize and reduce the predicted TTB-PRO of a patient's RT plan. Materials/Methods We implemented a prioritized approach to TTB optimization using an in-house optimization plugin linked to a commercial treatment planning system. Prioritized TTB (P-TTB) plans were optimized using a two-stage hierarchal method for inverse IMRT planning. In the first stage, TTB, defined as the weighted-sum of NTCPs, is minimized subject to high-priority OAR dose limits, target prescription, and target coverage requirements. Weights for the toxicities directly represent the relative undesirability of toxicities. In the second stage, a weighted-sum dose-metric objective is minimized, similar to current clinical practice. This stage is subject to the same constraints as the first stage, but an additional constraint for TTB is incorporated based on the optimal solution of the first stage. Using this approach, we retrospectively generated plans for 5 HNC patients and compared the resulting plans to IMRT plans generated without the added TTB constraint. Both plans consisted of 9 equally spaced beams with the same OAR dose limits and dose-metric objective. Target dose requirements were based on simultaneous integrated boost prescriptions of 70 and 56 Gy delivered in 35 fractions. TTB was calculated using previously published NTCP models for dysphagia and xerostomia. Dysphagia was based on increase in aspiration or HNQOL score > 81 as a function of pharyngeal constrictor (PC) mean dose. Xerostomia was based on parotid flow ratio 73 as a function of parotid gland (PG) mean dose. Results Our optimization engine successfully generated P-TTB plans for all five patients while maintaining similar target coverage to the standard dosimetrically optimized plans. P-TTB plans resulted in average absolute NTCP reductions of 15.5% (range: 0.2 - 53.4) for dysphagia and 20.5% (1.9 - 68.5) for xerostomia. These changes correlate to average mean dose decreases of 6.9 Gy (0.2 - 22.7) for PC and 10.2 Gy (1.2 - 33.5) for PGs. P-TTB planning resulted in greater fluence modulation and higher doses to OARs not included in the calculation of TTB. Conclusion The P-TTB planning method offers an intuitive optimization approach to balancing trade-offs between various treatment outcomes and has the ability to directly reduce and redistribute predicted toxicity which may aid in improving QOL for HNC patients receiving RT.

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.

Purpose To study the dosimetric impact of deformable image registration–based contour propagation on MRI-based cervical cancer brachytherapy planning. Methods and Materials High-risk clinical target volume (HRCTV) and organ-at-risk (OAR) contours were delineated on MR images of 10 patients who underwent ring and tandem brachytherapy. A second set of contours were propagated using a commercially available deformable registration algorithm. “Manual-contour” and “propagated-contour” plans were optimized to achieve a maximum dose to the most minimally exposed 90% of the volume (D90) (%) of 6 Gy/fraction, respecting minimum dose to the most exposed 2cc of the volume (D2cc) OAR constraints of 5.25 Gy and 4.2 Gy/fraction for bladder and rectum/sigmoid (86.5 and 73.4 Gy equivalent dose in 2 Gy fractions [EQD2] for external beam radiotherapy [EBRT] + brachytherapy, respectively). Plans were compared using geometric and dosimetric (total dose [EQD2] EBRT + brachytherapy) parameters. Results The differences between the manual- and propagated-contour plans with respect to the HRCTV D90 and bladder, rectum, and sigmoid D2cc were not statistically significant (per-fraction basis). For the EBRT + brachytherapy course, the D2cc delivered to the manually contoured OARs by the propagated-contour plans ranging 98–107%, 95–105%, and 92–108% of the dose delivered by the manual-contour plans (max 90.4, 70.3, and 75.4 Gy for the bladder, rectum, and sigmoid, respectively). The HRCTV dose in the propagated-contour plans was 97–103% of the dose in the manual-contour plans (maximum difference 2.92 Gy). Increased bladder filling resulted in increased bladder dose in manual- and propagated-contour plans. Conclusions When deformable image registration–propagated contours are used for cervical brachytherapy planning, the HRCTV dose is similar to the dose delivered by manual-contour plans and the doses delivered to the OARs are clinically acceptable, suggesting that our algorithm can replace manual contouring for appropriately selected cases that lack major interfractional anatomical changes.

With improved survivorship in medulloblastoma, there has been an increasing incidence of late complications. To date, no studies have specifically addressed the risk of radiation-associated diffuse intrinsic pontine glioma (DIPG) in medulloblastoma survivors. Query of the International DIPG Registry identified six cases of DIPG with a history of medulloblastoma treated with radiotherapy. All patients underwent central radiologic review that confirmed a diagnosis of DIPG. Six additional cases were identified in reports from recent cooperative group medulloblastoma trials (total n = 12; ages 7 to 21 years). From these cases, molecular subgrouping of primary medulloblastomas with available tissue (n = 5) revealed only non-WNT, non-SHH subgroups (group 3 or 4). The estimated cumulative incidence of DIPG after post-treatment medulloblastoma ranged from 0.3–3.9%. Posterior fossa radiation exposure (including brainstem) was greater than 53.0 Gy in all cases with available details. Tumor/germline exome sequencing of three radiation-associated DIPGs revealed an H3 wild-type status and mutational signature distinct from primary DIPG with evidence of radiation-induced DNA damage. Mutations identified in the radiation-associated DIPGs had significant molecular overlap with recurrent drivers of adult glioblastoma (e.g. NRAS, EGFR, and PTEN), as opposed to epigenetic dysregulation in H3-driven primary DIPGs. Patients with radiation-associated DIPG had a significantly worse median overall survival (median 8 months; range 4–17 months) compared to patients with primary DIPG. Here, it is demonstrated that DIPG occurs as a not infrequent complication of radiation therapy in survivors of pediatric medulloblastoma and that radiation-associated DIPGs may present as a poorly-prognostic distinct molecular subgroup of H3 wild-type DIPG. Given the abysmal survival of these cases, these findings provide a compelling argument for efforts to reduce exposure of the brainstem in the treatment of medulloblastoma. Additionally, patients with radiation-associated DIPG may benefit from future therapies targeted to the molecular features of adult glioblastoma rather than primary DIPG. Electronic supplementary material The online version of this article (10.1186/s40478-018-0570-9) contains supplementary material, which is available to authorized users.

With improved survivorship in medulloblastoma, there has been increasing recognition of the occurrence of secondary malignant brain tumors. To date, no studies have specifically addressed the risk of diffuse intrinsic pontine glioma (DIPG) in medulloblastoma survivors. We queried the International DIPG Registry and identified six cases of DIPG with prior medulloblastoma. Six additional cases were identified in reports from recent cooperative group medulloblastoma trials. Incidence of DIPG after medulloblastoma ranged from 0.3–3.9%. All 12 cases underwent surgical resection followed by craniospinal photon irradiation (range 18–36 Gy) and posterior fossa boost (range 19.8–36 Gy). Posterior fossa exposure was greater than 53 Gy in all cases. Median time to diagnosis of secondary DIPG was 7 years (range 2–11 years). Patients died of secondary DIPG a median of 8 months after diagnosis (range 4–17 months). Molecular subgroup of primary medulloblastomas with available tissue (n=5) revealed only non-WNT, non-SHH subgroups (group 3 or 4). Tumor/germline exome sequencing of three secondary DIPGs demonstrated tumors to be H3.3 wildtype and harbor higher mutational burden than radiation-naïve DIPGs. Mutational signature analysis of secondary DIPGs showed mutations consistent with radiation-induced DNA damage (e.g., insertional event in TP53), as well as mutations in other oncogenic drivers (e.g., NRAS, PI3KCA), suggestive of distinct mutational processes compared with primary DIPGs. In conclusion, we report for the first time that survivors of pediatric medulloblastoma are at risk for the development of secondary DIPG, likely consequent to radiation exposure. This risk highlights the importance of radiation field, volume, and modality in medulloblastoma treatment.

Radiation therapy has been associated with increased risk of secondary gliomas in survivors of childhood medulloblastoma. Despite this clear association, no studies have specifically addressed the risk of diffuse intrinsic pontine glioma (DIPG) after craniospinal irradiation in medulloblastoma survivors. We performed a systematic review of patients enrolled in the DIPG Registry and Repository as well as a literature review of studies reporting recent cooperative group medulloblastoma trials in order to identify cases of DIPG occurring after radiation therapy for pediatric medulloblastoma. Eleven cases of secondary DIPG in patients with primary medulloblastoma were identified from the DIPG Registry (n=5) and literature review (n=6). Patients were diagnosed with primary medulloblastoma between the ages of 2-9 years. All patients underwent surgical resection followed by craniospinal photon irradiation (range 18-36 Gy) and posterior fossa boost (range 19.8-36 Gy). For cases with dosimetric information available, mean brainstem exposure was 50.1-54.0 Gy. Median time to diagnosis of secondary DIPG was 7.4 years (range 2-11 years). Patients died of secondary DIPG a median of 8 months after diagnosis (range 4-17 months). We performed germline and tumor DNA and RNA sequencing of secondary DIPGs at autopsy, when available, which revealed no germline cancer predisposition syndromes and mutations associated with DIPG and non-brainstem high-grade glioma (TP53 loss, PTEN loss, and NRAS mutation). The incidence of DIPG as a secondary malignancy after medulloblastoma ranged from 0.3-2.6% between the involved institutions and reported studies. In conclusion, we report that survivors of pediatric medulloblastoma are at increased risk for the development of secondary DIPG. While treatment varied for the patients in our cohort, all cases resulted in death within 2 years of diagnosis. This risk highlights the importance of radiation field and modality in the treatment of children with medulloblastoma, and provides a compelling argument for efforts to reduce brainstem exposure.

PURPOSE: To determine whether serial cone-beam computed tomography (CBCT) images taken during head and neck radiotherapy (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 (DIR) for 119 HNR patients (60 or 70 Gy in 2 Gy/fraction over 6 or 7 weeks). DIR accuracy for a subset of deformed contours was quantified using the Dice similarity coefficient (DSC) and mean distance-to-agreement (MDA) in comparison to manually drawn contours. Average weekly changes in CBCT-measured mean HU intensity and volume were calculated for each PG relative to week 1. Dose-volume-histogram (DVH) statistics were extracted from each plan, and interactions between dose, volume, and intensity were investigated. Univariable analysis and penalized logistic regression were used to analyze association with observer-rated xerostomia at one year following HNR. Models including CBCT delta imaging features were compared to clinical/DVH-only models using area under the receiver-operating characteristic curve (AUC) for Grade 1 or higher (Grade≥1) and for Grade 2 or higher (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. Mid-treatment volume change was highly correlated with mean PG dose (r=−0.318, p

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.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.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).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.

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.

Purpose: It is well documented that the delivered dose to patients undergoing radiotherapy (RT) is often different from the planned dose due to geometric variability and uncertainties in patient positioning. Recent work suggests that accumulated dose to the GTV is a better predictor of progression compared to the minimum planned dose to the PTV. The purpose of this study is to evaluate if deviations from the planned dose can contributed to tumor progression. Methods: From 2010 to 2014 an in-house Phase II clinical trial of adaptive stereotactic body RT was completed. Of the 90 patients enrolled, 7 patients had a local recurrence defined on contrast enhanced CT or MR imaging 3–21 months after completion of RT. Retrospective dose accumulation was performed using a biomechanical model-based deformable image registration algorithm (DIR) to accumulate the dose based on the kV CBCT acquired prior to each fraction for soft tissue alignment of the patient. The DIR algorithm was previously validated for geometric accuracy in the liver (target registration error = 2.0 mm) and dose accumulation in a homogeneous image, similar to a liver CBCT (gamma index = 91%). Following dose accumulation, the minimum dose to 0.5 cc of the GTV was compared between the planned and accumulated dose. Work is ongoing to evaluate the tumor control probability based on the planned and accumulated dose. Results: DIR and dose accumulation was performed on all fractions for 6 patients with local recurrence. The difference in minimum dose to 0.5 cc of the GTV ranged from −0.3–2.3 Gy over 3–5 fractions. One patient had a potentially significant difference in minimum dose of 2.3 Gy. Conclusion: Dose accumulation can reveal tumor underdosage, improving our ability to understand recurrence and tumor progression patterns, and could aid in adaptive re-planning during therapy to correct for this. This work was supported in part by NIH P01CA059827.

Purpose: To perform validation and commissioning of a commercial deformable image registration (DIR) algorithm (Velocity, Varian Medical Systems) for numerous clinical sites using single and multi-modality images. Methods: In this retrospective study, the DIR algorithm was evaluated for 10 patients in each of the following body sites: head and neck (HN), prostate, liver, and gynecological (GYN). HN DIRs were evaluated from planning (p)CT to re-pCT and pCTs to daily CBCTs using dice similarity coefficients (DSC) of corresponding anatomical structures. Prostate DIRs were evaluated from pCT to CBCTs using DSC and target registration error (TRE) of implanted RF beacons within the prostate. Liver DIRs were evaluated from pMR to pCT using DSC and TRE of vessel bifurcations. GYN DIRs were evaluated between fractionated brachytherapy MRIs using DSC of corresponding anatomical structures. Results: Analysis to date has given average DSCs for HN pCT-to-(re)pCT DIR for the brainstem, cochleas, constrictors, spinal canal, cord, esophagus, larynx, parotids, and submandibular glands as 0.88, 0.65, 0.67, 0.91, 0.77, 0.69, 0.77, 0.87, and 0.71, respectively. Average DSCs for HN pCT-to-CBCT DIR for the constrictors, spinal canal, esophagus, larynx, parotids, and submandibular glands were 0.64, 0.90, 0.62, 0.82, 0.75, and 0.69, respectively. For prostate pCT-to-CBCT DIR the DSC for the bladder, femoral heads, prostate, and rectum were 0.71, 0.82, 0.69, and 0.61, respectively. Average TRE using implanted beacons was 3.35 mm. For liver pCT-to-pMR, the average liver DSC was 0.94 and TRE was 5.26 mm. For GYN MR-to-MR DIR the DSC for the bladder, sigmoid colon, GTV, and rectum were 0.79, 0.58, 0.67, and 0.76, respectively. Conclusion: The Velocity DIR algorithm has been evaluated over a number of anatomical sites. This work functions to document the uncertainties in the DIR in the commissioning process so that these can be accounted for in the development of downstream clinical processes. This work was supported in part by a co-development agreement with Varian Medical Systems

Purpose: Previous studies have shown that radiotherapy treatment for liver metastases causes marked liver hypertrophy in areas receiving low dose and atrophy/fibrosis in areas receiving high dose. The purpose of this work is to develop and evaluate a biomechanical model-based dose-response model to describe these liver responses to SBRT. Methods: In this retrospective study, a biomechanical model-based deformable registration algorithm, Morfeus, was expanded to include dose-based boundary conditions. Liver and tumor volumes were contoured on the planning images and CT/MR images three months post-RT and converted to finite element models. A thermal expansion-based relationship correlating the delivered dose and volume response was generated from 22 patients previously treated. This coefficient, combined with the planned dose, was applied as an additional boundary condition to describe the volumetric response of the liver of an additional cohort of metastatic liver patients treated with SBRT. The accuracy of the model was evaluated based on overall volumetric liver comparisons and the target registration error (TRE) using the average deviations in positions of identified vascular bifurcations on each set of registered images, with a target accuracy of the 2.5mm isotropic dose grid (vector dimension 4.3mm). Results: The thermal expansion coefficient models the volumetric change of the liver to within 3%. The accuracy of Morfeus with dose-expansion boundary conditions a TRE of 5.7±2.8mm compared to 11.2±3.7mm using rigid registration and 8.9±0.28mm using Morfeus with only spatial boundary conditions. Conclusion: A biomechanical model has been developed to describe the volumetric and spatial response of the liver to SBRT. This work will enable the improvement of correlating functional imaging with delivered dose, the mapping of the delivered dose from one treatment onto the planning images for a subsequent treatment, and will further provide information to assist with the biological characterization of patients’ response to radiation.