Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Modeling gamma knife radiosurgical toxicity for multiple brain metastases.

BACKGROUND AND PURPOSE: Radiation oncology protocols for single fraction radiosurgery recommend setting dosing criteria based on assumed risk of radionecrosis, which can be predicted by the 12 Gy normal brain volume (V12). In this study, we show that tumor surface area (SA) and a simple power-law model using only preplan variables can estimate and minimize radiosurgical toxicity. MATERIALS AND METHODS: A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rxn, and tumor longest axial dimension LAD (V12 âˆ¼ Rxn1.5*LAD2), was independently validated using a secondary 63-patient cohort with 302 brain metastases. RESULTS: Surface area was the best univariate linear predictor of V12 (adjR2 = 0.770), followed by longest axial dimension (adjR2 = 0.755) and volume (adjR2 = 0.745). The power-law model accounted for 90% variance in V12 for 1217 metastatic lesions (adjR2 = 0.906) and 245 patients (adjR2 = 0.896). The average difference ΔV12 between predicted and measured V12s was (0.28 ± 0.55) cm3 per lesion and (1.0 ± 1.2) cm3 per patient. The power-law predictive capability was validated using a secondary 63-patient dataset (adjR2 = 0.867) with 302 brain metastases (adjR2 = 0.825). CONCLUSION: Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.

Physical wedge as a tool for radiochromic film calibration.

Reliable calibration is one of the major challenges in using radiochromic films (RCF) for radiation dosimetry. In this study the feasibility of using dose gradients produced by a physical wedge (PW) for RCF calibration was investigated. The aim was to establish an efficient and reproducible method for calibrating RCF using a PW. Film strips were used to capture the wedge dose profile for five different exposures and the acquired scans were processed to generate corresponding net optical density wedge profiles. The proposed method was compared to the benchmark calibration, following the guidelines for precise calibration using uniform dose fields. The results of the benchmark comparison presented in this paper showed that using a single film strip for measuring wedge dose profile is sufficient for estimating a reliable calibration curve within the recorded dose range. Furthermore, the PW calibration can be extrapolated or extended by using multiple gradients for the optimal coverage of the desired calibration dose range. The method outlined in this paper can be readily replicated using the equipment and expertise commonly found in a radiotherapy center. Once the dose profile and central axis attenuation coefficient of the PW are determined, they can serve as a reference for a variety of calibrations using different types and batches of film. This investigation demonstrated that the calibration curves obtained with the presented PW calibration method are within the bounds of the measurement uncertainty evaluated for the conventional uniform dose field calibration method.

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit.

Objectives. The purpose of this study is to present data from the clinical commissioning of an Xstrahl 150 x-ray unit used for superficial radiotherapy,Methods. Commissioning tasks included vendor acceptance tests, timer reproducibility, linearity and end-effect measurements, half-value layer (HVL) measurements, inverse square law verification, head-leakage measurements, and beam output calibration. In addition, percent depth dose (PDD) curves were determined for different combinations of filter/kV settings and applicators. Automated PDD water phantom scans were performed utilizing four contemporary detectors: a microDiamond detector, a microSilicon detector, an EDGE detector, and a PinPoint ionization chamber. The measured PDD data were compared to the published values in BJR Supplement 25,Results. The x-ray unit's mechanical, safety, and radiation characteristics were within vendor-stated specifications. Across sixty commissioned x-ray beams, the PDDs determined in water using solid state detectors were in excellent agreement with the BJR 25 data. For the lower (<100 kVp) and medium-energy (≥100 kVp) superficial beams the average agreement was within [-3.6,+0.4]% and [-3.7,+1.4]% range, respectively. For the high-energy superficial (low-energy orthovoltage) x-rays at 150 kVp, the average difference for the largest 20 × 20 cm2collimator was (-0.7 ± 1.0)%,Conclusions. This study presents machine characterization data collected for clinical use of a superficial x-ray unit. Special focus was placed on utilizing contemporary detectors and techniques for the relative PDD measurements using a motorized water phantom. The results in this study confirm that the aggregate values published in the BJR 25 report still serve as a valid benchmark when comparing data from site-specific measurements, or the reference data for clinical utilization without such measurements,Advances in knowledge. This paper presents comprehensive data from the acceptance and commissioning of a modern kilovoltage superficial x-ray radiotherapy machine. Comparisons between the PDD data measured in this study using different detectors and BJR 25 data are highlighted.

Ensemble learning for glioma patients overall survival prediction using pre-operative MRIs.

Objective: Gliomas are the most common primary brain tumors. Approximately 70% of the glioma patients diagnosed with glioblastoma have an averaged overall survival (OS) of only ∼16 months. Early survival prediction is essential for treatment decision-making in glioma patients. Here we proposed an ensemble learning approach to predict the post-operative OS of glioma patients using only pre-operative MRIs.Approach: Our dataset was from the Medical Image Computing and Computer Assisted Intervention Brain Tumor Segmentation challenge 2020, which consists of multimodal pre-operative MRI scans of 235 glioma patients with survival days recorded. The backbone of our approach was a Siamese network consisting of twinned ResNet-based feature extractors followed by a 3-layer classifier. During training, the feature extractors explored traits of intra and inter-class by minimizing contrastive loss of randomly paired 2D pre-operative MRIs, and the classifier utilized the extracted features to generate labels with cost defined by cross-entropy loss. During testing, the extracted features were also utilized to define distance between the test sample and the reference composed of training data, to generate an additional predictor via K-NN classification. The final label was the ensemble classification from both the Siamese model and the K-NN model.Main results: Our approach classifies the glioma patients into 3 OS classes: long-survivors (>15 months), mid-survivors (between 10 and 15 months) and short-survivors (<10 months). The performance is assessed by the accuracy (ACC) and the area under the curve (AUC) of 3-class classification. The final result achieved an ACC of 65.22% and AUC of 0.81.Significance: Our Siamese network based ensemble learning approach demonstrated promising ability in mining discriminative features with minimal manual processing and generalization requirement. This prediction strategy can be potentially applied to assist timely clinical decision-making.

Dose kernel decomposition for spot-based radiotherapy treatment planning.

PURPOSE: Pre-calculation of accurate dose deposition kernels for treatment planning of spot-based radiotherapies, such as Gamma Knife (GK) and Gamma Pod (GP), can be very time-consuming and may require large data storage with an enormous number of possible spots. We proposed a novel kernel decomposition (KD) model to address accurate and fast (real-time) dose calculation with reduced data storage requirements for spot-based treatment planning. The application of the KD model was demonstrated for clinical GK and GP radiotherapy platforms. METHODS: The dose deposition kernel at each spot (shot position) is modeled as the product of a shift-invariant kernel based on a reference kernel and spatially variant scale factor. The reference kernel, one for each collimator, is defined at the center of the commissioning phantom for GK and at the center of the treatment target for GP and calculated using the Monte Carlo (MC) method. The spatially variant scale factor is defined as the ratio of the mean tissue maximum ratio (TMR) at the candidate shot position to that at the reference kernel position, and the mean TMR map is calculated within the entire volume through parallel beam ray tracing on the density image followed by averaging over all source directions. The proposed KD dose calculations were compared with the MC method and with the GK and GP treatment planning system (TPS) computations for various shot positions and collimator sizes utilizing a phantom and 14 and 12 clinical plans for GK and GP, respectively. RESULTS: For the phantom study, the KD Gamma index (3%/1 mm) passing rates were greater than 99% (median 100%) relative to the MC doses, except for the shots close to the boundary. The passing rates dropped below 90% for 8 mm (16 mm) shots positioned within ∼1 cm (∼2 cm) of the boundary. For the clinical GK plans, the KD Gamma passing rates were greater than 99% (median 100%) compared to the MC and greater than 92% (median 99%) compared to the TPS. For the clinical GP plans, the KD Gamma passing rates were greater than 95% (median 98%) compared to the MC and greater than 91% (median 97%) compared to the TPS. The scale factors were calculated in sub-seconds with GPU implementation and only need to be calculated once before treatment plan optimization. The calculation of the dose kernel was also within sub-seconds without requiring beam-by-beam calculation commonly done in the TPS. CONCLUSION: The proposed model can provide an accurate dose and enables real-time dose and derivative calculations by kernel shifting and scaling without pre-calculating or requiring large data storage for GK and GP dose deposition kernels during treatment planning. This model could be useful for spot-based radiotherapy treatment planning by allowing an efficient global fine search for optimal spots.

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation.

Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.

Expanded Radiosurgery Capabilities Utilizing Gamma Knife Icon™.

The indications and techniques for the treatment of intracranial lesions continue to evolve with the advent of novel technologies. The Gamma Knife Icon™ (GK Icon™) is the most recent model available from Elekta, providing a frameless solution for stereotactic radiosurgery. At our institution, 382 patients with 3,213 separate intracranial lesions have been treated with frameless stereotactic radiotherapy using the GK Icon. The wide range of diagnoses include brain metastases, meningiomas, arteriovenous malformations, acoustic neuromas, pituitary adenomas, and several other histologies. The ability to perform both frame and frameless treatments on the GK Icon has significantly increased our daily volume by almost 50% on a single machine. Although the frameless approach allows one to take advantage of the precision in radiosurgery, the intricacies regarding treatment with this frameless system are not well established. Our initial experience will help to serve as a guide to those wishing to implement this novel technology in their practice.

Oxygen-Sensitive MRI: A Predictive Imaging Biomarker for Tumor Radiation Response?

PURPOSE: To develop a noninvasive prognostic imaging biomarker related to hypoxia to predict SABR tumor control. METHODS AND MATERIALS: A total of 145 subcutaneous syngeneic Dunning prostate R3327-AT1 rat tumors were focally irradiated once using cone beam computed tomography guidance on a small animal irradiator at 225 kV. Various doses in the range of 0 to 100 Gy were administered, while rats breathed air or oxygen, and tumor control was assessed up to 200 days. Oxygen-sensitive magnetic resonance imaging (MRI) (T1-weighted, ΔR1, ΔR2*) was applied to 79 of these tumors at 4.7 T to assess response to an oxygen gas breathing challenge on the day before irradiation as a probe of tumor hypoxia. RESULTS: Increasing radiation dose in the range of 0 to 90 Gy enhanced tumor control of air-breathing rats with a TCD50 estimated at 59.6 ± 1.5 Gy. Control was significantly improved at some doses when rats breathed oxygen during irradiation (eg, 40 Gy; P < .05), and overall there was a modest left shift in the control curve: TCD50(oxygen) = 53.1 ± 3.1 Gy (P < .05 vs air). Oxygen-sensitive MRI showed variable response to oxygen gas breathing challenge; the magnitude of T1-weighted signal response (%ΔSI) allowed stratification of tumors in terms of local control at 40 Gy. Tumors showing %ΔSI >0.922 with O2-gas breathing challenge showed significantly better control at 40 Gy during irradiation while breathing oxygen (75% vs 0%, P < .01). In addition, increased radiation dose (50 Gy) substantially overcame resistance, with 50% control for poorly oxygenated tumors. Stratification of dose-response curves based on %ΔSI >0.922 revealed different survival curves, with TCD50 = 36.2 ± 3.2 Gy for tumors responsive to oxygen gas breathing challenge; this was significantly less than the 54.7 ± 2.4 Gy for unresponsive tumors (P < .005), irrespective of the gas inhaled during tumor irradiation. CONCLUSIONS: Oxygen-sensitive MRI allowed stratification of tumors in terms of local control at 40 Gy, indicating its use as a potential predictive imaging biomarker. Increasing dose to 50 Gy overcame radiation resistance attributable to hypoxia in 50% of tumors.

A general algorithm for distributed treatments of multiple brain metastases.

PURPOSE: Stereotactic radiosurgery (SRS) has become a primary treatment for multiple brain metastases (BM) but may require distribution of BMs over several sessions to make delivery time and radiation toxicity manageable. Contrasting to equal fraction dose in conventional fractionation, distributed SRS delivers full dose to a subset of BMs in each session while avoiding adjacent BMs in the same session to reduce toxicity from overlapping radiation. However, current clinical treatment planning for distributed SRS relies on manual BM assignment, which can be tedious and error prone. This work describes a novel approach to automate the distribution of BM in the Gamma Knife (GK) clinical workflow. METHODS: We represent each BM as an electrostatic field of the same polarity that exerts repulsive forces on other BMs in the same session. This representation naturally leads to separation of close BMs into different sessions to lower the potential energy. Indeed, the BM distribution problem can be formulated as minimization of the total potential energy from all treatment sessions subject to delivery time constraints in mixed-integer quadratic programming (MIQP). We retrospectively studied eight clinical GK cases of multiple BM and compared the automated MIQP solution with clinically used BM distribution to demonstrate the efficacy of the proposed approach. RESULTS: With the problem size equal to the number of BMs times the number of sessions, this MIQP can be solved in a minute on a personal workstation. The MIQP solution effectively separated BMs for a given number of treatment sessions and evened out the delivery time distribution among sessions. Compared to the clinically used manual BM distributions in paired t-test for a similar range of delivery time variation, the automated BM distributions had lower energy objectives (range of decrease: [11% 89%]; median: 25%; P = . 073 ), more uniformly distributed treatment volumes (range of decrease for the normalized standard deviation of volume distribution: [0.02 0.95]; median: 0.16; P = . 013 ), more scattered BMs in each treatment session (range of increase for the mean minimum BM distance: [0 14] mm; median: 6 mm; P = . 008 ), and lower overall V 12 (range of decrease: [0.0 1.6] cc; median: 0.2 cc; P = . 052 ). Moreover, without distribution, that is, with all BMs treated in the same session, V 12 was substantially larger compared to both manual and automated BM distributions; the increase ranged from 0.1 to 16.6 cc with a median of 1.3 cc. CONCLUSIONS: The proposed approach models the clinical practice and provides an efficient solution for optimal selection of BM subsets for distributed SRS. Further evaluations are underway to establish this approach as a tool for improving clinical workflow and to facilitate systematic study on the benefits of distributed SRS treatments.

Dose rate determination for preclinical total body irradiation.

The accuracy of delivered radiation dose and the reproducibility of employed radiotherapy methods are key factors for preclinical radiobiology applications and research studies. In this work, ionization chamber (IC) measurements and Monte Carlo (MC) simulations were used to accurately determine the dose rate for total body irradiation (TBI), a classic radiobiologic and immunologic experimental method. Several phantom configurations, including large solid water slab, small water box and rodentomorphic mouse and rat phantoms were simulated and measured for TBI setup utilizing a preclinical irradiator XRad320. The irradiator calibration and the phantom measurements were performed using an ADCL calibrated IC N31010 following the AAPM TG-61 protocol. The MC simulations were carried out using Geant4/GATE to compute absorbed dose distributions for all phantom configurations. All simulated and measured geometries had favorable agreement. On average, the relative dose rate difference was 2.3%. However, the study indicated large dose rate deviations, if calibration conditions are assumed for a given experimental setup as commonly done for a quick determination of irradiation times utilizing lookup tables and hand calculations. In a TBI setting, the reference calibration geometry at an extended source-to-surface distance and a large reference field size is likely to overestimate true photon scatter. Consequently, the measured and hand calculated dose rates, for TBI geometries in this study, had large discrepancies: 16% for a large solid water slab, 27% for a small water box, and 31%, 36%, and 30% for mouse phantom, rat phantom, and mouse phantom in a pie cage, respectively. Small changes in TBI experimental setup could result in large dose rate variations. MC simulations and the corresponding measurements specific to a designed experimental setup are vital for accurate preclinical dosimetry and reproducibility of radiobiological findings. This study supports the well-recognized need for physics consultation for all radiobiological investigations.

Margin-Free Fractionated Stereotactic Radiation Therapy for Pediatric Brain Tumors.

PURPOSE: Conventional radiation therapy (RT) to pediatric brain tumors exposes a large volume of normal brain to unwarranted radiation causing late toxicity. We hypothesized that in well demarcated pediatric tumors lacking microscopic extensions, fractionated stereotactic RT (SRT), without target volume expansions, can reduce high dose normal tissue irradiation without affecting local control. METHODS AND MATERIALS: Between 2008 and 2017, 52 pediatric patients with brain tumors were treated using the CyberKnife (CK) with SRT in 180 to 200 cGy per fraction. Thirty representative cases were retrospectively planned for intensity modulated RT (IMRT) with 4-mm PTV expansion. We calculated the volume of normal tissue within the high or intermediate dose region adjacent to the target. Plan quality and radiation dose-volume dosimetry parameters were compared between CK and IMRT plans. We also reported overall survival, progression-free survival (PFS), and local control. RESULTS: Tumors included low-grade gliomas (n = 28), craniopharyngiomas (n = 16), and ependymomas (n = 8). The volumes of normal tissue receiving high (≥80% of prescription dose or ≥40 Gy) or intermediate (80% > dose ≥50% of the prescription dose or 40 Gy > dose ≥25 Gy) dose were significantly smaller with CK versus IMRT plans (P < .0001 for all comparisons). With a median follow-up of 3.7 years (range, 0.1-9.0), 3-year local control was 92% for all patients. Eight failures occurred: 1 craniopharyngioma (marginal), 2 ependymomas (both in-field), and 5 low-grade gliomas (2 in-field, 1 marginal, and 2 distant). CONCLUSIONS: Fractionated SRT using CK without target volume expansion appears to reduce the volume of irradiated tissue without majorly compromising local control in pediatric demarcated brain tumors. These results are hypothesis generating and should be tested and validated in prospective studies.

A web-based brain metastases segmentation and labeling platform for stereotactic radiosurgery.

PURPOSE: Stereotactic radiosurgery (SRS) has become a standard of care for patients' with brain metastases (BMs). However, the manual multiple BMs delineation can be time-consuming and could create an efficiency bottleneck in SRS workflow. There is a clinical need for automatic delineation and quantitative evaluation tools. In this study, building on our previous developed deep learning-based segmentation algorithms, we developed a web-based automated BMs segmentation and labeling platform to assist the SRS clinical workflow. METHOD: This platform was developed based on the Django framework, including a web client and a back-end server. The web client enables interactions as database access, data import, and image viewing. The server performs the segmentation and labeling tasks including: skull stripping; deep learning-based BMs segmentation; and affine registration-based BMs labeling. Additionally, the client can display BMs contours with corresponding atlas labels, and allows further postprocessing tasks including: (a) adjusting window levels; (b) displaying/hiding specific contours; (c) removing false-positive contours; (d) exporting contours as DICOM RTStruct files; etc. RESULTS: We evaluated this platform on 10 clinical cases with BMs number varied from 12-81 per case. The overall operation took about 4-5 min per patient. The segmentation accuracy was evaluated between the manual contour and automatic segmentation with several metrics. The averaged center of mass shift was 1.55 ± 0.36 mm, the Hausdorff distance was 2.98 ± 0.63 mm, the mean of surface-to-surface distance (SSD) was 1.06 ± 0.31 mm, and the standard deviation of SSD was 0.80 ± 0.16 mm. In addition, the initial averaged false-positive over union (FPoU) and false-negative rate (FNR) were 0.43 ± 0.19 and 0.15 ± 0.10 respectively. After case-specific postprocessing, the averaged FPoU and FNR were 0.19 ± 0.10 and 0.15 ± 0.10 respectively. CONCLUSION: The evaluated web-based BMs segmentation and labeling platform can substantially improve the clinical efficiency compared to manual contouring. This platform can be a useful tool for assisting SRS treatment planning and treatment follow-up.

Considerations of target surface area and the risk of radiosurgical toxicity.

OBJECTIVE: The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery. METHODS AND MATERIALS: Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively. RESULTS: In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models. CONCLUSIONS: In a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk.

Modeling Elekta VersaHD using the Varian Eclipse treatment planning system for photon beams: A single-institution experience.

The aim of this study was to report a single-institution experience and commissioning data for Elekta VersaHD linear accelerators (LINACs) for photon beams in the Eclipse treatment planning system (TPS). Two VersaHD LINACs equipped with 160-leaf collimators were commissioned. For each energy, the percent-depth-dose (PDD) curves, beam profiles, output factors, leaf transmission factors and dosimetric leaf gaps (DLGs) were acquired in accordance with the AAPM task group reports No. 45 and No. 106 and the vendor-supplied documents. The measured data were imported into Eclipse TPS to build a VersaHD beam model. The model was validated by creating treatment plans spanning over the full-spectrum of treatment sites and techniques used in our clinic. The quality assurance measurements were performed using MatriXX, ionization chamber, and radiochromic film. The DLG values were iteratively adjusted to optimize the agreement between planned and measured doses. Mobius, an independent LINAC logfile-based quality assurance tool, was also commissioned both for routine intensity-modulated radiation therapy (IMRT) QA and as a secondary check for the Eclipse VersaHD model. The Eclipse-generated VersaHD model was in excellent agreement with the measured PDD curves and beam profiles. The measured leaf transmission factors were less than 0.5% for all energies. The model validation study yielded absolute point dose agreement between ionization chamber measurements and Eclipse within ±4% for all cases. The comparison between Mobius and Eclipse, and between Mobius and ionization chamber measurements lead to absolute point dose agreement within ±5%. The corresponding 3D dose distributions evaluated with 3%global/2mm gamma criteria resulted in larger than 90% passing rates for all plans. The Eclipse TPS can model VersaHD LINACs with clinically acceptable accuracy. The model validation study and comparisons with Mobius demonstrated that the modeling of VersaHD in Eclipse necessitates further improvement to provide dosimetric accuracy on par with Varian LINACs.

Stereotactic Radiosurgery for Multiple Brain Metastases From Renal-Cell Carcinoma.

BACKGROUND: Brain metastases (BM) pose a significant problem in patients with metastatic renal-cell carcinoma (mRCC). Local and systemic therapies including stereotactic radiosurgery (SRS) are rapidly evolving, necessitating reassessments of outcomes for modern patient management. PATIENTS AND METHODS: The mRCC patients with BM treated with SRS were reviewed. Patient demographics, clinical history, and SRS treatment parameters were identified. RESULTS: Among 268 patients with mRCC treated between 2006 and 2015, 38 patients were identified with BM. A total of 243 BM were treated with SRS with 1 to 26 BMs treated per SRS session (median, 2 BMs). The median (range) BM size was 0.6 (0.2-3.1) cm and median (range) SRS treatment dose was 18 (12-24) Gy. Treated BM local control rates at 1 and 2 years were 91.8% (95% confidence interval, 85.7-95.4) and 86.1% (95% confidence interval, 77.1-91.7), respectively. BM control declined for larger tumors. Survival after 1-year was 57.5% (95% CI 40.2-71.4) for all patients. Survival was not statistically different between patients with < 5 BM versus ≥ 5 BM. Survival was prognostic based on International Metastatic Renal Cell Carcinoma Database (IMDC) risk groups in patients with < 5 BM. Two patients experienced grade 3 radiation necrosis requiring surgical intervention. CONCLUSION: SRS is effective in controlling BM in patients with mRCC. Over half of treated patients survive past a year, and no differences in survival were noted in patients with > 5 metastases. Prognostic risk categories based on systemic disease (IMDC) are predictive of survival in this BM population, with limited rates of symptomatic radiation necrosis.

Improved Survival Outcomes for Kidney Cancer Patients With Brain Metastases.

BACKGROUND: Brain metastases (BM) occur frequently in patients with metastatic kidney cancer and are a significant source of morbidity and mortality. Although historically associated with a poor prognosis, survival outcomes for patients in the modern era are incompletely characterized. In particular, outcomes after adjusting for systemic therapy administration and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) risk factors are not well-known. PATIENTS AND METHODS: A retrospective database of patients with metastatic renal cell carcinoma (RCC) treated at University of Texas Southwestern Medical Center between 2006 and 2015 was created. Data relevant to their diagnosis, treatment course, and outcomes were systematically collected. Survival was analyzed by the Kaplan-Meier method. Patients with BM were compared with patients without BM after adjusting for the timing of BM diagnosis, either prior to or during first-line systemic therapy. The impact of stratification according to IMDC risk group was assessed. RESULTS: A total of 56 (28.4%) of 268 patients with metastatic RCC were diagnosed with BM prior to or during first-line systemic therapy. Median overall survival (OS) for systemic therapy-naive patients with BM compared with matched patients without BM was 19.5 versus 28.7 months (P = .0117). When analyzed according to IMDC risk group, the median OS for patients with BM was similar for favorable- and intermediate-risk patients (not reached vs. not reached; and 29.0 vs. 36.7 months; P = .5254), and inferior for poor-risk patients (3.5 vs. 9.4 months; P = .0462). For patients developing BM while on first-line systemic therapy, survival from the time of progression did not significantly differ by presence or absence of BM (11.8 vs. 17.8 months; P = .6658). CONCLUSIONS: Survival rates for patients with BM are significantly better than historical reports. After adjusting for systemic therapy, the survival rates of patients with BM in favorable- and intermediate-risk groups were remarkably better than expected and not statistically different from patients without BM, though this represents a single institution experience, and numbers are modest.

MR-CBCT image-guided system for radiotherapy of orthotopic rat prostate tumors.

Multi-modality image-guided radiotherapy is the standard of care in contemporary cancer management; however, it is not common in preclinical settings due to both hardware and software limitations. Soft tissue lesions, such as orthotopic prostate tumors, are difficult to identify using cone beam computed tomography (CBCT) imaging alone. In this study, we characterized a research magnetic resonance (MR) scanner for preclinical studies and created a protocol for combined MR-CBCT image-guided small animal radiotherapy. Two in-house dual-modality, MR and CBCT compatible, phantoms were designed and manufactured using 3D printing technology. The phantoms were used for quality assurance tests and to facilitate end-to-end testing for combined preclinical MR and CBCT based treatment planning. MR and CBCT images of the phantoms were acquired utilizing a Varian 4.7 T scanner and XRad-225Cx irradiator, respectively. The geometry distortion was assessed by comparing MR images to phantom blueprints and CBCT. The corrected MR scans were co-registered with CBCT and subsequently used for treatment planning. The fidelity of 3D printed phantoms compared to the blueprint design yielded favorable agreement as verified with the CBCT measurements. The geometric distortion, which varied between -5% and 11% throughout the scanning volume, was substantially reduced to within 0.4% after correction. The distortion free MR images were co-registered with the corresponding CBCT images and imported into a commercial treatment planning software SmART Plan. The planning target volume (PTV) was on average 19% smaller when contoured on the corrected MR-CBCT images relative to raw images without distortion correction. An MR-CBCT based preclinical workflow was successfully designed and implemented for small animal radiotherapy. Combined MR-CBCT image-guided radiotherapy for preclinical research potentially delivers enhanced relevance to human radiotherapy for various disease sites. This novel protocol is wide-ranging and not limited to the orthotopic prostate tumor study presented in the study.

Real-Time Whole-Brain Radiation Therapy: A Single-Institution Experience.

PURPOSE: To demonstrate the feasibility of a real-time whole-brain radiation therapy (WBRT) workflow, taking advantage of contemporary radiation therapy capabilities and seeking to optimize clinical workflow for WBRT. METHODS AND MATERIALS: We developed a method incorporating the linear accelerator's on-board imaging system for patient simulation, used cone-beam computed tomography (CBCT) data for treatment planning, and delivered the first fraction of prescribed therapy, all during the patient's initial appointment. Simulation was performed in the linear accelerator vault. An acquired CBCT data set was used for scripted treatment planning protocol, providing inversely planned, automated treatment plan generation. The osseous boundaries of the brain were auto-contoured to create a target volume. Two parallel-opposed beams using field-in-field intensity modulate radiation therapy covered this target to the user-defined inferior level (C1 or C2). The method was commissioned using an anthropomorphic head phantom and verified using 100 clinically treated patients. RESULTS: Whole-brain target heterogeneity was within 95%-107% of the prescription dose, and target coverage compared favorably to standard, manually created 3-dimensional plans. For the commissioning CBCT datasets, the secondary monitor unit verification and independent 3-dimensional dose distribution comparison for computed and delivered doses were within 2% agreement relative to the scripted auto-plans. On average, time needed to complete the entire process was 35.1 ± 10.3 minutes from CBCT start to last beam delivered. CONCLUSIONS: The real-time WBRT workflow using integrated on-site imaging, planning, quality assurance, and delivery was tested and deemed clinically feasible. The design necessitates a synchronized team consisting of physician, physicist, dosimetrist, and therapists. This work serves as a proof of concept of real-time planning and delivery for other treatment sites.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.