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Background and Objectives: Ependymoma is commonly classified as World Health Organization grade 2 with the anaplastic variant categorized as grade 3. Incomplete resection or anaplastic features can result in unfavorable outcomes. Stereotactic radiosurgery (SRS) provides a minimally invasive approach for recurrent ependymomas. Our study investigates the efficacy and safety of SRS for grade 2 and 3 ependymomas in pediatric and adult populations., Methods: We conducted a retrospective analysis on 34 patients with 75 ependymomas after CyberKnife SRS between 1998 and 2023. Fourteen were pediatric (3-18 years), and 20 were adult (19-75 years) patients. The median age was 21 years, and the median tumor volume was 0.64 cc. The median single-fraction equivalent dose was 16.6 Gy, with SRS administered at 77% of the median isodose line., Results: After a median follow-up of 42.7 months (range: 3.8-438.3), 22.7% of ependymomas progressed. The 5-year local tumor control rate was 78.1%, varying between 59.6% and 90.2% for children and adults, with grade 2 at 85.9% compared with 58.5% for grade 3 tumors. The 5-year overall survival rate was 73.6%, notably higher in adults (94.7%) than in children (41%), and 100% for grade 2 but decreased to 35.9% for grade 3 patients. The 5-year progression-free survival rate was 68.5%, with 78.3% and 49.2% for adults and children, respectively, and a favorable 88.8% for grade 2, contrasting with 32.6% for grade 3 patients. Symptom improvement was observed in 85.3% of patients. Adverse radiation effects occurred in 21.4% of pediatric patients., Conclusion: Our study supports SRS as a viable modality for pediatric and adult patients with grade 2 and 3 ependymomas. Despite lower local tumor control in pediatric and grade 3 cases, integrating SRS holds promise for improved outcomes. Emphasizing careful patient selection, personalized treatment planning, and long-term follow-up is crucial for optimal neurosurgical outcomes., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Congress of Neurological Surgeons.)

Background and Objectives: Stereotactic radiosurgery (SRS) has been an attractive treatment modality for both cranial and spinal hemangioblastomas, especially for multiple lesions commonly associated with von Hippel-Lindau (VHL) disease. This study aims to provide the largest long-term analysis of treatment efficacy and adverse effects of SRS for cranial and spinal hemangioblastomas at a single institution., Methods: We evaluated the clinical and radiological outcomes of patients with hemangioblastomas treated with CyberKnife SRS at our institute from 1998 to 2022. The follow-up data were available for 135 hemangioblastomas in 35 patients. Twenty-eight patients had 123 hemangioblastomas associated with VHL, and 7 had 12 sporadic hemangioblastomas. The median age was 36 years, and the median tumor volume accounted for 0.4 cc. The SRS was administered with the median single-fraction equivalent dose of 18 Gy to the 77% median isodose line., Results: At a median follow-up of 57 months (range: 3-260), only 20 (16.2%) of the VHL-associated and 1 (8.3%) sporadic hemangioblastomas progressed. The 5-year local tumor control rate was 91.3% for all hemangioblastomas, 91.7% among the sporadic lesions, and 92.9% in patients with VHL. SRS improved tumor-associated symptoms of 98 (74.8%) of 131 symptomatic hemangioblastomas, including headache, neck pain, dizziness, visual disturbances, dysesthesia, ataxia, motor impairment, seizures, and dysphagia. Two patients developed radiation necrosis (5.7%), and 1 of them required surgical resection., Conclusion: SRS is a safe and effective treatment option for patients with hemangioblastomas in critical locations, such as the brainstem, cervicomedullary junction, and spinal cord, and in patients with multiple hemangioblastomas associated with VHL disease., (Copyright © Congress of Neurological Surgeons 2023. All rights reserved.)

Purpose: To develop an angular correction methodology and characterize a high-resolution complementary metal-oxide-semiconductor (CMOS) array for patient specific quality assurance on a robotic arm linear accelerator., Methods: Beam path files from the treatment planning software (TPS) were used to calculate the angle of radiation beam with respect to the detector plane. Beams from multiple discrete angles were delivered to the CMOS detector array and an angular dependency look up table (LUT) was created. The LUT was then used to correct for the angular dependency of the detector. An iso-centric 5 mm fixed cone, non iso-centric multi-target fixed cone, 10 mm Iris and a multi-leaf collimator (MLC) based collimated plan were delivered to the phantom and compared to the TPS with and without angular correction applied. Additionally, the CMOS array was compared to gafchromic film and a diode array., Results: Large errors of up to 30% were observed for oblique angles. When angular correction was applied, the gamma passing rate increased from 99.2% to 100% (average gamma value decreased from 0.29 to 0.14) for the 5-mm iso-centric cone plan. Similarly, the passing rate increased from 84.0% to 100% for the Iris plan and from 49.98% to 98.4% for the MLC plan when angular correction was applied. For the multi-target plan, applying angular correction improved the gamma passing rate from 94% to 99.6%. The 5 mm iso-centric fixed cone plan was also delivered to film, and the gamma passing rate was 91.3% when using gafchromic film as the reference dataset, whereas the diode array provided insufficient sampling for this plan., Conclusion: A methodology of calculating the beam angle based on the beam path files was developed and validated. The array was demonstrated to be superior to other quality assurance tools because of its sub-millimeter spatial resolution and immediate read out of the results., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

In 2019, our institution became the second in the world to go live with GammaPod (Xcision Medical Systems, LLC, Columbia, MD), a device dedicated for stereotactic radiation therapy of breast cancer, with breast immobilization, real-time imaging, and highly-conformal dosimetry. At our institution, GammaPod is used for 5-fraction adjuvant partial breast irradiation, single-fraction tumor cavity boost before whole-breast irradiation, single-fraction preoperative radiation, and (in poor surgical candidates), single-fraction definitive radiation. Here, we describe our workflow, observed procedure step times, and homegrown techniques for improved efficiency in our institutional experience of 93 patients treated between 2019 and 2021., (Published by Elsevier Inc.)

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., (© 2022 Institute of Physics and Engineering in Medicine.)

Purpose: Our purpose was to evaluate cosmetic changes after 5-fraction adjuvant stereotactic partial breast irradiation (S-PBI)., Methods and Materials: Seventy-five women with in situ or invasive breast cancer stage 0, I, or II, with tumor size ≤3 cm, were enrolled after lumpectomy in a phase 1 dose escalation trial of S-PBI into cohorts receiving 30, 32.5, 35, 37.5, or 40 Gy in 5 fractions. Before S-PBI, 3 to 4 gold fiducial markers were placed in the lumpectomy cavity for tracking with the Synchrony respiratory tracking system. S-PBI was delivered with a CyberKnife robotic radiosurgery system. Patients and physicians evaluated global cosmesis using the Harvard Breast Cosmesis Scale. Eight independent panelists evaluated digital photography for global cosmesis and 10 subdomains at baseline and follow-up. McNemar tests were used to evaluate change in cosmesis, graded as excellent/good or fair/poor, from baseline to year 3. Wilcoxon signed rank tests were used to evaluate change in subdomains. Cohen's kappa (κ) statistic was used to estimate interobserver agreement (IOA) between raters, and Fleiss' κ was used to estimate IOA between panelists., Results: Median cosmetic follow-up was 5, 5, 5, 4, and 3 years for the 30, 32.5, 35, 37.5, and 40 Gy cohorts. Most patients reported excellent/good cosmesis at both baseline (86.3%) and year 3 (89.8%). No dose cohort had significantly worsened cosmesis by year 3 on McNemar analysis. No cosmetic subdomain had significant worsening by year 3. IOA was fair for patient-physician (κ = 0.300, P < .001), patient-panel (κ = 0.295, P < .001), physician-panel (κ = 0.256, P < .001), and individual panelists (Fleiss κ = 0.327, P < .001)., Conclusions: Dose escalation of S-PBI from 30 to 40 Gy in 5 fractions for early stage breast cancer was not associated with a detectable change in cosmesis by year 3. S-PBI is a promising modality for treatment of early stage breast cancer., (Published by Elsevier Inc.)

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., (© 2021 American Association of Physicists in Medicine.)

Background and Purpose: Involuntary motion due to swallowing cause inaccurate dose delivery during larynx radiotherapy, a deviation that may be particularly problematic during stereotactic body radiation therapy (SBRT). The goal of this study was to develop a motion management solution for larynx SBRT using surface imaging., Material and Methods: Ten patients were recently treated on a phase II study of larynx SBRT on a LINAC equipped with a surface guidance system. A small region of the immobilization mask was manually cut open to allow surface tracking. Pre-treatment and intra-fractional CBCTs were acquired to verify internal anatomy. Patients were verbally instructed not to swallow during treatment. During treatment delivery, beam hold was initiated by the Motion Management Interface if surface motion exceeded a patient-specific threshold. Patient motion was recorded in log files and analyzed. We also performed phantom studies to assess the theoretical impact of gating on dose delivery., Results: The frequency (6.5 ± 5.2 times per fraction) and duration (3.9 ± 2.5 seconds per swallow) of swallowing varied both between patients and fractions. The magnitude of each swallow showed mean peak amplitude at 5.8 ± 3.8 mm above baseline, mostly in the longitudinal direction. Beam duty cycle was 95.0% ± 7.0% (absolute range: 76-100%), with inefficiency most prominent in the early fractions. The 95th percentile residual motion was reduced from 3.4 mm to 2.3 mm with both verbal instruction and gating. Phantom studies confirmed dose delivery accuracy represented by gamma pass rate was improved by 5% using this approach., Conclusions: Laryngeal motion management using surface imaging is feasible and efficacious. Uncontrolled movement of the larynx was not uncommon during treatment, with gating reducing potential for unplanned dose deviations. Additional research is needed to determine the clinical benefit with this system., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: This study reports predictive dosimetric and physiologic factors for fat necrosis after stereotactic-partial breast irradiation (S-PBI)., Methods and Materials: Seventy-five patients with ductal carcinoma-in situ or invasive nonlobular epithelial histologies stage 0, I, or II, with tumor size <3 cm were enrolled in a dose-escalation, phase I S-PBI trial between January 2011 and July 2015. Fat necrosis was evaluated clinically at each follow-up. Treatment data were extracted from the Multiplan Treatment Planning System (Cyberknife, Accuray). Univariate and stepwise logistic regression analyses were conducted to identify factors associated with palpable fat necrosis., Results: With a median follow-up of 61 months (range: 4.3-99.5 months), 11 patients experienced palpable fat necrosis, 5 cases of which were painful. The median time to development of fat necrosis was 12.7 months (range, 3-42 months). On univariate analyses, higher V32.5-47.5 Gy (P < .05) and larger breast volume (P < .01) were predictive of any fat necrosis; higher V35-50 Gy (P < .05), receiving 2 treatments on consecutive days (P = .02), and higher Dmax (P = .01) were predictive of painful fat necrosis. On multivariate analyses, breast volume larger than 1063 cm 3 remained a predictive factor for any fat necrosis; receiving 2 treatments on consecutive days and higher V45 Gy were predictive of painful fat necrosis. Breast laterality, planning target volume (PTV), race, body mass index, diabetic status, and tobacco or drug use were not significantly associated with fat necrosis on univariate analysis., Conclusions: Early-stage breast cancer patients treated with breast conserving surgery and S-PBI in our study had a fat necrosis rate comparable to other accelerated partial breast irradiation modalities, but S-PBI is less invasive. To reduce risk of painful fat necrosis, we recommend not delivering fractions on consecutive days; limiting V42.5 < 50 cm 3 , V45 < 20 cm 3 , V47.5 < 1 cm 3 , Dmax ≤ 48 Gy and PTV < 100 cm 3 when feasible; and counseling patients about the increased risk for fat necrosis when constraints are not met and for those with breast volume >1000 cm 3 ., (Published by Elsevier Inc.)

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., (© 2020 American Association of Physicists in Medicine.)

Introduction: Stereotactic body radiation therapy (SBRT) was found effective in treating laryngeal cancer with only five treatment fractions by a recent clinical trial (NCT01984502, ClinicalTrials.gov). Nevertheless, this trial used the Cyberknife system, which is not widely accessible enough to benefit all patients affected by laryngeal cancer. Our study investigates the feasibility of larynx SBRT treatment planning on a conventional gantry-based LINAC and compares its plan quality with that from the Cyberknife., Materials & Methods: Ten larynx SBRT cases were originally treated by Cyberknife using fixed cones in our institution, with plans created and optimized using the Monte-Carlo algorithm in the MultiPlan treatment planning system. These cases were retrospectively planned in the Eclipse planning system for a LINAC with the same prescription dose. We used volumetric modulated arc therapy (VMAT) for larynx SBRT planning in Eclipse and incorporated non-coplanar arcs to approach the Cyberknife's large solid angle delivery space. We used both anisotropic analytical algorithm (AAA) and Acuros XB (AXB) algorithm for dose calculation and compared their accuracy by measurements on an in-house larynx phantom. We compared the LINAC VMAT plans (VMAT-AAA and VMAT-AXB) with the original Cyberknife plans using dosimetric endpoints such as the conformity index, gradient indices (R50, R20), OAR maximum/mean doses, and the monitor units., Results: Phantom measurement showed that both the AAA and the AXB algorithms provided adequate dose calculation accuracy (94.7% gamma pass rate on 2%/2 mm criteria for AAA vs. 97.3% for AXB), though AXB provided better accuracy in the air cavity. The LINAC-based VMAT plans achieved similar dosimetric endpoints as the Cyberknife planning, and all plans met the larynx SBRT dosimetric constraints. Cyberknife plans achieved an average conformity index of 1.13, compared to 1.20 of VMAT-AXB and 1.19 of VMAT-AAA. The VMAT plans spared the thyroid gland better with average Dmean of 2.4 Gy (VMAT-AXB) and 2.7 Gy (VMAT-AAA), as compared to 4.3 Gy for Cyberknife plans. The VMAT-AAA plans had a slightly lower contralateral arytenoid Dmax (average: 15.2 Gy) than Cyberknife plans (average: 17.9 Gy) with statistical significance, while the contralateral arytenoid Dmax was similar between VMAT-AXB and Cyberknife plans with no statistically significant difference. Cyberknife plans offered slightly better R50 (average: 5.0) than VMAT-AXB (5.9) and VMAT-AAA (5.7) plans. The VMAT plans substantially reduced the plan MUs to less than 1/3 of the Cyberknife plans, and the differences were statistically significant. The other metrics were similar between VMAT and Cyberknife plans with no statistically significant differences., Conclusions: Gantry-based LINACs can achieve similar plan quality to Cyberknife systems. Treatment outcome with both methods remains to be investigated.

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.

Background: Recently developed stereotactic partial breast irradiation (S-PBI) allows delivery of a high biologically potent dose to the target while sparing adjacent critical organs and normal tissue. With S-PBI tumoricidal doses, accurate and precise dose delivery is critical to achieve high treatment quality. This study is to investigate both rigid and non-rigid components of target geometric error and their corresponding margins in S-PBI and identify correlated clinical factors., Methods: Forty-three early-stage breast cancer patients with implanted gold fiducial markers were enrolled in the study. Fiducial positions recorded on the orthogonal kV images on a Cyberknife system during treatment were used to estimate intra-fraction errors and composite errors (including intra-fraction errors and residual errors after patient setup). Both rigid and non-rigid components of intra-fraction and composite errors were analyzed and used to estimate rigid and non-rigid margins, respectively. Univariate and multivariate linear regressions were conducted to evaluate correlations between clinical factors and errors., Results: For the study group, the intra-fraction rigid and non-rigid errors are 2.0 ± 0.6 mm and 0.3 ± 0.2 mm, respectively. The composite rigid and non-rigid errors are 2.3 ± 0.5 mm and 1.3 ± 0.8 mm, respectively. The rigid margins in the left-right, anterior-posterior, and superior-inferior directions are estimated as 2.1, 2.4, and 2.3 mm, respectively. The estimated non-rigid margin, assumed to be isotropic, is 1.7 mm. The outer breast quadrants are more susceptible to composite errors occurrence than the inner breast quadrants. The target to chest wall distance is the clinical factor correlated with target geometric errors., Conclusions: This is the first comprehensive analysis of breast target geometric rigid and non-rigid errors in S-PBI. Upon the estimation, the non-rigid margin is comparable to rigid margin, and therefore should be included in planning target volume as it cannot be accounted for by the Cyberknife system. Treatment margins selection also need to consider the impact of relevant clinical factor.

Objective: Evolutionary stochastic global optimization algorithms are widely used in large-scale, nonconvex problems. However, enhancing the search efficiency and repeatability of these techniques often requires well-customized approaches. This study investigates one such approach., Methods: We use particle swarm optimization (PSO) algorithm to solve a 4D radiation therapy (RT) inverse planning problem, where the key idea is to use respiratory motion as an additional degree of freedom in lung cancer RT. The primary goal is to administer a lethal dose to the tumor target while sparing surrounding healthy tissue. Our optimization iteratively adjusts radiation fluence-weights for all beam apertures across all respiratory phases. We implement three PSO-based approaches: conventionally used unconstrained, hard-constrained, and our proposed virtual search. As proof of concept, five lung cancer patient cases are optimized over ten runs using each PSO approach. For comparison, a dynamically penalized likelihood (DPL) algorithm-a popular RT optimization technique is also implemented and used., Results: The proposed technique significantly improves the robustness to random initialization while requiring fewer iteration cycles to converge across all cases. DPL manages to find the global optimum in 2 out of 5 RT cases over significantly more iterations., Conclusion: The proposed virtual search approach boosts the swarm search efficiency, and consequently, improves the optimization convergence rate and robustness for PSO., Significance: RT planning is a large-scale, nonconvex optimization problem, where finding optimal solutions in a clinically practical time is critical. Our proposed approach can potentially improve the optimization efficiency in similar time-sensitive problems.

Purpose: We have initiated a multi-institutional phase I trial of 5-fraction stereotactic body radiotherapy (SBRT) for Stage III-IVa laryngeal cancer. We conducted this pilot dosimetric study to confirm potential utility of online adaptive replanning to preserve treatment quality., Methods: We evaluated ten cases: five patients enrolled onto the current trial and five patients enrolled onto a separate phase I SBRT trial for early-stage glottic larynx cancer. Baseline SBRT treatment plans were generated per protocol. Daily cone-beam CT (CBCT) or diagnostic CT images were acquired prior to each treatment fraction. Simulation CT images and target volumes were deformably registered to daily volumetric images, the original SBRT plan was copied to the deformed images and contours, delivered dose distributions were re-calculated on the deformed CT images. All of these were performed on a commercial treatment planning system. In-house software was developed to propagate the delivered dose distribution back to reference CT images using the deformation information exported from the treatment planning system. Dosimetric differences were evaluated via dose-volume histograms., Results: We could evaluate dose within 10 minutes in all cases. Prescribed coverage to gross tumor volume (GTV) and clinical target volume (CTV) was uniformly preserved; however, intended prescription dose coverage of planning treatment volume (PTV) was lost in 53% of daily treatments (mean: 93.9%, range: 83.9-97.9%). Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases., Conclusions: Although GTV and CTV SBRT dose coverage is preserved with in-room three-dimensional image guidance, PTV coverage can vary significantly from intended plans and dose to critical structures may exceed tolerances. Online adaptive treatment re-planning is potentially necessary and clinically applicable to fully preserve treatment quality. Confirmatory trial accrual and analysis remains ongoing., (© 2016 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98  ±  0.01, an NMI of 0.97  ±  0.01, an SSIM of 0.999  ±  0.001, an HD of 2.2  ±  0.8 mm, an MSSD of 0.1  ±  0.1 mm, and an SDSSD of 0.3  ±  0.1 mm. The validation on the BRATS data resulted in a DC of 0.89  ±  0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86  ±  0.09, an NMI of 0.80  ±  0.11, an SSIM of 0.999  ±  0.001, an HD of 8.8  ±  12.6 mm, an MSSD of 1.5  ±  3.2 mm, and an SDSSD of 1.8  ±  3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

The aim of this study is to compare the recent Eclipse Acuros XB (AXB) dose calculation engine with the Pinnacle collapsed cone convolution/superposition (CCC) dose calculation algorithm and the Eclipse anisotropic analytic algorithm (AAA) for stereotactic ablative radiotherapy (SAbR) treatment planning of thoracic spinal (T-spine) metastases using IMRT and VMAT delivery techniques. The three commissioned dose engines (CCC, AAA, and AXB) were validated with ion chamber and EBT2 film measurements utilizing a heterogeneous slab-geometry water phantom and an anthropomorphic phantom. Step-and-shoot IMRT and VMAT treatment plans were developed and optimized for eight patients in Pinnacle, following our institutional SAbR protocol for spinal metastases. The CCC algorithm, with heterogeneity corrections, was used for dose calculations. These plans were then exported to Eclipse and recalculated using the AAA and AXB dose calculation algorithms. Various dosimetric parameters calculated with CCC and AAA were compared to that of the AXB calculations. In regions receiving above 50% of prescription dose, the calculated CCC mean dose is 3.1%-4.1% higher than that of AXB calculations for IMRT plans and 2.8%-3.5% higher for VMAT plans, while the calculated AAA mean dose is 1.5%-2.4% lower for IMRT and 1.2%-1.6% lower for VMAT. Statistically significant differences (p < 0.05) were observed for most GTV and PTV indices between the CCC and AXB calculations for IMRT and VMAT, while differences between the AAA and AXB calculations were not statistically significant. For T-spine SAbR treatment planning, the CCC calculations give a statistically significant overestimation of target dose compared to AXB. AAA underestimates target dose with no statistical significance compared to AXB. Further study is needed to determine the clinical impact of these findings.

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. [ABSTRACT FROM AUTHOR]

Background: Recently developed stereotactic partial breast irradiation (S-PBI) allows delivery of a high biologically potent dose to the target while sparing adjacent critical organs and normal tissue. With S-PBI tumoricidal doses, accurate and precise dose delivery is critical to achieve high treatment quality. This study is to investigate both rigid and non-rigid components of target geometric error and their corresponding margins in S-PBI and identify correlated clinical factors.Methods: Forty-three early-stage breast cancer patients with implanted gold fiducial markers were enrolled in the study. Fiducial positions recorded on the orthogonal kV images on a Cyberknife system during treatment were used to estimate intra-fraction errors and composite errors (including intra-fraction errors and residual errors after patient setup). Both rigid and non-rigid components of intra-fraction and composite errors were analyzed and used to estimate rigid and non-rigid margins, respectively. Univariate and multivariate linear regressions were conducted to evaluate correlations between clinical factors and errors.Results: For the study group, the intra-fraction rigid and non-rigid errors are 2.0 ± 0.6 mm and 0.3 ± 0.2 mm, respectively. The composite rigid and non-rigid errors are 2.3 ± 0.5 mm and 1.3 ± 0.8 mm, respectively. The rigid margins in the left-right, anterior-posterior, and superior-inferior directions are estimated as 2.1, 2.4, and 2.3 mm, respectively. The estimated non-rigid margin, assumed to be isotropic, is 1.7 mm. The outer breast quadrants are more susceptible to composite errors occurrence than the inner breast quadrants. The target to chest wall distance is the clinical factor correlated with target geometric errors.Conclusions: This is the first comprehensive analysis of breast target geometric rigid and non-rigid errors in S-PBI. Upon the estimation, the non-rigid margin is comparable to rigid margin, and therefore should be included in planning target volume as it cannot be accounted for by the Cyberknife system. Treatment margins selection also need to consider the impact of relevant clinical factor. [ABSTRACT FROM AUTHOR]