Single versus multiple fraction stereotactic radiosurgery for medium-sized brain metastases (4-14 cc in volume): reducing or fractionating the radiosurgery dose?

Background and purpose: Stereotactic radiosurgery (SRS) of brain metastases (BM) and resection cavities is a widely used and effective treatment modality. Based on target lesion size and anatomical location, single fraction SRS (SF-SRS) or multiple fraction SRS (MF-SRS) are applied. Current clinical recommendations conditionally recommend either reduced dose SF-SRS or MF-SRS for medium-sized BM (2-2.9 cm in diameter). Despite excellent local control rates, SRS carries the risk of radionecrosis (RN). The purpose of this study was to assess the 12-months local control (LC) rate and 12-months RN rate of this specific patient population. Materials and methods: This single-center retrospective study included 54 patients with medium-sized intact BM (n=28) or resection cavities (n=30) treated with either SF-SRS or MF-SRS. Follow-up MRI was used to determine LC and RN using a modification of the "Brain Tumor Reporting and Data System" (BT-RADS) scoring system. Results: The 12-month LC rate following treatment of intact BM was 66.7% for SF-SRS and 60.0% for MF-SRS (p=1.000). For resection cavities, the 12-month LC rate was 92.9%% after SF-SRS and 46.2% after MF-SRS (p=0.013). For intact BM, RN rate was 17.6% for SF-SRS and 20.0% for MF-SRS (p=1.000). For resection cavities, RN rate was 28.6% for SF-SRS and 20.0% for MF-SRS (p=1.000). Conclusion: Patients with intact BM showed no statistically significant differences in 12-months LC and RN rate following SF-SRS or MF-SRS. In patients with resection cavities the 12-months LC rate was significantly better following SF-SRS, with no increase in the RNFS.

Simultaneous multi-region detection of GABA+ and Glx using 3D spatially resolved SLOW-editing and EPSI-readout at 7T.

GABA+ and Glx (glutamate and glutamine) are widely studied metabolites, yet the commonly used magnetic resonance spectroscopy (MRS) techniques have significant limitations, including sensitivity to B0 and B1+-inhomogeneities, limited bandwidth of MEGA-pulses, high SAR which is accentuated at 7T. To address these limitations, we propose SLOW-EPSI method, employing a large 3D MRSI coverage and achieving a high resolution down to 0.26 ml. Simulation results demonstrate the robustness of SLOW-editing for both GABA+ and Glx against B0 and B1+-inhomogeneities within the range of [-0.3, +0.3] ppm and [40 %, 250 %], respectively. Two protocols, both utilizing a 70 mm thick FOV slab, were employed to target distinct brain regions in vivo, differentiated by their orientation: transverse and tilted. Protocol 1 (n = 11) encompassed 5 locations (cortical gray matter, white matter, frontal lobe, parietal lobe, and cingulate gyrus). Protocol 2 (n = 5) involved 9 locations (cortical gray matter, white matter, frontal lobe, occipital lobe, cingulate gyrus, caudate nucleus, hippocampus, putamen, and inferior thalamus). Quantitative analysis of GABA+ and Glx was conducted in a stepwise manner. First, B1+/B1--inhomogeneities were corrected using water reference data. Next, GABA+ and Glx values were calculated employing spectral fitting. Finally, the GABA+ level for each selected region was compared to the global Glx within the same subject, generating the GABA+/Glx_global ratio. Our findings from two protocols indicate that the GABA+/Glx_global level in cortical gray matter was approximately 16 % higher than in white matter. Elevated GABA+/Glx_global levels acquired with protocol 2 were observed in specific regions such as the caudate nucleus (0.118±0.067), putamen (0.108±0.023), thalamus (0.092±0.036), and occipital cortex (0.091±0.010), when compared to the cortical gray matter (0.079±0.012). Overall, our results highlight the effectiveness of SLOW-EPSI as a robust and efficient technique for accurate measurements of GABA+ and Glx at 7T. In contrast to previous SVS and 2D-MRSI based editing sequences with which only one or a limited number of brain regions can be measured simultaneously, the method presented here measures GABA+ and Glx from any brain area and any arbitrarily shaped volume that can be flexibly selected after the examination. Quantification of GABA+ and Glx across multiple brain regions through spectral fitting is achievable with a 9-minute acquisition. Additionally, acquisition times of 18-27 min (GABA+) and 9-18 min (Glx) are required to generate 3D maps, which are constructed using Gaussian fitting and peak integration.

ASTRA: Atomic Surface Transformations for Radiotherapy Quality Assurance.

Treatment for glioblastoma, an aggressive brain tumour usually relies on radiotherapy. This involves planning how to achieve the desired radiation dose distribution, which is known as treatment planning. Treatment planning is impacted by human errors, inter-expert variability in segmenting (or outlining) the tumor target and organs-at-risk, and differences in segmentation protocols. Erroneous segmentations translate to erroneous dose distributions, and hence sub-optimal clinical outcomes. Reviewing segmentations is time-intensive, significantly reduces the efficiency of radiation oncology teams, and hence restricts timely radiotherapy interventions to limit tumor growth. Moreover, to date, radiation oncologists review and correct segmentations without information on how potential corrections might affect radiation dose distributions, leading to an ineffective and suboptimal segmentation correction workflow. In this paper, we introduce an automated deep-learning based method: atomic surface transformations for radiotherapy quality assurance (ASTRA), that predicts the potential impact of local segmentation variations on radiotherapy dose predictions, thereby serving as an effective dose-aware sensitivity map of segmentation variations. On a dataset of 100 glioblastoma patients, we show how the proposed approach enables assessment and visualization of areas of organs-at-risk being most susceptible to dose changes, providing clinicians with a dose-informed mechanism to review and correct segmentations for radiation therapy planning. These initial results suggest strong potential for employing such methods within a broader automated quality assurance system in the radiotherapy planning workflow. Code to reproduce this is available at https://github.com/amithjkamath/astraClinical Relevance: ASTRA shows promise in indicating what regions of the OARs are more likely to impact the distribution of radiation dose.

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring.

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process.

Triggered episodic vestibular syndrome and transient loss of consciousness due to a retrostyloidal vagal schwannoma: a case report.

Background: Various conditions may trigger episodic vertigo or dizziness, with positional changes being the most frequently identified condition. In this study, we describe a rare case of triggered episodic vestibular syndrome (EVS) accompanied by transient loss of consciousness (TLOC) linked to retrostyloidal vagal schwannoma. Case description: A 27-year woman with known vestibular migraine presented with a 19-month history of nausea, dysphagia, and odynophagia triggered by swallowing food and followed by recurrent TLOC. These symptoms occurred independently of her body position, resulting in a weight loss of 10 kg within 1 year and in an inability to work. An extensive cardiologic diagnostic work-up undertaken before she presented to the neurologic department was normal. On the fiberoptic endoscopic evaluation of swallowing, she showed a decreased sensitivity, a slight bulging of the right lateral pharyngeal wall, and a pathological pharyngeal squeeze maneuver without any further functional deficits. Quantitative vestibular testing revealed an intact peripheral-vestibular function, and electroencephalography was read as normal. On the brain MRI, a 16 x 15 x 12 mm lesion in the right retrostyloidal space suspicious of a vagal schwannoma was detected. Radiosurgery was preferred over surgical resection, as resection of tumors in the retrostyloid space bears the risk of intraoperative complications and may result in significant morbidity. A single radiosurgical procedure (stereotactic CyberKnife radiosurgery, 1 x 13Gy) accompanied by oral steroids was performed. On follow-up, a cessation of (pre)syncopes was noted 6 months after treatment. Only residual infrequent episodes of mild nausea were triggered by swallowing solid food remained. Brain MRI after 6 months demonstrated no progression of the lesion. In contrast, migraine headaches associated with dizziness remained frequent. Discussion: Distinguishing triggered and spontaneous EVS is important, and identifying specific triggers by structured history-taking is essential. Episodes being elicited by swallowing solid foods and accompanied by (near) TLOC should initiate a thorough search for vagal schwannoma, as symptoms are often disabling, and targeted treatment is available. In the case presented here, cessation of (pre)syncopes and significant reduction of nausea triggered by swallowing was noted with a 6-month delay, illustrating the advantages (no surgical complications) and disadvantages (delayed treatment response) of first-line radiotherapy in vagal schwannoma treatment.

Accurate prediction of isocitrate dehydrogenase -mutation status of gliomas using SLOW-editing magnetic resonance spectroscopic imaging at 7 T MR.

Background: 2-hydroxy-glutarate (2HG) is a metabolite that accumulates in isocitrate dehydrogenase (IDH)-mutated gliomas and can be detected noninvasively using MR spectroscopy. However, due to the low concentration of 2HG, established magnetic resonance spectroscopic imaging (MRSI) techniques at the low field have limitations with respect to signal-to-noise and to the spatial resolution that can be obtained within clinically acceptable measurement times. Recently a tailored editing method for 2HG detection at 7 Tesla (7 T) named SLOW-EPSI was developed. The underlying prospective study aimed to compare SLOW-EPSI to established techniques at 7 T and 3 T for IDH-mutation status determination. Methods: The applied sequences were MEGA-SVS and MEGA-CSI at both field strengths and SLOW-EPSI at 7 T only. Measurements were performed on a MAGNETOM-Terra 7 T MR-scanner in clinical mode using a Nova 1Tx32Rx head coil and on a 3 T MAGNETOM-Prisma scanner with a standard 32-channel head coil. Results: Fourteen patients with suspected glioma were enrolled. Histopathological confirmation was available in 12 patients. IDH mutation was confirmed in 9 out of 12 cases and 3 cases were characterized as IDH wildtype. SLOW-EPSI at 7 T showed the highest accuracy for IDH-status prediction (91.7% accuracy, 11 of the 12 predictions correct with 1 false negative case). At 7 T, MEGA-CSI had an accuracy of 58.3% and MEGA-SVS had an accuracy of 75%. At 3 T, MEGA-CSI showed an accuracy of 63.6% and MEGA-SVS of 33.3%. The co-edited cystathionine was detected in 2 out of 3 oligodendroglioma cases with 1p/19q codeletion. Conclusions: Depending on the pulse sequence, spectral editing can be a powerful tool for the noninvasive determination of the IDH status. SLOW-editing EPSI sequence is the preferable pulse sequence when used at 7 T for IDH-status characterization.

Therapy Resistance of Glioblastoma in Relation to the Subventricular Zone: What Is the Role of Radiotherapy?

Glioblastoma is a highly heterogeneous primary malignant brain tumor with marked inter-/intratumoral diversity and a poor prognosis. It may contain a population of neural stem cells (NSC) and glioblastoma stem cells that have the capacity for migration, self-renewal and differentiation. While both may contribute to resistance to therapy, NSCs may also play a role in brain tissue repair. The subventricular zone (SVZ) is the main reservoir of NSCs. This study investigated the impact of bilateral SVZ radiation doses on patient outcomes. We included 147 patients. SVZs were delineated and the dose administered was extracted from dose-volume histograms. Tumors were classified based on their spatial relationship to the SVZ. The dose and outcome correlations were analyzed using the Kaplan-Meier and Cox proportional hazards regression methods. Median progression-free survival (PFS) was 7 months (range: 4-11 months) and median overall survival (OS) was 14 months (range: 9-23 months). Patients with an ipsilateral SVZ who received ≥50 Gy showed significantly better PFS (8 versus 6 months; p < 0.001) and OS (16 versus 11 months; p < 0.001). Furthermore, lower doses (<32 Gy) to the contralateral SVZ were associated with improved PFS (8 versus 6 months; p = 0.030) and OS (15 versus 11 months; p = 0.001). Targeting the potential tumorigenic cells in the ipsilateral SVZ while sparing contralateral NSCs correlated with an improved outcome. Further studies should address the optimization of dose distribution with modern radiotherapy techniques for the areas surrounding infiltrated and healthy SVZs.

PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion.

BACKGROUND AND OBJECTIVE: Despite fast evolution cycles in deep learning methodologies for medical imaging in radiotherapy, auto-segmentation solutions rarely run in clinics due to the lack of open-source frameworks feasible for processing DICOM RT Structure Sets. Besides this shortage, available open-source DICOM RT Structure Set converters rely exclusively on 2D reconstruction approaches leading to pixelated contours with potentially low acceptance by healthcare professionals. PyRaDiSe, an open-source, deep learning framework independent Python package, addresses these issues by providing a framework for building auto-segmentation solutions feasible to operate directly on DICOM data. In addition, PyRaDiSe provides profound DICOM RT Structure Set conversion and processing capabilities; thus, it applies also to auto-segmentation-related tasks, such as dataset construction for deep learning model training. METHODS: The PyRaDiSe package follows a holistic approach and provides DICOM data handling, deep learning model inference, pre-processing, and post-processing functionalities. The DICOM data handling allows for highly automated and flexible handling of DICOM image series, DICOM RT Structure Sets, and DICOM registrations, including 2D-based and 3D-based conversion from and to DICOM RT Structure Sets. For deep learning model inference, extending given skeleton classes is straightforwardly achieved, allowing for employing any deep learning framework. Furthermore, a profound set of pre-processing and post-processing routines is included that incorporate partial invertibility for restoring spatial properties, such as image origin or orientation. RESULTS: The PyRaDiSe package, characterized by its flexibility and automated routines, allows for fast deployment and prototyping, reducing efforts for auto-segmentation pipeline implementation. Furthermore, while deep learning model inference is independent of the deep learning framework, it can easily be integrated into famous deep learning frameworks such as PyTorch or Tensorflow. The developed package has successfully demonstrated its capabilities in a research project at our institution for organs-at-risk segmentation in brain tumor patients. Furthermore, PyRaDiSe has shown its conversion performance for dataset construction. CONCLUSIONS: The PyRaDiSe package closes the gap between data science and clinical radiotherapy by enabling deep learning segmentation models to be easily transferred into clinical research practice. PyRaDiSe is available on https://github.com/ubern-mia/pyradise and can be installed directly from the Python Package Index using pip install pyradise.

Vestibular Schwannoma: Long-term Outcome of the Vestibular Function After Stereotactic Radiosurgery.

Objective: Evaluation at long term of the impact of the stereotactic surgery (SRS) on the vestibular function in vestibular schwannoma (VS) patients. Study design and setting: Retrospective study in a tertiary referral center. Patients: Fifty-one VS patients were included (34 females;17 males), aged from 41 to 78 years treated exclusively with SRS. Intervention: Vestibular function was assessed before SRS and with median time interval of 14 (FU1) and 25 (FU2) months after treatment. Vestibular evaluation included: history, clinical vestibular examination, videonystagmography, head impulse test (v-HIT) and cervical vestibular evoked myogenic potentials (c-VEMPS). Results: Before SRS, caloric testing (Caloric) was impaired in 77%; after treatment, in 92% (FU1) and 77% (FU2). Lateral HIT was decreased in 22% before SRS, in 39% at FU1 and FU2. C-VEMPS were absent in 50% before SRS, in 76% at FU1 and, FU2. Before SRS, no statistically significant association was found between asymptomatic and symptomatic patients with respect to the results of Caloric, v-HIT and c-VEMPS. This lack of association was also seen after SRS, at FU1 and FU2. Conclusion: Our study showed that the impairment of the vestibular function might be attributed to the VS itself as well as to the radiation of the inner ear during SRS. The lateral SSC at low frequencies and the saccular function seem to be more involved with the time.

Programmed Death-Ligand 1 Expression in Lung Cancer and Paired Brain Metastases-a Single-Center Study in 190 Patients.

Introduction: Expression of programmed death-ligand 1 (PD-L1) is the only routinely used tissue biomarker for predicting response to programmed cell death protein 1/PD-L1 inhibitors. It is to date unclear whether PD-L1 expression is preserved in brain metastases (BMs). Methods: In this single-center, retrospective study, we evaluated PD-L1 expression using the SP263 assay in consecutively resected BMs of lung carcinomas and paired primary tumors, diagnosed from 2000 to 2015, with correlation to clinicopathological and molecular tumor and patient characteristics. Results: PD-L1 tumor proportional score (TPS) could be evaluated on whole tissue slides in 191 BMs and 84 paired primary lung carcinomas. PD-L1 TPS was less than 1% in 113 of 191 (59.2%), 1% to 49% in 34 of 191 (17.8%), and greater than or equal to 50% in 44 of 191 (23.0%) BMs. TPS was concordant between BMs and paired primary lung carcinomas in most cases, with discordance regarding the clinically relevant cutoffs at 1% and 50% in 18 of 84 patients (21.4%). Four of 18 discordant cases had no shared mutations between the primary lung carcinoma and BM. Intratumoral heterogeneity, as assessed using tissue microarray cores, was only significant at the primary site (p Wilcoxon signed rank = 0.002) with higher PD-L1 TPS at the infiltration front (mean = 40.4%, interquartile range: 0%-90%). Neither TPS greater than or equal to 1% nor TPS greater than or equal to 50% nor discordance between the primary lung carcinoma and BMs had prognostic significance regarding overall survival or BM-specific overall survival. Conclusions: PD-L1 expression was mostly concordant between primary lung carcinoma and its BM and between resections of BM and stereotactic biopsies, mirrored by tissue microarray cores. Differences in PD-L1 TPS existed primarily in cases with TPS greater than 10%, for which also human assessment tends to be most error prone.