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Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensive imaging follow-up. Our objective in this study was to establish an algorithm by which to automate the volumetric measurement of vestibular schwannoma (VS) using a series of parametric MR images following radiosurgery. Based on a sample of 861 consecutive patients who underwent Gamma Knife radiosurgery (GKRS) between 1993 and 2008, the proposed end-to-end deep-learning scheme with automated pre-processing pipeline was applied to a series of 1290 MR examinations (T1W+C, and T2W parametric MR images). All of which were performed under consistent imaging acquisition protocols. The relative volume difference (RVD) between AI-based volumetric measurements and clinical measurements performed by expert radiologists were + 1.74%, − 0.31%, − 0.44%, − 0.19%, − 0.01%, and + 0.26% at each follow-up time point, regardless of the state of the tumor (progressed, pseudo-progressed, or regressed). This study outlines an approach to the evaluation of treatment responses via novel volumetric measurement algorithm, and can be used longitudinally following GKRS for VS. The proposed deep learning AI scheme is applicable to longitudinal follow-up assessments following a variety of therapeutic interventions.

The extraction of brain tumor tissues in 3D Brain Magnetic Resonance Imaging (MRI) plays an important role in diagnosis before the gamma knife radiosurgery (GKRS). In this article, the post-contrast T1 whole-brain MRI images had been collected by Taipei Veterans General Hospital (TVGH) and stored in DICOM format (dated from 1999 to 2018). The proposed method starts with the active contour model to get the region of interest (ROI) automatically and enhance the image contrast. The segmentation models are trained by MRI images with tumors to avoid imbalanced data problem under model construction. In order to achieve this objective, a two-step ensemble approach is used to establish such diagnosis, first, classify whether there is any tumor in the image, and second, segment the intracranial metastatic tumors by ensemble neural networks based on 2D U-Net architecture. The ensemble for classification and segmentation simultaneously also improves segmentation accuracy. The result of classification achieves a F1-measure of 75.64 % , while the result of segmentation achieves an IoU of 84.83 % and a DICE score of 86.21 % . Significantly reduce the time for manual labeling from 30 min to 18 s per patient.

Abstract BACKGROUND Atypical and anaplastic meningiomas have reduced progression-free/overall survival (PFS/OS) compared to benign meningiomas. Stereotactic radiosurgery (SRS) for atypical meningiomas (AMs) and anaplastic meningiomas (malignant meningiomas, MMs) has not been adequately described. OBJECTIVE To define clinical/radiographic outcomes for patients undergoing SRS for AM/MMs. METHODS An international, multicenter, retrospective cohort study was performed to define clinical/imaging outcomes for patients receiving SRS for AM/MMs. Tumor progression was assessed with response assessment in neuro-oncology (RANO) criteria. Factors associated with PFS/OS were assessed using Kaplan-Meier analysis and a Cox proportional hazards model. RESULTS A total of 271 patients received SRS for AMs (n = 233, 85.9%) or MMs (n = 38, 14.0%). Single-fraction SRS was most commonly employed (n = 264, 97.4%) with a mean target dose of 14.8 Gy. SRS was used as adjuvant treatment (n = 85, 31.4%), salvage therapy (n = 182, 67.2%), or primary therapy (1.5%). The 5-yr PFS/OS rate was 33.6% and 77.0%, respectively. Increasing age (hazard ratio (HR) = 1.01, P < .05) and a Ki-67 index > 15% (HR = 1.66, P < .03) negatively correlated with PFS. MMs (HR = 3.21, P < .05), increased age (HR = 1.04, P = .04), and reduced KPS (HR = 0.95, P = .04) were associated with shortened OS. Adjuvant versus salvage SRS did not impact PFS/OS. A shortened interval between surgery and SRS improved PFS for AMs (HR = 0.99, P = .02) on subgroup analysis. Radiation necrosis occurred in 34 (12.5%) patients. Five-year rates of repeat surgery/radiation were 33.8% and 60.4%, respectively. CONCLUSION AM/MMs remain challenging tumors to treat. Elevated proliferative indices are associated with tumor recurrence, while MMs have worse survival. SRS can control AM/MMs in the short term, but the 5-yr PFS rates are low, underscoring the need for improved treatment options for these patients. Graphical Abstract Graphical Abstract

PurposeGamma Knife radiosurgery (GKRS) is a non-invasive procedure for the treatment of brain metastases. This study sought to determine whether radiomic features of brain metastases derived from pre-GKRS magnetic resonance imaging (MRI) could be used in conjunction with clinical variables to predict the effectiveness of GKRS in achieving local tumor control.MethodsWe retrospectively analyzed 161 patients with non-small cell lung cancer (576 brain metastases) who underwent GKRS for brain metastases. The database included clinical data and pre-GKRS MRI. Brain metastases were demarcated by experienced neurosurgeons, and radiomic features of each brain metastasis were extracted. Consensus clustering was used for feature selection. Cox proportional hazards models and cause-specific proportional hazards models were used to correlate clinical variables and radiomic features with local control of brain metastases after GKRS.ResultsMultivariate Cox proportional hazards model revealed that higher zone percentage (hazard ratio, HR 0.712; P = .022) was independently associated with superior local tumor control. Similarly, multivariate cause-specific proportional hazards model revealed that higher zone percentage (HR 0.699; P = .014) was independently associated with superior local tumor control.ConclusionsThe zone percentage of brain metastases, a radiomic feature derived from pre-GKRS contrast-enhanced T1-weighted MRIs, was found to be an independent prognostic factor of local tumor control following GKRS in patients with non-small cell lung cancer and brain metastases. Radiomic features indicate the biological basis and characteristics of tumors and could potentially be used as surrogate biomarkers for predicting tumor prognosis following GKRS.

Background The pathophysiology of the reversible cerebral vasoconstriction syndrome (RCVS) remains enigmatic and the role of glymphatics in RCVS pathophysiology has not been evaluated. We aimed to investigate RCVS glymphatic dynamics and its clinical correlates. Methods We prospectively evaluated the glymphatic function in RCVS patients, with RCVS subjects and healthy controls (HCs) recruited between August 2020 and November 2023, by calculating diffusion-tensor imaging along the perivascular space (DTI-ALPS) index under a 3-T MRI. Clinical and vascular (transcranial color-coded duplex sonography) investigations were conducted in RCVS subjects. RCVS participants were separated into acute (≤ 30 days) and remission (≥ 90 days) groups by disease onset to MRI interval. The time-trend, acute stage and longitudinal analyses of the DTI-ALPS index were conducted. Correlations between DTI-ALPS index and vascular and clinical parameters were performed. Bonferroni correction was applied to vascular investigations ( q  = 0.05/11). Results A total of 138 RCVS patients (mean age, 46.8 years ± 11.8; 128 women) and 42 HCs (mean age, 46.0 years ± 4.5; 35 women) were evaluated. Acute RCVS demonstrated lower DTI-ALPS index than HCs ( p  < 0.001) and remission RCVS ( p  < 0.001). A continuously increasing DTI-ALPS trend after disease onset was demonstrated. The DTI-ALPS was lower when the internal carotid arteries resistance index and six-item Headache Impact test scores were higher. In contrast, during 50–100 days after disease onset, the DTI-ALPS index was higher when the middle cerebral artery flow velocity was higher. Conclusions Glymphatic function in patients with RCVS exhibited a unique dynamic evolution that was temporally coupled to different vascular indices and headache-related disabilities along the disease course. These findings may provide novel insights into the complex interactions between glymphatic transport, vasomotor control and pain modulation.

The aim of the study was to investigate whether morphology (i.e. compact/diffuse) of brain arteriovenous malformations (bAVMs) correlates with the incidence of hemorrhagic events in patients receiving Stereotactic Radiosurgery (SRS) for unruptured bAVMs. This retrospective study included 262 adult patients with unruptured bAVMs who underwent upfront SRS. Hemorrhagic events were defined as evidence of blood on CT or MRI. The morphology of bAVMs was evaluated using automated segmentation which calculated the proportion of vessel, brain tissue, and cerebrospinal fluid in bAVMs on T2-weighted MRI. Compactness index, defined as the ratio of vessel to brain tissue, categorized bAVMs into compact and diffuse types based on the optimal cutoff. Cox proportional hazard model was used to identify the independent factors for post-SRS hemorrhage. The median clinical follow-ups was 62.1 months. Post-SRS hemorrhage occurred in 13 (5.0%) patients and one of them had two bleeds, resulting in an annual bleeding rate of 0.8%. Multivariable analysis revealed bAVM morphology (compact versus diffuse), bAVM volume, and prescribed margin dose were significant predictors. The post-SRS hemorrhage rate increased with larger bAVM volume only among the diffuse nidi (1.7 versus 14.9 versus 30.6 hemorrhage per 1000 person-years in bAVM volume < 20 cm 3 versus 20–40 cm 3 versus > 40 cm 3 ; p = 0.022). The significantly higher post-SRS hemorrhage rate of Spetzler-Martin grade IV–V compared with grade I–III bAVMs (20.0 versus 3.3 hemorrhages per 1000 person-years; p = 0.001) mainly originated from the diffuse bAVMs rather than the compact subgroup (30.9 versus 4.8 hemorrhages per 1000 person-years; p = 0.035). Compact and smaller bAVMs, with higher prescribed margin dose harbor lower risks of post-SRS hemorrhage. The post-SRS hemorrhage rate exceeded 2.2% annually within the diffuse and large (> 40 cm 3 ) bAVMs and the diffuse Spetzler-Martin IV–V bAVMs. These findings may help guide patient selection of SRS for the unruptured bAVMs.

ObjectiveTo investigate the outcomes of cerebrospinal fluid (CSF) diversion in lung cancer patients with leptomeningeal carcinomatosis (LMC).MethodsA retrospective review of consecutive lung cancer patients with LMC suffering from increased intracranial pressure (IICP) and hydrocephalus between February 2017 and February 2020. We evaluated the survival benefit of CSF diversion surgery and assessed the outcomes of treatments administered post-LMC in terms of overall survival and shunt-related complications.ResultsThe study cohort included 50 patients (median age: 59 years). Ventricular peritoneal (VP) shunts were placed in 33 patients, and lumbar peritoneal (LP) shunts were placed in 7 patients. Programmable shunts were placed in 36 patients. Shunt adjustment was performed in 19 patients. Kaplan-Meier analysis revealed that shunt placement increased overall survival from 1.95 months to 6.21 months (p = 0.0012) and increased Karnofsky Performance Scores (KPS) from 60 to 70. Univariate analysis revealed no difference between VP or LP shunts in terms of survival. No differences in post-shunt systemic treatments (tyrosine kinase inhibitors (TKIs) or systemic treatments) were observed in overall survival. Shunt-related complications were noted in 7 patients, including shunt obstruction (n = 4), infection (n = 1), and over-drainage (n = 2).ConclusionCSF diversion (VP or LP shunt) appears to be an effective and safe treatment for lung cancer patients with LMC and hydrocephalus. Programmable shunts should be considered for complex cases, which commonly require pressure adjustments as the disease progresses.

Background Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding brain tissue can lead to perifocal edema within a few months after the procedure. Volumetric assessment of perifocal edema is crucial for therapy planning and monitoring. Post-radiosurgery changes in perifocal edema, appearing as hyper-dense areas in magnetic resonance T2-weighted (T2w) images, are clearly identifiable; however, physicians lack tools to segment and quantify the volume of these T2w hyper-dense areas. This has hindered not only the quantification of severity but also research on edema growth and case differentiation. Methods In this study, we trained a Mask Region-based Convolutional Neural Network (Mask R-CNN) to replace manual pre-processing in designating regions of interest. We also applied transfer learning to the DeepMedic deep learning model to facilitate the automatic segmentation and quantification of brain edema regions in images. The resulting quantitative findings were used to explore the effects of GKRS treatment on brain edema caused by meningioma. Results We studied 21 patients with meningiomas who had undergone GKRS treatment based on 154 regularly tracked T2w scans. From this group, we selected 130 scans for random assignment to a training set (80 scans), validation set (30 scans), and test set (20 scans). The actual range of the edema in the T2w images was labeled manually by a clinical radiologist to serve as the gold standard in supervised learning. The trained model was tasked with segmenting the test set for comparison with the manual segmentation results. The average Dice similarity coefficient in these comparisons was 84.7%. Conclusions The proposed scheme for the automated segmentation and quantification of brain edema post-radiosurgery demonstrated excellent results, suggesting its applicability to the development of predictive models. Trial registration Not applicable.

Background Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriovenous malformations (AVMs), this procedure can lead to radiation-induced changes (RICs) in the surrounding brain tissue. Volumetric assessment of RICs is crucial for therapy planning and monitoring. RICs that appear as hyper-dense areas in magnetic resonance T2-weighted (T2w) images are clearly identifiable; however, physicians lack tools for the segmentation and quantification of these areas. This paper presents an algorithm to calculate the volume of RICs in patients with AVMs following SRS. The algorithm could be used to predict the course of RICs and facilitate clinical management. Methods We trained a Mask Region-based Convolutional Neural Network (Mask R-CNN) as an alternative to manual pre-processing in designating regions of interest. We also applied transfer learning to the DeepMedic deep learning model to facilitate the automatic segmentation and quantification of AVM edema regions in T2w images. Results The resulting quantitative findings were used to explore the effects of SRS treatment among 28 patients with unruptured AVMs based on 139 regularly tracked T2w scans. The actual range of RICs in the T2w images was labeled manually by a clinical radiologist to serve as the gold standard in supervised learning. The trained model was tasked with segmenting the test set for comparison with the manual segmentation results. The average Dice similarity coefficient in these comparisons was 71.8%. Conclusions The proposed segmentation algorithm achieved results on par with conventional manual calculations in determining the volume of RICs, which were shown to peak at the end of the first year after SRS and then gradually decrease. These findings have the potential to enhance clinical decision-making. Trial registration Not applicable.

Background This paper presents a deep learning model for the automated segmentation of cerebral cavernous malformations (CCMs). Methods The model was trained using treatment planning data from 199 Gamma Knife (GK) exams, comprising 171 cases with a single CCM and 28 cases with multiple CCMs. The training data included initial MRI images with target CCM regions manually annotated by neurosurgeons. For the extraction of data related to the brain parenchyma, we employed a mask region-based convolutional neural network (Mask R-CNN). Subsequently, this data was processed using a 3D convolutional neural network known as DeepMedic. Results The efficacy of the brain parenchyma extraction model was demonstrated via five-fold cross-validation, resulting in an average Dice similarity coefficient of 0.956 ± 0.002. The segmentation models used for CCMs achieved average Dice similarity coefficients of 0.741 ± 0.028 based solely on T2W images. The Dice similarity coefficients for the segmentation of CCMs types were as follows: Zabramski Classification type I (0.743), type II (0.742), and type III (0.740). We also developed a user-friendly graphical user interface to facilitate the use of these models in clinical analysis. Conclusions This paper presents a deep learning model for the automated segmentation of CCMs, demonstrating sufficient performance across various Zabramski classifications. Trial registration not applicable.