Unveiling the hidden role of extracellular vesicles in brain metastases: a comprehensive review.

Background: Extracellular vesicles (EVs) are small, transparent vesicles that can be found in various biological fluids and are derived from the amplification of cell membranes. Recent studies have increasingly demonstrated that EVs play a crucial regulatory role in tumorigenesis and development, including the progression of metastatic tumors in distant organs. Brain metastases (BMs) are highly prevalent in patients with lung cancer, breast cancer, and melanoma, and patients often experience serious complications and are often associated with a poor prognosis. The immune microenvironment of brain metastases was different from that of the primary tumor. Nevertheless, the existing review on the role and therapeutic potential of EVs in immune microenvironment of BMs is relatively limited. Main body: This review provides a comprehensive analysis of the published research literature, summarizing the vital role of EVs in BMs. Studies have demonstrated that EVs participate in the regulation of the BMs immune microenvironment, exemplified by their ability to modify the permeability of the blood-brain barrier, change immune cell infiltration, and activate associated cells for promoting tumor cell survival and proliferation. Furthermore, EVs have the potential to serve as biomarkers for disease surveillance and prediction of BMs. Conclusion: Overall, EVs play a key role in the regulation of the immune microenvironment of brain metastasis and are expected to make advances in immunotherapy and disease diagnosis. Future studies will help reveal the specific mechanisms of EVs in brain metastases and use them as new therapeutic strategies.

Extraneural metastatic ependymoma: distant metastasis to the pleura, lungs, lymph nodes and bone.

Ependymomas are neuroepithelial tumours arising from ependymal cells surrounding the cerebral ventricles that rarely metastasise to extraneural structures. This spread has been reported to occur to the lungs, lymph nodes, liver and bone. We describe the case of a patient with recurrent CNS WHO grade 3 ependymoma with extraneural metastatic disease. He was treated with multiple surgical resections, radiation therapy and salvage chemotherapy for his extraneural metastasis to the lungs, bone, pleural space and lymph nodes.

MR-based radiomics predictive modelling of EGFR mutation and HER2 overexpression in metastatic brain adenocarcinoma: a two-centre study.

OBJECTIVES: Magnetic resonance (MR)-based radiomics features of brain metastases are utilised to predict epidermal growth factor receptor (EGFR) mutation and human epidermal growth factor receptor 2 (HER2) overexpression in adenocarcinoma, with the aim to identify the most predictive MR sequence. METHODS: A retrospective inclusion of 268 individuals with brain metastases from adenocarcinoma across two institutions was conducted. Utilising T1-weighted imaging (T1 contrast-enhanced [T1-CE]) and T2 fluid-attenuated inversion recovery (T2-FLAIR) sequences, 1,409 radiomics features were extracted. These sequences were randomly divided into training and test sets at a 7:3 ratio. The selection of relevant features was done using the least absolute shrinkage selection operator, and the training cohort's support vector classifier model was employed to generate the predictive model. The performance of the radiomics features was evaluated using a separate test set. RESULTS: For contrast-enhanced T1-CE cohorts, the radiomics features based on 19 selected characteristics exhibited excellent discrimination. No significant differences in age, sex, and time to metastasis were observed between the groups with EGFR mutations or HER2 + and those with wild-type EGFR or HER2 (p > 0.05). Radiomics feature analysis for T1-CE revealed an area under the curve (AUC) of 0.98, classification accuracy of 0.93, sensitivity of 0.92, and specificity of 0.93 in the training cohort. In the test set, the AUC was 0.82. The 19 radiomics features for the T2-FLAIR sequence showed AUCs of 0.86 in the training set and 0.70 in the test set. CONCLUSIONS: This study developed a T1-CE signature that could serve as a non-invasive adjunctive tool to determine the presence of EGFR mutations and HER2 + status in adenocarcinoma, aiding in the direction of treatment plans. CLINICAL RELEVANCE STATEMENT: We propose radiomics features based on T1-CE brain MR sequences that are both evidence-based and non-invasive. These can be employed to guide clinical treatment planning in patients with brain metastases from adenocarcinoma.

Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study.

OBJECTIVE: A high number of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis who have not had surgery often have a negative outlook. Radiotherapy remains a most common and effective method. Nomograms were developed to forecast the cancer-specific survival (CSS) and overall survival (OS) in NSCLC individuals with nonoperative brain metastases who underwent radiotherapy. METHODS: Information was gathered from the Surveillance, Epidemiology, and End Results (SEER) database about patients diagnosed with NSCLC who had brain metastases not suitable for surgery. Nomograms were created and tested using multivariate Cox regression models to forecast CSS and OS at intervals of 1, 2, and 3 years. RESULTS: The research involved 3413 individuals diagnosed with NSCLC brain metastases who had undergone radiotherapy but had not experienced surgery. These participants were randomly divided into two categories. The analysis revealed that gender, age, ethnicity, marital status, tumor location, tumor laterality, tumor grade, histology, T stage, N stage, chemotherapy, tumor size, lung metastasis, bone metastasis, and liver metastasis were significant independent predictors for OS and CSS. The C-index for the training set for predicting OS was .709 (95% CI, .697-.721), and for the validation set, it was .705 (95% CI, .686-.723), respectively. The C-index for predicting CSS was .710 (95% CI, .697-.722) in the training set and .703 (95% CI, .684-.722) in the validation set, respectively. The nomograms model, as suggested by the impressive C-index, exhibits outstanding differentiation ability. Moreover, the ROC and calibration curves reveal its commendable precision and distinguishing potential. CONCLUSIONS: For the first time, highly accurate and reliable nomograms were developed to predict OS and CSS in NSCLC patients with non-surgical brain metastases, who have undergone radiotherapy treatment. The nomograms may assist in tailoring counseling strategies and choosing the most effective treatment method.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Simultaneous targeting of peripheral and brain tumors with a therapeutic nanoparticle to disrupt metabolic adaptability at both sites.

Brain metastasis of advanced breast cancer often results in deleterious consequences. Metastases to the brain lead to significant challenges in treatment options, as the blood-brain barrier (BBB) prevents conventional therapy. Thus, we hypothesized that creation of a nanoparticle (NP) that distributes to both primary tumor site and across the BBB for secondary brain tumor can be extremely beneficial. Here, we report a simple targeting strategy to attack both the primary breast and secondary brain tumors utilizing a single NP platform. The nature of these mitochondrion-targeted, BBB-penetrating NPs allow for simultaneous targeting and drug delivery to the hyperpolarized mitochondrial membrane of the extracranial primary tumor site in addition to tumors at the brain. By utilizing a combination of such dual anatomical distributing NPs loaded with therapeutics, we demonstrate a proof-of-concept idea to combat the increased metabolic plasticity of brain metastases by lowering two major energy sources, oxidative phosphorylation (OXPHOS) and glycolysis. By utilizing complementary studies and genomic analyses, we demonstrate the utility of a chemotherapeutic prodrug to decrease OXPHOS and glycolysis by pairing with a NP loaded with pyruvate dehydrogenase kinase 1 inhibitor. Decreasing glycolysis aims to combat the metabolic flexibility of both primary and secondary tumors for therapeutic outcome. We also address the in vivo safety parameters by addressing peripheral neuropathy and neurobehavior outcomes. Our results also demonstrate that this combination therapeutic approach utilizes mitochondrial genome targeting strategy to overcome DNA repair-based chemoresistance mechanisms.

Cancer-stromal cell interactions in breast cancer brain metastases induce glycocalyx-mediated resistance to HER2-targeting therapies.

Brain metastatic breast cancer is particularly lethal largely due to therapeutic resistance. Almost half of the patients with metastatic HER2-positive breast cancer develop brain metastases, representing a major clinical challenge. We previously described that cancer-associated fibroblasts are an important source of resistance in primary tumors. Here, we report that breast cancer brain metastasis stromal cell interactions in 3D cocultures induce therapeutic resistance to HER2-targeting agents, particularly to the small molecule inhibitor of HER2/EGFR neratinib. We investigated the underlying mechanisms using a synthetic Notch reporter system enabling the sorting of cancer cells that directly interact with stromal cells. We identified mucins and bulky glycoprotein synthesis as top-up-regulated genes and pathways by comparing the gene expression and chromatin profiles of stroma-contact and no-contact cancer cells before and after neratinib treatment. Glycoprotein gene signatures were also enriched in human brain metastases compared to primary tumors. We confirmed increased glycocalyx surrounding cocultures by immunofluorescence and showed that mucinase treatment increased sensitivity to neratinib by enabling a more efficient inhibition of EGFR/HER2 signaling in cancer cells. Overexpression of truncated MUC1 lacking the intracellular domain as a model of increased glycocalyx-induced resistance to neratinib both in cell culture and in experimental brain metastases in immunodeficient mice. Our results highlight the importance of glycoproteins as a resistance mechanism to HER2-targeting therapies in breast cancer brain metastases.

Response of treatment-naive brain metastases to stereotactic radiosurgery.

With improvements in survival for patients with metastatic cancer, long-term local control of brain metastases has become an increasingly important clinical priority. While consensus guidelines recommend surgery followed by stereotactic radiosurgery (SRS) for lesions >3 cm, smaller lesions (≤3 cm) treated with SRS alone elicit variable responses. To determine factors influencing this variable response to SRS, we analyzed outcomes of brain metastases ≤3 cm diameter in patients with no prior systemic therapy treated with frame-based single-fraction SRS. Following SRS, 259 out of 1733 (15%) treated lesions demonstrated MRI findings concerning for local treatment failure (LTF), of which 202 /1733 (12%) demonstrated LTF and 54/1733 (3%) had an adverse radiation effect. Multivariate analysis demonstrated tumor size (>1.5 cm) and melanoma histology were associated with higher LTF rates. Our results demonstrate that brain metastases ≤3 cm are not uniformly responsive to SRS and suggest that prospective studies to evaluate the effect of SRS alone or in combination with surgery on brain metastases ≤3 cm matched by tumor size and histology are warranted. These studies will help establish multi-disciplinary treatment guidelines that improve local control while minimizing radiation necrosis during treatment of brain metastasis ≤3 cm.

Narrow interval dual phase 18F-FDG PET/CT: A practical approach for distinguishing tumor recurrence from radiation necrosis in brain metastasis.

Purpose of our research is to demonstrate efficacy of narrow interval dual phase [18F]-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) imaging in distinguishing tumor recurrence (TR) from radiation necrosis (RN) in patients treated for brain metastases. 35 consecutive patients (22 female, 13 male) with various cancer subtypes, lesion size > 1.0 cm3, and suspected recurrence on brain magnetic resonance imaging (MRI) underwent narrow interval dual phase FDG-PET/CT (30 and 90 min after tracer injection). Clinical outcome was determined via sequential MRIs or pathology reports. Maximum standard uptake value (SUVmax) of lesion (L), gray matter (GM), and white matter (WM) was measured on early (1) and delayed (2) imaging. Analyzed variables include % change, late phase, and early phase for L uptake, L/GM uptake, and L/WM uptake. Statistical analysis (P < .01), receiver operator characteristic (ROC) curve and area under curve (AUC) cutoff values were obtained. Change in L/GM ratio of > -2% was 95% sensitive, 91% specific, and 93% accurate (P < .001, AUC = 0.99) in distinguishing TR from RN. Change in SUVmax of lesion alone was the second-best indicator (P < .001, AUC = 0.94) with an ROC cutoff > 30.5% yielding 86% sensitivity, 83% specificity, and 84% accuracy. Other variables (L alone or L/GM ratios in early or late phase, all L/WM ratios) were significantly less accurate. Utilizing narrow interval dual phase FDG-PET/CT in patients with brain metastasis treated with radiation therapy provides a practical approach to distinguish TR from RN. Narrow time interval allows for better patient comfort, greater efficiency of PET/CT scanner, and lower disruption of workflow.

Prognostic Implications of Small Cell Lung Cancer Transcriptional Subtyping for CNS Metastases.

PURPOSE: Small cell lung cancer (SCLC) often metastasizes to the brain and has poor prognosis. SCLC subtypes distinguished by expressing transcriptional factors ASCL1 or NEUROD1 have been identified. This study investigates the impact of transcription factor-defined SCLC subtype on incidence and outcomes of brain metastases (BMs). METHODS: Patients with SCLC with ASCL1 (A) and NEUROD1 (N) immunohistochemical expression status were identified and classified: (1) A+/N-, (2) A+/N+, (3) A-/N+, and (4) A-/N-. Cumulative incidence competing risk analyses were used to assess incidence of CNS progression. Cox proportional hazards models were used for multivariable analyses of overall survival (OS) and CNS progression-free survival (CNS-PFS). RESULTS: Of 164 patients, most were either A+/N- or A+/N+ (n = 62, n = 63, respectively). BMs were present at diagnosis in 24 patients (15%). Among them, the 12-month cumulative incidence of subsequent CNS progression was numerically highest for A+/N- (50% [95% CI, 10.5 to 74.7]; P = .47). Among those BM-free at diagnosis, the 12-month cumulative incidence of CNS progression was numerically the highest for A+/N- (16% [95% CI, 7.5 to 27.9]) and A-/N+ (9.1% [95% CI, 0.0 to 34.8]; P = .20). Both subtypes, A+/N- and A-/N+, had worse OS compared with A+/N+ (A+/N-: hazard ratio [HR], 1.62 [95% CI, 1.01 to 2.51]; P < .05; A-/N+: HR, 3.02 [95% CI, 1.35 to 6.76]; P = .007). Excellent response rates (28, 65% CR/PR) across subtypes were seen in patients who had CNS-directed radiotherapy versus systemic therapy alone (9, 36% CR/PR). CONCLUSION: To our knowledge, this report is the first to investigate CNS-specific outcomes based on transcription factor subtypes in patients with SCLC. BM-free patients at diagnosis with A+/N- or A-/N+ subtypes had worse outcomes compared with those with transcriptional factor coexpression. Further investigation into the mechanisms and implications of SCLC subtyping on CNS-specific outcomes is warranted to ultimately guide personalized care.

SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases.

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival.

Brain radiotherapy and anlotinib control primary cardiac angiosarcoma with metastases: A case report.

RATIONALE: Primary cardiac angiosarcoma (PCA) is a rare and fatal disease with a poor prognosis. Whether the survival of PCA patients can be prolonged with additional treatment following complete surgical excision is controversial. PATIENT CONCERNS: In this case study, a 52-year-old male complained of chest tightness and pain for 7 days before admission into the hospital. Subsequently, he revisited the hospital because of dizziness and headache. DIAGNOSES: Initially, the patient was diagnosed with PCA in the right atrium by thoracic computed tomography (CT). Palliative resection identified brain, lung, and liver metastases. INTERVENTION: The patient accepted multimodal combination therapy, including first-line chemotherapy and then second-line anlotinib concurrent with brain radiotherapy and immunotherapy. OUTCOME: Although anlotinib combined with brain radiotherapy controlled the growth of intracranial lesions, progression-free survival (PFS) was only 5 months, and the overall survival (OS) was only 12 months. LESSON: The treatment for metastatic PCA needs an in-depth exploration.

Comparative analysis of the spatial distribution of brain metastases across several primary cancers using machine learning and deep learning models.

OBJECTIVE: Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable. METHODS: To test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume. RESULTS: Our findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature. CONCLUSIONS: In summary, our results support accurate multiclass ML classification regarding brain metastases distribution.

Improved outcomes for triple negative breast cancer brain metastases patients after stereotactic radiosurgery and new systemic approaches.

PURPOSE: Although ongoing studies are assessing the efficacy of new systemic therapies for patients with triple negative breast cancer (TNBC), the overwhelming majority have excluded patients with brain metastases (BM). Therefore, we aim to characterize systemic therapies and outcomes in a cohort of patients with TNBC and BM managed with stereotactic radiosurgery (SRS) and delineate predictors of increased survival. METHODS: We used our prospective patient registry to evaluate data from 2012 to 2023. We included patients who received SRS for TNBC-BM. A competing risk analysis was conducted to assess local and distant control. RESULTS: Forty-three patients with 262 tumors were included. The median overall survival (OS) was 16 months (95% CI 13-19 months). Predictors of increased OS after initial SRS include Breast GPA score > 1 (p < 0.001) and use of immunotherapy such as pembrolizumab (p = 0.011). The median time on immunotherapy was 8 months (IQR 4.4, 11.2). The median time to new CNS lesions after the first SRS treatment was 17 months (95% CI 12-22). The cumulative rate for development of new CNS metastases after initial SRS at 6 months, 1 year, and 2 years was 23%, 40%, and 70%, respectively. Thirty patients (70%) underwent multiple SRS treatments, with a median time of 5 months (95% CI 0.59-9.4 months) for the appearance of new CNS metastases after second SRS treatment. CONCLUSIONS: TNBC patients with BM can achieve longer survival than might have been previously anticipated with median survival now surpassing one year. The use of immunotherapy is associated with increased median OS of 23 months.

Survival prediction in patients with gynecological cancer irradiated for brain metastases.

BACKGROUND AND PURPOSE: This large population-based, retrospective, single-center study aimed to identify prognostic factors in patients with brain metastases (BM) from gynecological cancers. MATERIAL AND METHODS: One hundred and forty four patients with BM from gynecological cancer treated with radiotherapy (RT) were identified. Primary cancer diagnosis, age, performance status, number of BM, presence of extracranial disease, and type of BM treatment were assessed. Overall survival (OS) was calculated using the Kaplan-Meier method and the Cox proportional hazards regression model was used for multivariable analysis. A prognostic index (PI) was developed based on scores from independent predictors of OS. RESULTS: Median OS for the entire study population was 6.2 months. Forty per cent of patients died within 3 months after start of RT. Primary cancer with the origin in cervix or vulva (p = 0.001),  Eastern Cooperative Oncology Group (ECOG) 3-4 (p < 0.001), and the presence of extracranial disease (p = 0.001) were associated with significantly shorter OS. The developed PI based on these factors, categorized patients into three risk groups with a median OS of 13.5, 4.0, and 2.4 months for the good, intermediate, and poor prognosis group, respectively. INTERPRETATION: Patients with BM from gynecological cancers carry a poor prognosis. We identified prognostic factors and developed a scoring tool to select patients with better or worse prognosis. Patients in the high-risk group have a particular poor prognosis, and omission of RT could be considered.

Personalized identification and characterization of genome-wide gene expression differences between patient-matched intracranial and extracranial melanoma metastasis pairs.

Melanoma is the most serious type of skin cancer that frequently spreads to other organs of the human body. Especially melanoma metastases to the brain (intracranial metastases) are hard to treat and a major cause of death of melanoma patients. Little is known about molecular alterations and altered mechanisms that distinguish intra- from extracranial melanoma metastases. So far, almost all existing studies compared intracranial metastases from one set of patients to extracranial metastases of an another set of melanoma patients. This neglects the important facts that each melanoma is highly individual and that intra- and extracranial melanoma metastases from the same patient are more similar to each other than to melanoma metastases from other patients in the same organ. To overcome this, we compared the gene expression profiles of 16 intracranial metastases to their corresponding 21 patient-matched extracranial metastases in a personalized way using a three-state Hidden Markov Model (HMM) to identify altered genes for each individual metastasis pair. This enabled three major findings by considering the predicted gene expression alterations across all patients: (i) most frequently altered pathways include cytokine-receptor interaction, calcium signaling, ECM-receptor interaction, cAMP signaling, Jak-STAT and PI3K/Akt signaling, (ii) immune-relevant signaling pathway genes were downregulated in intracranial metastases, and (iii) intracranial metastases were associated with a brain-like phenotype gene expression program. Further, the integration of all differentially expressed genes across the patient-matched melanoma metastasis pairs led to a set of 103 genes that were consistently down- or up-regulated in at least 11 of the 16 of the patients. This set of genes contained many genes involved in the regulation of immune responses, cell growth, cellular signaling and transport processes. An analysis of these genes in the TCGA melanoma cohort showed that the expression behavior of 11 genes was significantly associated with survival. Moreover, a comparison of the 103 genes to three closely related melanoma metastasis studies revealed a core set of eight genes that were consistently down- or upregulated in intra- compared to extracranial metastases in at least two of the three related studies (down: CILP, DPT, FGF7, LAMP3, MEOX2, TMEM119; up: GLDN, PMP2) including FGF7 that was also significantly associated with survival. Our findings contribute to a better characterization of genes and pathways that distinguish intra- from extracranial melanoma metastasis and provide important hints for future experimental studies to identify potential targets for new therapeutic approaches.

Prognosis versus Actual Outcomes in Stereotactic Radiosurgery of Brain Metastases: Reliability of Common Prognostic Parameters and Indices.

This study aims to evaluate the clinical outcome of stereotactic radiosurgery as the sole treatment for brain metastases and to assess prognostic factors influencing survival. A total of 108 consecutive patients with 213 metastases were retrospectively analyzed. Treatment was determined with close-meshed MRI follow-up. Various prognostic factors were assessed, and several prognostic indices were compared regarding their reliability to estimate overall survival. Median overall survival was 15 months; one-year overall survival was 50.5%. Both one- and two-year local controls were 90.9%. The rate of new metastases after SRS was 49.1%. Multivariate analysis of prognostic factors revealed that the presence of extracranial metastases, male sex, lower KPI, and progressive extracranial disease were significant risk factors for decreased survival. Of all evaluated prognostic indices, the Basic Score for Brain Metastases (BSBMs) showed the best correlation with overall survival. A substantial survival advantage was found for female patients after SRS when compared to male patients (18 versus 9 months, p = 0.003). SRS of brain metastasis is a safe and effective treatment option when frequent monitoring for new metastases with MRI is performed. Common prognostic scores lack reliable estimation of survival times. Female sex should be considered as an additional independent positive prognostic factor influencing survival.