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.

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.

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.

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.

Osimertinib plus local treatment for brain metastases versus osimertinib alone in patients with EGFR-Mutant Non-Small Cell Lung Cancer.

OBJECTIVES: Osimertinib is a standard treatment for patients with EGFR-mutant non-small cell lung cancer (NSCLC) and is highly effective for brain metastases (BMs). However, it is unclear whether local treatment (LT) for BMs prior to osimertinib administration improves survival in EGFR-mutant NSCLC. We aimed to reveal the survival benefit of upfront local treatment (LT) for BMs in patients treated with osimertinib. MATERIALS AND METHODS: This multicenter retrospective study included consecutive patients with EGFR mutation (19del or L858R)-positive NSCLC who had BMs before osimertinib initiation between August 2018 and October 2021. We compared overall survival (OS) and central nervous system progression-free survival (CNS-PFS) between patients who received upfront LT for BMs (the upfront LT group), and patients who received osimertinib only (the osimertinib-alone group). Inverse-probability treatment weighting (IPTW) analysis was performed to adjust for potential confounding factors. RESULTS: Of the 121 patients analyzed, 57 and 64 patients had 19del and L858R, respectively. Forty-five and 76 patients were included in the upfront LT group and the osimertinib-alone groups, respectively. IPTW-adjusted Kaplan-Meier curves showed that the OS of the upfront LT group was significantly longer than that of the osimertinib-alone group (median, 95 % confidence intervals [95 %CI]: Not reached [NR], NR-NR vs. 31.2, 21.7-33.2; p = 0.021). The hazard ratio (HR) for OS and CNS-PFS was 0.37 (95 %CI, 0.16-0.87) and 0.36 (95 %CI, 0.15-0.87), respectively. CONCLUSIONS: The OS and CNS-PFS of patients who received upfront LT for BMs followed by osimertinib were significantly longer than those of patients who received osimertinib alone. Upfront LT for BMs may be beneficial in patients with EGFR-mutant NSCLC treated with osimertinib.

[A Case of Five Years Recurrence-Free Survival after Successful Multidisciplinary Treatment for Simultaneous Brain Metastasis and Heterochronic Small Intestinal Metastasis from Lung Cancer].

The patient was a 54-year-old male at the time of initial examination. He was aware of numbness and weakness in the left hemisphere of his body and came to see the hospital. He was diagnosed with brain metastasis of lung cancer and started treatment(cT2N0M1[Brain]). He underwent gamma knife for the head lesion and nivolumab for the lung lesion. The patient's lesions shrank with the success of the medical treatment, but recurred with small intestinal metastasis. He underwent a partial resection of the small intestine and was treated again with nivolumab, which resulted in a complete response. He is currently alive without recurrence. We have experienced a very rare case of recurrence-free survival after treatment for brain metastasis and small intestinal metastasis of lung cancer.

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.

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.

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.

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.

Genetic and Immunological Characterization of Brain Metastases from Solid Cancers.

BACKGROUND/AIM: Brain metastasis, a leading cause of cancer death, is a clinical challenge. Recently, genetic characterization of brain metastatic lesions based on next generation sequencing-based advanced technologies, such as single-cell RNA sequencing, has been performed to develop novel efficient therapies. The present study aimed to investigate brain-metastasis-specific biomarkers as well as relevant prognostic factors. PATIENTS AND METHODS: The genetic profiles and expression levels of immune response-associated genes and 820 cancer-associated genes were compared between primary cancer lesions and metastatic cancer lesions obtained from nine cancer patients at the Shizuoka Cancer Center. Cytokine and chemokine marker genes were analyzed via quantitative PCR. T-cell receptor (TCR) repertoire profiling was performed for the same patients. For survival analysis, survival data of 52 cancer patients with brain metastases were utilized. RESULTS: Comparison of driver mutation profiling between primary and metastatic lesions revealed shared core mutations in both lesions and a few new mutations in metastatic lesions. A high tumor mutation burden (TMB) was detected in metastatic lesions. Volcano plot analysis revealed specific features of the metastatic tumor microenvironment, such as cancer signaling promotion and immune suppression due to decreased immune cell infiltration. Survival analysis revealed that three genes, the TREML2 gene, the BTLA gene on activated microglia and the CERS2 gene on metastatic tumor, were potent prognostic factors. CONCLUSION: High TMB in metastatic lesions indicates potential benefit from immune checkpoint inhibitor usage for brain metastasis and TREML2 and BTLA are factors associated with poor prognosis. Activated microglia may be novel targets for the treatment of brain metastasis.

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.

Antipsychotic Zuclopenthixol Inhibits Melanoma Growth and Brain Metastasis by Inducing Apoptosis and Cell Cycle Arrest.

BACKGROUND: The incidence of melanoma brain metastasis (MBM) is high and significantly compromises patient survival and quality of life. Effective treatment of MBM is made difficult by the blood-brain barrier (BBB), since it restricts the entry of drugs into the brain. Certain anti-psychotic drugs able to cross the BBB have demonstrated efficacy in suppressing brain metastasis in preclinical studies. However, the activity of zuclopenthixol against MBM is not yet clear. METHODS: Cell viability assays were employed to investigate the potential of zuclopenthixol in the treatment of MBM. Subsequently, the mechanism of action was investigated by RNA-sequencing (RNAseq), flow cytometry-based cell cycle and apoptosis assays, protein expression analysis, and autophagy flux detection. Additionally, the efficacy of zuclopenthixol against tumor growth was investigated in vivo, including MBM models. RESULTS: Zuclopenthixol inhibited the proliferation of various melanoma cell lines at minimal doses by causing cell cycle arrest in the G0/G1 phase and mitochondrial-mediated intrinsic apoptosis. Zuclopenthixol also induced cytoprotective autophagy, and inhibition of autophagy enhanced the anti-melanoma effects of zuclopenthixol. Furthermore, zuclopenthixol inhibited the growth of human melanoma tumors in nude mice, as well as the growth of intracranial metastases in a mouse model of MBM. CONCLUSIONS: These results demonstrate that zuclopenthixol has significant potential as an effective therapeutic agent for MBM.

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis.

BACKGROUND: Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS. METHODS: PubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17. RESULTS: A total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84-0.93) and 0.87 (95% CI: 0.81-0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5-11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08-0.19), and the diagnostic odds ratio was 56 (95% CI: 25-125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76-0.98) and 0.80 (95% CI: 0.53-0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6-14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02-0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606). CONCLUSION: ML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.

Development and validation of an MRI-Based nomogram to predict the effectiveness of immunotherapy for brain metastasis in patients with non-small cell lung cancer.

Introduction: The variability and unpredictability of immune checkpoint inhibitors (ICIs) in treating brain metastases (BMs) in patients with advanced non-small cell lung cancer (NSCLC) is the main concern. We assessed the utility of novel imaging biomarkers (radiomics) for discerning patients with NSCLC and BMs who would derive advantages from ICIs treatment. Methods: Data clinical outcomes and pretreatment magnetic resonance images (MRI) were collected on patients with NSCLC with BMs treated with ICIs between June 2019 and June 2022 and divided into training and test sets. Metastatic brain lesions were contoured using ITK-SNAP software, and 3748 radiomic features capturing both intra- and peritumoral texture patterns were extracted. A clinical radiomic nomogram (CRN) was built to evaluate intracranial progression-free survival, progression-free survival, and overall survival. The prognostic value of the CRN was assessed by Kaplan-Meier survival analysis and log-rank tests. Results: In the study, a total of 174 patients were included, and 122 and 52 were allocated to the training and validation sets correspondingly. The intratumoral radiomic signature, peritumoral radiomic signature, clinical signature, and CRN predicted intracranial objective response rate. Kaplan-Meier analyses showed a significantly longer intracranial progression-free survival in the low-CRN group than in the high-CRN group (p < 0.001). The CRN was also significantly associated with progression-free survival (p < 0.001) but not overall survival. Discussion: Radiomics biomarkers from pretreatment MRI images were predictive of intracranial response. Pretreatment radiomics may allow the early prediction of benefits.