Search

Background: Immune checkpoint inhibitors (ICIs) are an emerging tool in the treatment of brain metastases (BMs), Stereotactic radiosurgery (SRS), traditionally used for BMs, elicits an immune brain response and can act synergistically with ICIs. We aim to investigate the efficacy of ICI administered with SRS and determine the impact of timing on BM response., Methods: A systematical search was performed to identify potential studies concerning BMs managed with SRS alone or with SRS + ICI with relative timing administration (ICI concurrent with SRS, ICI nonconcurrent with SRS, SRS before ICI, SRS after ICI). The overall survival (OS), 12-month OS, local progression-free survival (LPFS), 12-month local brain control (LBC), distant progression-free survival (DPFS), 12-month distant brain control (DBC), and adverse events (intracranial hemorrhage, radionecrosis) were analyzed using the random-effects model., Results: A total of 16 retrospective studies with 1356 BM patients were included. Compared to nonconcurrent therapy, concurrent therapy revealed a significantly longer OS (HR= 1.43; p  = 0.008) and 12-months LBC (HR = 1.91; p  = 0.04), a similar 12-months DBC (HR = 1.12; p  = 0.547) and higher complication rate ( R  = 0.77; p  = 0.346). Concurrent therapy leads to a significantly higher OS compared to ICI before SRS (HR = 2.55; p  = 0.0003)., Conclusion: The combination of SRS with ICI improves patients' clinical and radiological outcomes. The effectiveness of the combination is subject to the identification of an optimal therapeutic window.

To determine the efficacy of stereotactic radiosurgery (SRS) in treating patients with brain metastases (BMs), a network meta-analysis (NMA) of randomized controlled trials (RCTs) and a direct comparison of cohort studies were performed. Relevant literature regarding the effectiveness of SRS alone and in combination with whole-brain radiotherapy (WBRT) and surgery was retrieved using systematic database searches up to April 2019. The patterns of overall survival (OS), one-year OS, progression-free survival (PFS), one-year local brain control (LBC), one-year distant brain control (DBC), neurological death (ND), and complication rate were analyzed. A total of 18 RCTs and 37 cohorts were included in the meta-analysis. Our data revealed that SRS carried a better OS than SRS+WBRT (p = 0.048) and WBRT (p = 0.041). Also, SRS+WBRT demonstrated a significantly improved PFS, LBC, and DBC compared to WBRT alone and SRS alone. Finally, SRS achieved the same LBC as high as surgery, but intracranial relapse occurred considerably more frequently in the absence of WBRT. However, there were not any significant differences in ND and toxicities between SRS and other groups. Therefore, SRS alone may be a better alternative since increased patient survival may outweigh the increased risk of brain tumor recurrence associated with it., Competing Interests: Declaration of Competing Interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Purpose: Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images., Methods: PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020. Studies that evaluated the performance of DL-based models in the segmentation, and/or classification and/or grading of HNCs and/or brain tumors were included. Selected studies for each analysis were statistically evaluated based on the diagnostic performance metrics., Results: The search results retrieved 1,664 related studies, of which 30 studies were eligible for meta-analysis. The overall performance of DL models for the complete tumor in terms of the pooled Dice score, sensitivity, and specificity was 0.8965 (95% confidence interval (95% CI): 0.76-0.9994), 0.9132 (95% CI: 0.71-0.994) and 0.9164 (95% CI: 0.78-1.00), respectively. The DL methods achieved the highest performance for classifying three types of glioma, meningioma, and pituitary tumors with overall accuracies of 96.01%, 99.73%, and 96.58%, respectively. Stratification of glioma tumors by high and low grading revealed overall accuracies of 94.32% and 94.23% for the DL methods, respectively., Conclusion: Based on the obtained results, we can acknowledge the significant ability of DL methods in the mentioned applications. Poor reporting in these studies challenges the analysis process, so it is recommended that future studies report comprehensive results based on different metrics.