Brain metastases are a common and severe complication potentially leading to death in patients with metastatic melanoma. Immunotherapy and targeted therapy have significantly improved progression-free survival (PFS) and overall survival (OS) in patients with advanced melanoma. Few studies focus on patients with central nervous system (CNS) metastases, and these patients are often excluded and have a poor prognosis. It has been suggested that immunotherapy could reduce the incidence of brain metastases. We tested this hypothesis in a retrospective bicentric study. We performed a retrospective, bicentric descriptive analysis on a cohort of 293 patients treated for metastatic melanoma between May 2014 and October 2017 (Toulouse, N = 202; Limoges, N = 91). Patients with brain metastasis at diagnosis were excluded from the analysis. Patients were separated into two groups according to the first line of treatment: immunotherapy [immune checkpoint inhibitor (ICI)] vs other and anti-PD-1 vs other. The primary endpoint was the cumulative incidence of brain metastases, and secondary endpoints were OS and PFS. At 12 months, the cumulative incidence of brain metastases was 13.78% in the ICI group [95% confidence interval (CI) 9.14-19.36] and 27.26% in the other group (95% CI 19.38-35.71), P = 0.004. The cumulative incidence was 9.49% in the anti-PD-1 group (95% CI 5.43-14.90) vs 30.11% in the other group (95% CI 22.59-37.97), P < 0.0001. In multivariable analysis (model with 277 patients), anti-PD-1 reduced the risk of brain metastases by almost 70% (hazard ratio = 0.29, 95% CI 0.15-0.56, P < 0.0001). The use of ICI (anti-PD-1/PD-L1) in advanced melanomas without initial brain metastasis shows a protective effect and prevents their occurrence.
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http://dx.doi.org/10.1097/CMR.0000000000000700 | DOI Listing |
Adv Sci (Weinh)
January 2025
The department of oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs.
View Article and Find Full Text PDFF1000Res
January 2025
Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Introduction: Magnetic resonance imaging (MRI) is essential for brain imaging, but conventional methods rely on qualitative contrast, are time-intensive, and prone to variability. Magnetic resonance finger printing (MRF) addresses these limitations by enabling fast, simultaneous mapping of multiple tissue properties like T1, T2. Using dynamic acquisition parameters and a precomputed signal dictionary, MRF provides robust, qualitative maps, improving diagnostic precision and expanding clinical and research applications in brain imaging.
View Article and Find Full Text PDFInt J Nanomedicine
January 2025
Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, People's Republic of China.
Glioma is the most common primary malignant brain tumor with a poor survival rate. It is characterized by diffuse and invasive growth and heterogeneity, which limits tumor identification and complete resection. Therefore, the precise detection and postoperative adjuvant therapy of gliomas have become increasingly important and urgent.
View Article and Find Full Text PDFCancer Imaging
January 2025
Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Background: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significant reproducibility issues. We compare the prediction of overall survival (OS) in recurrent high-grade glioma(HGG) patients undergoing immunotherapy, using deep learning (DL) classification networks along with radiomic signatures derived from manual and convolutional neural networks (CNN) automated segmentation.
View Article and Find Full Text PDFBMC Biol
January 2025
Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany.
Background: Glioblastoma multiforme (GBM) is characterized by its cellular complexity, with a microenvironment consisting of diverse cell types, including oligodendrocyte precursor cells (OPCs) and neoplastic CD133 + radial glia-like cells. This study focuses on exploring the distinct cellular transitions in GBM, emphasizing the role of alternative polyadenylation (APA) in modulating microRNA-binding and post-transcriptional regulation.
Results: Our research identified unique APA profiles that signify the transitional phases between neoplastic cells and OPCs, underscoring the importance of APA in cellular identity and transformation in GBM.
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