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When compared with solid brain metastases from NSCLC, leptomeningeal disease (LMD) has unique growth patterns and is rapidly fatal. Patients with LMD do not undergo surgical resection, limiting the tissue available for scientific research. In this study we performed whole exome sequencing on eight samples of LMD to identify somatic mutations and compared the results with those for 26 solid brain metastases. We found that taste 2 receptor member 31 gene (TAS2R31) and phosphodiesterase 4D interacting protein gene (PDE4DIP) were recurrently mutated among LMD samples, suggesting involvement in LMD progression. Together with a retrospective review of the charts of an additional 44 patients with NSCLC LMD, we discovered a surprisingly low number of KRAS mutations (n = 4 [7.7%]) but a high number of EGFR mutations (n = 33 [63.5%]). The median interval for development of LMD from NSCLC was shorter in patients with mutant EGFR (16.3 months) than in patients with wild-type EGFR (23.9 months) (p = 0.017). Targeted analysis of recurrent mutations thus presents a useful complement to the existing diagnostic tool kit, and correlations of EGFR in LMD and KRAS in solid metastases suggest that molecular distinctions or systemic treatment pressure underpin the differences in growth patterns within the brain.
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http://dx.doi.org/10.1016/j.jtho.2018.03.018 | DOI Listing |
Curr Med Imaging
September 2025
Department of Pharmacy, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
Unlabelled: Leptomeningeal metastasis (LM) is a severe complication of solid malignancies, including lung adenocarcinoma, characterized by poor prognosis and diagnostic challenges. This study assesses whether curvilinear peri-brainstem hyperintense signals on MRI are a characteristic feature of LM in lung adenocarcinoma patients.
Methods: This retrospective study analyzed data from multiple centers, encompassing lung adenocarcinoma patients with peri-brainstem curvilinear hyperintense signals on MRI between January 2016 and March 2022.
Front Immunol
September 2025
Precision Pharmacy and Drug Development Center, Department of Pharmacy, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
Gliomas are the most common primary malignant tumors of the central nervous system (CNS), and despite progress in molecular diagnostics and targeted therapies, their prognosis remains poor. In recent years, immunotherapy has emerged as a promising treatment modality in cancer therapy. However, the inevitable immune evasion by tumor cells is a key barrier affecting therapeutic efficacy.
View Article and Find Full Text PDFCancer Manag Res
September 2025
The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian, People's Republic of China.
Background: Lung cancer brain metastasis (LCBM) accounts for 40-50% of intracranial malignancies, with emerging evidence of alternative metastatic pathways circumventing the blood-brain barrier. Existing prognostic models lack validation in Asian populations and molecular stratification. This multicenter study aimed to develop a clinical nomogram integrating clinicopathological and molecular determinants for personalized LCBM management.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
September 2025
Neurosurgery Department, 10th Military Research Hospital and PolyClinic SPZOZ, Bydgoszcz, Poland.
Background: Pheochromocytoma (PCC) is a rare neuroendocrine tumor, with 10-15% of cases showing malignant behavior defined by metastatic spread, including exceptionally rare central nervous system (CNS) involvement. Brain metastases present unique diagnostic and therapeutic challenges due to their potential to impair neurological function. This study reports a case of malignant PCC (mPCC) with CNS metastases and a systematic review to clarify the clinical patterns, management strategies, and prognostic factors.
View Article and Find Full Text PDFMed Eng Phys
October 2025
Biomedical Device Technology, Istanbul Aydın University, Istanbul, 34093, Istanbul, Turkey. Electronic address:
Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection.
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