Objective: Paclitaxel-cisplatin is considered to be a standard therapy for metastatic non-small-cell lung cancer (NSCLC). The aim of this study was to evaluate the activity and toxicity of this combination with vinorelbine or gemcitabine as front-line therapy in brain metastases from NSCLC.
Methods: Twenty-six chemotherapy-naive patients with an ECOG performance status of 0-2 were treated with paclitaxel (135 mg/m(2)) on day 1, cisplatin (120 mg/m(2)) on day 1, and either vinorelbine (30 mg/m(2)) on days 1 and 15 or gemcitabine (800 mg/m(2)) on days 1 and 8. Whole-brain irradiation was offered early in case of progression and later as consolidation treatment.
Results: All patients were evaluated for toxicity and 25 for response. An intracranial response rate was observed in 38% of the patients (95% CI: 22-59%). WHO grade 3-4 neutropenia and thrombocytopenia occurred in 31 and 4% of the patients, respectively. There was one treatment-related death. Non-hematological toxicities were mild. After a median follow-up of 46 months, the median overall survival for all patients was 21.4 weeks and the median time to progression was 12.8 weeks.
Conclusions: Paclitaxel and cisplatin combined with vinorelbine or gemcitabine as front-line therapy in brain metastases seem to achieve responses similar to those for extracranial disease, suggesting a meaningful role in this setting.
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http://dx.doi.org/10.1159/000066520 | DOI Listing |
Mol Biol Rep
January 2025
Institute of Pathogenic Biology, Guilin Medical University, Guilin, 541199, China.
Cyclin-dependent kinase 5 (CDK5), a unique member of the CDK family, is a proline-directed serine/threonine protein kinase with critical roles in various physiological and pathological processes. Widely expressed in the central nervous system, CDK5 is strongly implicated in neurological diseases. Beyond its neurological roles, CDK5 is involved in metabolic disorders, psychiatric conditions, and tumor progression, contributing to processes such as proliferation, migration, immune evasion, genomic stability, and angiogenesis.
View Article and Find Full Text PDFNeuroradiology
January 2025
Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Elife
January 2025
Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, United States.
Mutations in Sonic Hedgehog (SHH) signaling pathway genes, for example, (SUFU), drive granule neuron precursors (GNP) to form medulloblastomas (MB). However, how different molecular lesions in the Shh pathway drive transformation is frequently unclear, and mutations in the cerebellum seem distinct. In this study, we show that fibroblast growth factor 5 (FGF5) signaling is integral for many infantile MB cases and that expression is uniquely upregulated in infantile MB tumors.
View Article and Find Full Text PDFOnco Targets Ther
January 2025
Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, People's Republic of China.
Lung cancer is a malignant tumor with high morbidity and mortality in China and worldwide. Once it metastasizes to the brain, its prognosis is very poor. Brain metastases are found in about 20% of newly diagnosed non-small-cell lung cancer (NSCLC) patients.
View Article and Find Full Text PDFFront Neurol
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Objective: To develop a machine learning-based clinical and/or radiomics model for predicting the primary site of brain metastases using multiparametric magnetic resonance imaging (MRI).
Materials And Methods: A total of 202 patients (87 males, 115 females) with 439 brain metastases were retrospectively included, divided into training sets (brain metastases of lung cancer [BMLC] = 194, brain metastases of breast cancer [BMBC] = 108, brain metastases of gastrointestinal tumor [BMGiT] = 48) and test sets (BMLC = 50, BMBC = 27, BMGiT = 12). A total of 3,404 quantitative image features were obtained through semi-automatic segmentation from MRI images (T1WI, T2WI, FLAIR, and T1-CE).
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