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This study aims to describe the demographic profile,clinical characteristics, and histopathological profile of primary malignant central nervous system (cns) tumors of children and adolescents and assess the efficacy of treatment methods, and identify factors that influence the survival. Using annotations, we developed a machine learning approach to integrate multimodal features into a risk prediction model. Results from a phase 1 study.

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The high concordance and npv for detecting kras and braf mutations suggest that liquid biopsy is suitable to guide targeted therapy selection in crc patients with liver metastases but lacks sensitivity for patients with only lung or peritoneal metastases. We have developed a novel approach based on a traditional ct radiomics model to to accurately predict os and tumor heterogeneity in rpls As of april 2, 2025, 57 patients had at least one tumor assessment.