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    題名: MRI-based Radiomics and Deep Learning in the Diagnosis and Prognosis of Gliomas
    作者: MINH, TRAN NGUYEN TUAN
    貢獻者: 國際醫學研究博士學位學程
    黎阮國慶
    關鍵詞: gliomas;machine learning;deep learning;predictive models;survival;MGMT methylation status
    gliomas;machine learning;deep learning;predictive models;survival;MGMT methylation status
    日期: 2024-07-01
    上傳時間: 2024-11-06 13:34:00 (UTC+8)
    摘要: Among human brain tumors, gliomas constitute the majority. This disease remains deadly despite modern treatment strategies. Only 3-5 % of patients with glioblastoma (GBM), also known as World Health Organization (WHO) grade IV glioma, can survive more than three years after diagnosis. Integrating survival predictions into treatment planning may revolutionize glioma management strategies and enhance patient-centered care as research advances. The O6-methylguanine-DNA methyltransferase (MGMT) methylation status of glioma patients serves both as a prognostic and therapeutic indicator for those diagnosed with gliomas. The medical research community is increasingly focused on noninvasive, computer-aided techniques due to their precision and reduced procedural risks. There remain substantial opportunities for research in applying radiomics, machine learning (ML), and deep learning (DL) to glioma patients. Advancing studies in these areas have the potential to enhance our understanding and improve clinical outcomes significantly.
    Among human brain tumors, gliomas constitute the majority. This disease remains deadly despite modern treatment strategies. Only 3-5 % of patients with glioblastoma (GBM), also known as World Health Organization (WHO) grade IV glioma, can survive more than three years after diagnosis. Integrating survival predictions into treatment planning may revolutionize glioma management strategies and enhance patient-centered care as research advances. The O6-methylguanine-DNA methyltransferase (MGMT) methylation status of glioma patients serves both as a prognostic and therapeutic indicator for those diagnosed with gliomas. The medical research community is increasingly focused on noninvasive, computer-aided techniques due to their precision and reduced procedural risks. There remain substantial opportunities for research in applying radiomics, machine learning (ML), and deep learning (DL) to glioma patients. Advancing studies in these areas have the potential to enhance our understanding and improve clinical outcomes significantly.
    描述: 博士
    指導教授：黎阮國慶
    口試委員：羅崇銘
    口試委員：黎阮國慶
    口試委員：陳彥廷
    口試委員：杜書儒
    口試委員：蘇家玉
    附註: 論文公開日期：2024-07-09
    資料類型: thesis
    顯示於類別:[國際醫學研究碩博士學位學程] 博碩士論文

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