Huang Jacob, Gholami Behnood, Agar Nathalie Y R, Norton Isaiah, Haddad Wassim M, Tannenbaum Allen R
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7965-8. doi: 10.1109/IEMBS.2011.6091964.
Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample's histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry.
胶质瘤组织学类型是预后评估的主要因素，并用于确定合适的治疗方案。此外，由于颅腔环境的敏感性，实时肿瘤细胞分类和边界检测有助于提高肿瘤切除的精确性和完整性。最近对质谱技术的一项改进称为解吸电喷雾电离，它在常压环境下运行，无需使用制备化合物。这使得在手术和其他现场操作过程中能够实时获取质谱图。在本文中，我们提出了一个使用稀疏核机器的框架，通过分析解吸电喷雾电离质谱法获取的胶质瘤样本的化学成分，来确定其组织病理学亚型。
