Surgical Molecular Imaging Laboratory, Department of Neurosurgery, 221, Longwood Avenue, BLI-137, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
Anal Chem. 2010 Apr 1;82(7):2621-5. doi: 10.1021/ac100113w.
Often considered benign, meningiomas represent 32% of intracranial tumors with three grades of malignancy defined by the World Health Organization (WHO) histology based classification. Malignant meningiomas are associated with less than 2 years median survival. The inability to predict recurrence and progression of meningiomas induces significant anxiety for patients and limits physicians in implementing prophylactic treatment approaches. This report presents an analytical approach to tissue characterization based on matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry imaging (MSI) which is introduced in an attempt to develop a reference database for predictive classification of brain tumors. This pilot study was designed to evaluate the potential of such an approach and to begin to address limitations of the current methodology. Five recurrent and progressive meningiomas for which surgical specimens were available from the original and progressed grades were selected and tested against nonprogressive high-grade meningiomas, high-grade gliomas, and nontumor brain specimens. The common profiling approach of data acquisition was compared to imaging and revealed significant benefits in spatially resolved acquisition for improved spectral definition. A preliminary classifier based on the support vector machine showed the ability to distinguish meningioma image spectra from the nontumor brain and from gliomas, a different type of brain tumor, and to enable class imaging of surgical tissue. Although the development of classifiers was shown to be sensitive to data preparation parameters such as recalibration and peak picking criteria, it also suggested the potential for maturing into a predictive algorithm if provided with a larger series of well-defined cases.
脑膜瘤通常被认为是良性的，占颅内肿瘤的 32%，根据世界卫生组织（WHO）的组织学分类，恶性脑膜瘤分为三级。恶性脑膜瘤的中位生存期不到 2 年。由于无法预测脑膜瘤的复发和进展，这给患者带来了极大的焦虑，并限制了医生实施预防性治疗方法。本报告介绍了一种基于基质辅助激光解吸电离飞行时间（MALDI-TOF）质谱成像（MSI）的组织特征分析方法，旨在建立一个预测脑肿瘤分类的参考数据库。本研究旨在评估该方法的潜力，并开始解决当前方法学的局限性。选择了 5 例复发性和进行性脑膜瘤，这些肿瘤有原始和进展阶段的手术标本，并与非进行性高级别脑膜瘤、高级别胶质瘤和非肿瘤脑组织标本进行了比较。对数据采集的常用分析方法与成像进行了比较，结果表明在空间分辨率采集方面具有明显的优势，可以改善光谱定义。基于支持向量机的初步分类器显示出能够区分脑膜瘤图像光谱与非肿瘤脑组织和胶质瘤的能力，从而能够对手术组织进行分类成像。尽管分类器的开发显示出对数据准备参数（如重新校准和峰选择标准）的敏感性，但如果提供了一系列定义明确的病例，它也表明了成熟为预测算法的潜力。
