Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers
Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers
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DOI：
10.3892/ijo_00000090
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发表时间：
2008-11-01
影响因子：
5.2
通讯作者：
Tzika, A. Aria
中科院分区：
文献类型：
--
作者：
Andronesi, Ovidiu C.;Blekas, Konstantinos D.;Tzika, A. Aria
脑肿瘤是成人癌症患者死亡的主要原因之一。然而，由于检测到的代谢物数量较少，体内磁共振波谱（MRS）对这些肿瘤的分子分类受到限制。体外 MRS 在较高视野下提供信息丰富的生物标志物谱，但也会消耗样品，使其无法用于后续分析。相比之下，离体高分辨率魔角旋转 (HRMAS) MRS 可以节省样品，但需要大量样品，并且根据样品测试温度，可能会对生成准确数据造成技术挑战。我们开发了一种新颖的方法，结合了二维 (213)、固态、HRMAS 质子 (H-1) NMR 方法、TOBSY（全键合光谱），最大限度地发挥了 HRMAS 的优势和稳健的分类策略。我们使用了 55 份脑活检中每份 -8 摄氏度下的约 2 毫克组织，可靠地检测到了 16 种不同的生物学相关分子种类。我们比较了两种分类策略：支持向量机 (SVM) 分类器和使用 Levenberg-Marquardt 反向传播算法的前馈神经网络。我们使用最小冗余/最大相关性 (MRMR) 方法作为强大的特征选择方案以及 SVM 分类器。我们认为，基于信息丰富的二维 MRS 的脑肿瘤分子表征应该使我们能够在体内高精度地对无法手术的患者进行分型和预后。
Brain tumors are one of the leading causes of death in adults with cancer; however, molecular classification of these tumors with in vivo magnetic resonance spectroscopy (MRS) is limited because of the small number of metabolites detected. In vitro MRS provides highly informative biomarker profiles at higher fields, but also consumes the sample so that it is unavailable for subsequent analysis. In contrast, ex vivo high-resolution magic angle spinning (HRMAS) MRS conserves the sample but requires large samples and can pose technical challenges for producing accurate data, depending on the sample testing temperature. We developed a novel approach that combines a two-dimensional (213), solid-state, HRMAS proton (H-1) NMR method, TOBSY (total through-bond spectroscopy), which maximizes the advantages of HRMAS and a robust classification strategy. We used similar to 2 mg of tissue at -8 degrees C from each of 55 brain biopsies, and reliably detected 16 different biologically relevant molecular species. We compared two classification strategies, the support vector machine (SVM) classifier and a feed-forward neural network using the Levenberg-Marquardt back-propagation algorithm. We used the minimum redundancy/maximum relevance (MRMR) method as a powerful feature-selection scheme along with the SVM classifier. We suggest that molecular characterization of brain tumors based on highly informative 2D MRS should enable us to type and prognose even inoperable patients with high accuracy in vivo.