Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China.
Phys Med Biol. 2012 Dec 21;57(24):8393-404. doi: 10.1088/0031-9155/57/24/8393. Epub 2012 Nov 30.
Glioma is the primary tumor in the central nervous system, and poses one of the greatest challenges in clinical treatment. MIB-1 and p53 are the most useful biomarkers for gliomas and could help neurosurgeons establish a therapeutic schedule. However, these biomarkers are commonly detected with the help of immunohistochemistry (IHC), which wastes time and energy and is often influenced by subjective factors. To reduce the subjective factors and improve the efficiency in the judgment of IHC, a novel magnetic resonance image (MRI) analysis method is proposed in the present study to detect the expression status of MIB-1 and p53 in IHC. The proposed method includes two kinds of MRI acquisition (FLAIR and T1 FLAIR images), regions of interest (ROIs) selection, texture features (i.e. the gray level gradient co-occurrence matrix (GLGCM), Minkowski functions (MFs), etc) extraction in ROIs, and classification with a support vector machine in a leave-one-out cross validation strategy. By classifying the ROIs, the performance of the method was evaluated by accuracy, area under ROC curve (AUC), etc. A high accuracy (0.7640 ± 0.0225) and AUC (0.7873 ± 0.0377) for MIB-I detection were achieved. In terms of the texture features, 0.7621 ± 0.0199, 0.7666 ± 0.0365 and 0.7426 ± 0.0451 AUC can be obtained using only GLCM, RLM or GLGCM for MIB-1 detection, respectively. In all, the experimental results demonstrated that MR image texture features are associated with the expression status of MIB-1 and p53. The proposed method has the potential to realize high accuracy and robust detection for MIB-I expression status, which makes it promising for clinical glioma diagnosis and prognosis.
脑胶质瘤是中枢神经系统的原发性肿瘤，也是临床治疗中面临的最大挑战之一。MIB-1 和 p53 是脑胶质瘤最有用的生物标志物，可以帮助神经外科医生制定治疗方案。然而，这些生物标志物通常需要借助免疫组织化学（IHC）来检测，这既浪费时间和精力，又常常受到主观因素的影响。为了减少主观因素并提高 IHC 判断的效率，本研究提出了一种新的磁共振成像（MRI）分析方法，用于检测 IHC 中 MIB-1 和 p53 的表达状态。该方法包括两种 MRI 采集（FLAIR 和 T1 FLAIR 图像）、感兴趣区（ROI）选择、ROI 中的纹理特征（即灰度梯度共生矩阵（GLGCM）、Minkowski 函数（MFs）等）提取以及在留一交叉验证策略中使用支持向量机进行分类。通过对 ROI 进行分类，以准确率、ROC 曲线下面积（AUC）等评估方法评估了该方法的性能。MIB-I 检测的准确率和 AUC 分别达到 0.7640±0.0225 和 0.7873±0.0377。就纹理特征而言，仅使用 GLCM、RLM 或 GLGCM 进行 MIB-1 检测，可分别获得 0.7621±0.0199、0.7666±0.0365 和 0.7426±0.0451 AUC。总之，实验结果表明 MRI 图像纹理特征与 MIB-1 和 p53 的表达状态有关。该方法有望实现 MIB-1 表达状态的高精度和稳健检测，为临床脑胶质瘤的诊断和预后提供了新的思路。
