Zhang Jing, Barboriak Daniel P, Hobbs Hasan, Mazurowski Maciej A
Department of Radiology, Duke University School of Medicine, Durham, North Carolina 27710.
Med Phys. 2014 Apr;41(4):042301. doi: 10.1118/1.4866218.
Glioblastoma is the most common malignant brain tumor. It is characterized by low median survival time and high survival variability. Survival prognosis for glioblastoma is very important for optimized treatment planning. Imaging features observed in magnetic resonance (MR) images were shown to be a good predictor of survival. However, manual assessment of MR features is time-consuming and can be associated with a high inter-reader variability as well as inaccuracies in the assessment. In response to this limitation, the authors proposed and evaluated a computer algorithm that extracts important MR image features in a fully automatic manner.
The algorithm first automatically segmented the available volumes into a background region and four tumor regions. Then, it extracted ten features from the segmented MR imaging volumes, some of which were previously indicated as predictive of clinical outcomes. To evaluate the algorithm, the authors compared the extracted features for 73 glioblastoma patients to the reference standard established by manual segmentation of the tumors.
The experiments showed that their algorithm was able to extract most of the image features with moderate to high accuracy. High correlation coefficients between the automatically extracted value and reference standard were observed for the tumor location, minor and major axis length as well as tumor volume. Moderately high correlation coefficients were also observed for proportion of enhancing tumor, proportion of necrosis, and thickness of enhancing margin. The correlation coefficients for all these features were statistically significant (p < 0.0001).
The authors proposed and evaluated an algorithm that, given a set of MR volumes of a glioblastoma patient, is able to extract MR image features that correlate well with their reference standard. Future studies will evaluate how well the computer-extracted features predict survival.
胶质母细胞瘤是最常见的恶性脑肿瘤。其特点是中位生存时间短且生存变异性高。胶质母细胞瘤的生存预后对于优化治疗方案非常重要。磁共振（MR）图像中观察到的影像特征被证明是生存的良好预测指标。然而，手动评估MR特征既耗时，又可能存在较高的阅片者间变异性以及评估不准确的问题。针对这一局限性，作者提出并评估了一种能以全自动方式提取重要MR图像特征的计算机算法。
该算法首先自动将可用体积分割为一个背景区域和四个肿瘤区域。然后，从分割后的MR成像体积中提取十个特征，其中一些特征先前已被指出可预测临床结果。为评估该算法，作者将73例胶质母细胞瘤患者提取的特征与通过手动分割肿瘤建立的参考标准进行了比较。
实验表明，他们的算法能够以中等到高精度提取大部分图像特征。在肿瘤位置、短轴和长轴长度以及肿瘤体积方面，自动提取值与参考标准之间观察到高相关系数。在增强肿瘤比例、坏死比例和增强边缘厚度方面也观察到中等偏高的相关系数。所有这些特征的相关系数均具有统计学意义（p < 0.0001）。
作者提出并评估了一种算法，该算法在给定一组胶质母细胞瘤患者的MR体积时，能够提取与参考标准相关性良好的MR图像特征。未来的研究将评估计算机提取的特征对生存的预测效果如何。
