Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning
Coregistered FDG PET/CT-Based Textural Characterization of Head and Neck Cancer for Radiation Treatment Planning
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DOI：
10.1109/tmi.2008.2004425
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发表时间：
2009-03-01
影响因子：
10.6
通讯作者：
Mozeg, Daniel
中科院分区：
文献类型：
--
作者：
Yu, Huan;Caldwell, Curtis;Mozeg, Daniel
与单独使用CT模拟相比，共配准的氟脱氧葡萄糖（FDG）正电子发射断层扫描/计算机断层扫描（PET/CT）有可能提高头颈癌（HNC）放射靶向的准确性。本研究的目的是通过对F-18-FDG PET和CT图像进行定量纹理分析，确定有助于区分头颈部肿瘤和正常组织的纹理特征。从20例HNC和20例肺癌患者的PET/CT图像中手动分割出异常和典型正常组织。纹理特征，包括一些来自空间灰度依赖矩阵（SGLDM）和邻域灰度差矩阵（NGTDM）被选择用于表征这些分割的感兴趣区域（ROI）。两个K近邻（KNN）和决策树（DT）为基础的KNN分类器来区分异常和正常组织的图像。受试者工作特征（ROC）的曲线下面积（A（Z））用于评估与专家观察员相比的特征区分性能。采用留一法和bootstrap技术对结果进行了验证。基于DT的KNN分类器的A（Z）为0.95。正常和异常组织分类的敏感性和特异性分别为89%和99%。总之，从FDG PET/CT图像中提取的NGTDM特征（如PET粗糙度、PET对比度和CT粗糙度）提供了良好的区分性能。这些特征的临床使用可能导致HNC的辐射靶向的准确性的改善。
Coregistered fluoro-deoxy-glucose (FDG) positron emission tomography/computed tomography (PET/CT) has shown potential to improve the accuracy of radiation targeting of head and neck cancer (HNC) when compared to the use of CT simulation alone. The objective of this study was to identify textural features useful in distinguishing tumor from normal tissue in head and neck via quantitative texture analysis of coregistered F-18-FDG PET and CT images. Abnormal and typical normal tissues were manually segmented from PET/CT images of 20 patients with HNC and 20 patients with lung cancer. Texture features including some derived from spatial grey-level dependence matrices (SGLDM) and neighborhood gray-tone-difference matrices (NGTDM) were selected for characterization of these segmented regions of interest (ROIs). Both K nearest neighbors (KNNs) and decision tree (DT)-based KNN classifiers were employed to discriminate images of abnormal and normal tissues. The area under the curve (A(Z)) of receiver operating characteristics (ROC) was used to evaluate the discrimination performance of features in comparison to an expert observer. The leave-one-out and bootstrap techniques were used to validate the results. The A(Z) of DT-based KNN classifier was 0.95. Sensitivity and specificity for normal and abnormal tissue classification were 89% and 99%, respectively. In summary, NGTDM features such as PET Coarseness, PET Contrast, and CT Coarseness extracted from FDG PET/CT images provided good discrimination performance. The clinical use of such features may lead to improvement in the accuracy of radiation targeting of HNC.