Yu Huan, Caldwell Curtis, Mah Katherine, Poon Ian, Balogh Judith, MacKenzie Robert, Khaouam Nader, Tirona Romeo
Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
Int J Radiat Oncol Biol Phys. 2009 Oct 1;75(2):618-25. doi: 10.1016/j.ijrobp.2009.04.043. Epub 2009 Aug 14.
A co-registered multimodality pattern analysis segmentation system (COMPASS) was developed to automatically delineate the radiation targets in head-and-neck cancer (HNC) using both (18)F-fluoro-deoxy glucose-positron emission tomography (PET) and computed tomography (CT) images. The performance of the COMPASS was compared with the results of existing threshold-based methods and radiation oncologist-drawn contours.
The COMPASS extracted texture features from corresponding PET and CT voxels. Using these texture features, a decision-tree-based K-nearest-neighbor classifier labeled each voxel as either "normal" or "abnormal." The COMPASS was applied to the PET/CT images of 10 HNC patients. Automated segmentation results were validated against the manual segmentations of three radiation oncologists using the volume, sensitivity, and specificity. The performance of the COMPASS was compared with three PET-based threshold methods: standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio.
The tumor delineations of the COMPASS were both quantitatively and qualitatively more similar to those of the radiation oncologists than the delineations from the other methods. The specificity was 95% +/- 2%, 84% +/- 9%, 98% +/- 3%, and 96% +/- 4%, and the sensitivity was 90% +/- 12%, 93% +/- 10%, 48% +/- 20%, and 68% +/- 25% for the COMPASS, for a standard uptake value of 2.5, 50% maximal intensity, and signal/background ratio, respectively. The COMPASS distinguished HNC from adjacent normal tissues with high physiologic uptake and consistently defined tumors with large variability in (18)F-fluoro-deoxy glucose uptake, which are often problematic with the threshold-based methods.
Automated segmentation using texture analysis of PET/CT images has the potential to provide accurate delineation of HNC. This could lead to reduced interobserver variability, reduced uncertainty in target delineation, and improved treatment planning accuracy.
开发一种联合注册多模态模式分析分割系统（COMPASS），用于利用氟代脱氧葡萄糖正电子发射断层扫描（PET）和计算机断层扫描（CT）图像自动勾勒头颈癌（HNC）的放疗靶区。将COMPASS的性能与现有基于阈值的方法及放射肿瘤学家绘制的轮廓结果进行比较。
COMPASS从相应的PET和CT体素中提取纹理特征。利用这些纹理特征，基于决策树的K近邻分类器将每个体素标记为“正常”或“异常”。将COMPASS应用于10例HNC患者的PET/CT图像。使用体积、灵敏度和特异性，对照三位放射肿瘤学家的手动分割结果验证自动分割结果。将COMPASS的性能与三种基于PET的阈值方法进行比较：标准摄取值为2.5、最大强度的50%以及信号/背景比。
与其他方法的勾勒结果相比，COMPASS的肿瘤勾勒在定量和定性方面都与放射肿瘤学家的结果更为相似。对于COMPASS、标准摄取值为2.5、最大强度的50%以及信号/背景比，特异性分别为95%±2%、84%±9%、98%±3%和96%±4%，灵敏度分别为90%±12%、93%±10%、48%±20%和68%±25%。COMPASS能够将HNC与具有高生理性摄取的相邻正常组织区分开来，并始终能够界定氟代脱氧葡萄糖摄取差异较大的肿瘤，而这对于基于阈值的方法来说往往存在问题。
利用PET/CT图像纹理分析进行自动分割有潜力准确勾勒HNC。这可能会减少观察者间的差异，降低靶区勾勒的不确定性，并提高治疗计划的准确性。
