O'Connor Stacy D, Summers Ronald M, Yao Jianhua, Pickhardt Perry J, Choi J Richard
Department of Radiology, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C351, MSC 1182, Bethesda, MD 20892-1182, USA.
Radiology. 2006 Nov;241(2):426-32. doi: 10.1148/radiol.2412051223. Epub 2006 Sep 27.
To retrospectively identify volume and average attenuation thresholds for differentiating between ileocecal valve (ICV) and polyp at computed tomographic (CT) colonography with computer-aided detection (CAD).
Informed consent (with consent for future retrospective research) and institutional review board (IRB) approval were obtained for the original prospective study. This retrospective study had IRB approval, as well, and was HIPAA-compliant. A total of 496 patients were selected from a larger screening population. CT colonographic images from 394 patients (227 men, 167 women; mean age, 58.0 years; range, 40-79 years) were used as a training set, and images from 102 patients (76 men, 26 women; mean age, 59.8 years; range, 46-79 years) were used as a test set. A series of 2742 volume and attenuation thresholds, for which segmented findings both larger in volume and lower in average attenuation were labeled as ICVs and remaining findings were labeled polyps, were applied to the training set to determine settings with 100% sensitivity for polyp detection and the highest specificity for ICV detection. The optimal settings were then applied to the test set. Significance was assessed with the Fisher exact test, and 95% confidence intervals (CIs) were computed for sensitivity and specificity.
A total of 386 ICVs and 67 adenomatous polyps from the training set and 102 ICVs and 138 adenomatous polyps from the test set could be segmented with a three-dimensional segmentation algorithm. When supine and prone images were counted individually, 746 nonunique ICVs from the training set and 191 from the test set were segmentable. In the training set, a volume of 600 mm(3) and an attenuation of 36 HU provided 100% sensitivity (67 polyps; 95% CI: 93%, 100%) and the optimal 83% specificity (618 of 746 ICVs; 95% CI: 80%, 85%). When applied to the test set, this combination provided 97% sensitivity (134 of 138 polyps; 95% CI: 92%, 99%) and 84% specificity (160 of 191 ICVs; 95% CI: 78%, 89%). Differences in sensitivity and specificity in the detection of polyps between the sets were not significant.
Volume and average CT attenuation thresholds can help differentiate most ICVs from true polyps.
通过计算机辅助检测（CAD）的计算机断层扫描（CT）结肠成像，回顾性确定区分回盲瓣（ICV）和息肉的体积及平均衰减阈值。
原始前瞻性研究已获得知情同意（同意未来进行回顾性研究）及机构审查委员会（IRB）批准。本回顾性研究也获得了IRB批准，且符合健康保险流通与责任法案（HIPAA）。从更大的筛查人群中选取了496例患者。将394例患者（227例男性，167例女性；平均年龄58.0岁；范围40 - 79岁）的CT结肠成像图像用作训练集，102例患者（76例男性，26例女性；平均年龄59.8岁；范围46 - 79岁）的图像用作测试集。一系列2742个体积和衰减阈值被应用于训练集，对于这些阈值，体积较大且平均衰减较低的分割结果被标记为ICV，其余结果被标记为息肉，以确定息肉检测灵敏度为100%且ICV检测特异性最高的设置。然后将最佳设置应用于测试集。采用Fisher精确检验评估显著性，并计算灵敏度和特异性的95%置信区间（CI）。
使用三维分割算法可分割训练集中的386个ICV和67个腺瘤性息肉，以及测试集中的102个ICV和138个腺瘤性息肉。若仰卧位和俯卧位图像分别计算，训练集中746个非唯一的ICV和测试集中191个ICV可被分割。在训练集中，体积为600 mm³且衰减为36 HU时，灵敏度为100%（67个息肉；95% CI：93%，100%），特异性最佳为83%（746个ICV中的618个；95% CI：80%，85%）。应用于测试集时，该组合的灵敏度为97%（138个息肉中的134个；95% CI：92%，99%），特异性为84%（191个ICV中的160个；95% CI：78%，89%）。两组间息肉检测的灵敏度和特异性差异不显著。
体积和CT平均衰减阈值有助于区分大多数ICV与真正的息肉。
