Summers Ronald M, Jerebko Anna K, Franaszek Marek, Malley James D, Johnson C Daniel
Department of Diagnostic Radiology, Warren Grant Magnuson Clinical Center, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm 1C660, Bethesda, MD 20892-1182, USA.
Radiology. 2002 Nov;225(2):391-9. doi: 10.1148/radiol.2252011619.
To apply a computer-aided detection (CAD) algorithm to supine and prone multisection helical computed tomographic (CT) colonographic images to confirm if there is any added benefit provided by CAD over that of standard clinical interpretation.
CT colonography (with patients in both supine and prone positions) was performed with a multisection helical CT scanner in 40 asymptomatic high-risk patients. There were two consecutive series of patients, 20 of whom had at least one polyp 1.0 cm in size or larger and 20 of whom had normal colons at conventional colonoscopy performed the same day. The CT colonographic images were interpreted with an automated CAD algorithm and by two radiologists who were blinded to colonoscopy findings.
For 25 polyps at least 1.0 cm in size ("large" polyps), sensitivity for detection by at least one radiologist was 48% (12 of 25). The sensitivity of CAD for detecting large polyps was also 48% (12 of 25), but the CAD algorithm detected four of 13 large polyps that were not detected by either radiologist (31%, 95% two-sided CI: 9, 61), increasing the potential sensitivity to 64% (16 of 25). For polyps identifiable retrospectively, sensitivity of CAD was 67% (12 of 18), and sensitivity of the combination of detection with the CAD algorithm or by at least one radiologist was 89% (16 of 18). There were an average of 11 false-positive detections per patient for CAD.
In this series of patients in whom radiologists had difficulties detecting polyps (compared with sensitivities of 75%-90% reported in the literature), this CAD algorithm played a complementary role to conventional interpretation of CT colonographic images by detecting a number of large polyps missed by trained observers.
将计算机辅助检测（CAD）算法应用于仰卧位和俯卧位多层面螺旋计算机断层扫描（CT）结肠造影图像，以确定CAD相对于标准临床解读是否具有额外优势。
使用多层面螺旋CT扫描仪对40例无症状高危患者进行CT结肠造影检查（患者分别处于仰卧位和俯卧位）。患者分为连续两组，其中20例患者至少有一个直径1.0厘米或更大的息肉，另外20例患者在同一天进行的传统结肠镜检查中结肠正常。CT结肠造影图像由一种自动化CAD算法以及两名对结肠镜检查结果不知情的放射科医生进行解读。
对于25个直径至少1.0厘米的息肉（“大”息肉），至少一名放射科医生检测的敏感度为48%（25个中的12个）。CAD检测大息肉的敏感度也是48%（25个中的12个），但CAD算法检测出了13个大息肉中两名放射科医生均未检测出的4个（31%，95%双侧置信区间：9，61），使潜在敏感度提高到64%（25个中的16个）。对于可回顾性识别的息肉，CAD的敏感度为67%（18个中的12个），CAD算法或至少一名放射科医生联合检测的敏感度为89%（18个中的16个）。CAD平均每位患者有11次假阳性检测。
在这组放射科医生检测息肉存在困难的患者中（与文献报道的75% - 90%的敏感度相比），这种CAD算法通过检测出一些训练有素的观察者遗漏的大息肉，对CT结肠造影图像的传统解读起到了补充作用。
