Department of Medicine I, University of Erlangen-Nuremberg, Erlangen, Germany.
Gastrointest Endosc. 2013 Jul;78(1):115-20. doi: 10.1016/j.gie.2013.02.001. Epub 2013 Mar 23.
Computed virtual chromoendoscopy (CVC) enables high-definition imaging of mucosal lesions with improved tissue contrast. Previous studies have shown that CVC yields an improved detection rate of colorectal lesions. However, the learning curve for interpretation of CVC images is unknown.
To examine the learning curve of correctly identifying hyperplastic and adenomatous colorectal lesions by using CVC.
Prospective, 2-center study.
Consecutive patients undergoing screening colonoscopy were included. CVC images were analyzed by using corresponding polypectomies as the reference standard followed by a prospective, double-blind review of i-scan images.
A training set containing 20 images with known histology was reviewed to standardize image interpretation, followed by a blind review of 110 unknown images. Overall, 4 endoscopists from 2 different endoscopy centers evaluated the images, which were obtained by 1 endoscopist using high-definition endoscopy with CVC.
Patients were included in a prospective fashion. Seventy-seven of 110 colorectal lesions were adenomas and 33 were hyperplastic lesions. Mean diameter of colonic polyps was 4 mm (range, 2-20 mm). Overall accuracy for the group was 73.9% for lesions 1 to 22, 79.6% for lesions 23 to 44, 84.1% for lesions 45 to 66, 87.5% for lesions 67 to 88, and 94.3% for lesions 89 to 110. Accuracy of i-scan for prediction of polyp histology was not dependent on polyp size (≤5 mm, 6-10 mm, or > 10 mm). The ability to obtain high-quality images was stable over time, and high-quality images were constantly produced.
Post-hoc assessment.
Accurate interpretation of CVC images for prediction of hyperplastic and adenomatous colorectal lesions follows a learning curve but can be learned rapidly.
计算机虚拟 chromoendoscopy（CVC）能够实现黏膜病变的高清成像，并提高组织对比度。先前的研究表明，CVC 可提高结直肠病变的检出率。然而，CVC 图像解读的学习曲线尚不清楚。
研究使用 CVC 正确识别结直肠增生性和腺瘤性病变的学习曲线。
前瞻性、2 中心研究。
纳入连续接受筛查性结肠镜检查的患者。以相应的息肉切除术作为参考标准，分析 CVC 图像，随后前瞻性、双盲分析 i-scan 图像。
首先，回顾包含已知组织学的 20 个图像的训练集，以规范图像解读，然后盲法分析 110 个未知图像。来自 2 个不同内镜中心的 4 名内镜医生评估由同一名内镜医生使用高清内镜结合 CVC 获得的图像。
患者以前瞻性方式纳入研究。110 个结直肠病变中有 77 个为腺瘤，33 个为增生性病变。结肠息肉的平均直径为 4 毫米（范围 2-20 毫米）。对于 1-22 毫米的病变，整体准确率为 73.9%；对于 23-44 毫米的病变，整体准确率为 79.6%；对于 45-66 毫米的病变，整体准确率为 84.1%；对于 67-88 毫米的病变，整体准确率为 87.5%；对于 89-110 毫米的病变，整体准确率为 94.3%。i-scan 预测息肉组织学的准确性与息肉大小（≤5 毫米、6-10 毫米或>10 毫米）无关。获取高质量图像的能力随时间稳定，且不断产生高质量图像。
事后评估。
预测结直肠增生性和腺瘤性病变的 CVC 图像解读遵循学习曲线，但可快速学习。
