Miyaki Rie, Yoshida Shigeto, Tanaka Shinji, Kominami Yoko, Sanomura Yoji, Matsuo Taiji, Oka Shiro, Raytchev Bisser, Tamaki Toru, Koide Tetsushi, Kaneda Kazufumi, Yoshihara Masaharu, Chayama Kazuaki
Departments of *Gastroenterology and Metabolism †Endoscopy and Medicine ‡Information Engineering, Graduate School of Engineering §Research Institute for Nanodevice and Bio Systems ∥Department of Health Service Center, Hiroshima University, Hiroshima, Japan.
J Clin Gastroenterol. 2015 Feb;49(2):108-15. doi: 10.1097/MCG.0000000000000104.
To evaluate the usefulness of a newly devised computer system for use with laser-based endoscopy in differentiating between early gastric cancer, reddened lesions, and surrounding tissue.
Narrow-band imaging based on laser light illumination has come into recent use. We devised a support vector machine (SVM)-based analysis system to be used with the newly devised endoscopy system to quantitatively identify gastric cancer on images obtained by magnifying endoscopy with blue-laser imaging (BLI). We evaluated the usefulness of the computer system in combination with the new endoscopy system.
We evaluated the system as applied to 100 consecutive early gastric cancers in 95 patients examined by BLI magnification at Hiroshima University Hospital. We produced a set of images from the 100 early gastric cancers; 40 flat or slightly depressed, small, reddened lesions; and surrounding tissues, and we attempted to identify gastric cancer, reddened lesions, and surrounding tissue quantitatively.
The average SVM output value was 0.846 ± 0.220 for cancerous lesions, 0.381 ± 0.349 for reddened lesions, and 0.219 ± 0.277 for surrounding tissue, with the SVM output value for cancerous lesions being significantly greater than that for reddened lesions or surrounding tissue. The average SVM output value for differentiated-type cancer was 0.840 ± 0.207 and for undifferentiated-type cancer was 0.865 ± 0.259.
Although further development is needed, we conclude that our computer-based analysis system used with BLI will identify gastric cancers quantitatively.
评估一种新设计的计算机系统在基于激光的内窥镜检查中用于鉴别早期胃癌、发红病变和周围组织的实用性。
基于激光照明的窄带成像最近开始应用。我们设计了一种基于支持向量机（SVM）的分析系统，与新设计的内窥镜系统配合使用，以在通过蓝光激光成像（BLI）放大内窥镜检查获得的图像上定量识别胃癌。我们评估了该计算机系统与新内窥镜系统结合使用的实用性。
我们在广岛大学医院对95例接受BLI放大检查的患者中连续100例早期胃癌应用该系统进行评估。我们从100例早期胃癌、40例扁平或轻度凹陷的小发红病变以及周围组织中生成了一组图像，并试图定量识别胃癌、发红病变和周围组织。
癌性病变的平均SVM输出值为0.846±0.220，发红病变为0.381±0.349，周围组织为0.219±0.277，癌性病变的SVM输出值显著高于发红病变或周围组织。分化型癌的平均SVM输出值为0.840±0.207，未分化型癌为0.865±0.259。
尽管还需要进一步开发，但我们得出结论，我们与BLI配合使用的基于计算机的分析系统将能够定量识别胃癌。
Zotero 插件