Linked color imaging application for improving the endoscopic diagnosis accuracy: a pilot study

Xiaotian Sun, Tenghui Dong, Yiliang Bi, Min Min, Wei Shen, Yang Xu, Yan Liu, Xiaotian Sun, Tenghui Dong, Yiliang Bi, Min Min, Wei Shen, Yang Xu, Yan Liu

Abstract

Endoscopy has been widely used in diagnosing gastrointestinal mucosal lesions. However, there are still lack of objective endoscopic criteria. Linked color imaging (LCI) is newly developed endoscopic technique which enhances color contrast. Thus, we investigated the clinical application of LCI and further analyzed pixel brightness for RGB color model. All the lesions were observed by white light endoscopy (WLE), LCI and blue laser imaging (BLI). Matlab software was used to calculate pixel brightness for red (R), green (G) and blue color (B). Of the endoscopic images for lesions, LCI had significantly higher R compared with BLI but higher G compared with WLE (all P < 0.05). R/(G + B) was significantly different among 3 techniques and qualified as a composite LCI marker. Our correlation analysis of endoscopic diagnosis with pathology revealed that LCI was quite consistent with pathological diagnosis (P = 0.000) and the color could predict certain kinds of lesions. ROC curve demonstrated at the cutoff of R/(G+B) = 0.646, the area under curve was 0.646, and the sensitivity and specificity was 0.514 and 0.773. Taken together, LCI could improve efficiency and accuracy of diagnosing gastrointestinal mucosal lesions and benefit target biopsy. R/(G + B) based on pixel brightness may be introduced as a objective criterion for evaluating endoscopic images.

Figures

Figure 1. Typical endoscopic images for normal…
Figure 1. Typical endoscopic images for normal mucosa, HP infection, inflammation, intestinal metaplasia, atrophy, early cancer and advanced cancer.
Figure 2. Analysis of typical endoscopic images…
Figure 2. Analysis of typical endoscopic images from one patient with gastric adenomatous polyps.
The area of interest in endoscopic images was selected (dashed line) and analyzed by Metalab software to calculate the pixel brightness for red, green and blue color.
Figure 3. ROC curve for differentiating the…
Figure 3. ROC curve for differentiating the lesions from normal mucosa by pixel brightness.

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