Association between mucin phenotype and lesion border detection using acetic acid-indigo carmine chromoendoscopy in early gastric cancers.

Background: For successful treatment of early gastric cancers (EGCs), it is crucial to define the horizontal border of the lesion with high accuracy. Acetic acid-indigo carmine (AI) chromoendoscopy has been used to determine the horizontal border in EGCs, but this technique is less potent in certain situations. Mucin phenotype in gastric cancers refers to biological differences in precursor lesions and differences in histopathologic findings, and it might affect AI chromoendoscopy findings. We aimed to investigate the association between mucin phenotype and AI chromoendoscopy findings in EGCs.

Methods: We prospectively evaluated 126 lesions in 126 patients with endoscopically diagnosed EGCs. Conventional endoscopy and AI chromoendoscopy findings of these lesions before treatment were prospectively analyzed. The border distinction between the lesion and surrounding mucosa was classified as distinct or indistinct on conventional endoscopy and AI chromoendoscopy, respectively. Mucin phenotypes were classified as gastric, intestinal, gastrointestinal, or null type by immunohistochemistry.

Results: The lesion borders were distinct in 46.8% (59/126) of the lesions assessed using conventional endoscopy and in 73.0% (92/126) of those assessed with AI chromoendoscopy (p < 0.001). The border distinction rate of differentiated-type cancers on AI chromoendoscopy was significantly higher than that on conventional endoscopy (66/71 [93.0%] vs. 34/71 [47.9%], p < 0.001), but the border distinction rate of undifferentiated-type cancers on AI chromoendoscopy was not different from that on conventional endoscopy (26/55 [47.3%] vs. 25/55 [45.5%], p = 0.848). Compared with conventional endoscopy, AI chromoendoscopy identified borders in a significantly higher percentage of gastric, intestinal, and gastrointestinal mucin types; however, there was no difference in AI chromoendoscopy findings according to the mucin phenotype (p = 0.271).

Conclusion: AI chromoendoscopy was effective in horizontal border delineation in differentiated-type EGCs, but not in undifferentiated-type EGCs. Mucin phenotype had no effect on border distinction using AI chromoendoscopy.