Development of Deep Learning-Based Virtual Lugol Chromoendoscopy for Superficial Esophageal Squamous Cell Carcinoma.

Abstract

Lugol chromoendoscopy has been shown to increase the sensitivity of detection of esophageal squamous cell carcinoma (ESCC). We aimed to develop a deep learning-based virtual lugol chromoendoscopy (V-LCE) method. We developed still V-LCE images for superficial ESCC using a cycle-consistent generative adversarial network (CycleGAN). Six endoscopists graded the detection and margins of ESCCs using white-light endoscopy (WLE), real lugol chromoendoscopy (R-LCE), and V-LCE on a five-point scale ranging from 1 (poor) to 5 (excellent). We also calculated and compared the color differences between cancerous and non-cancerous areas using WLE, R-LCE, and V-LCE. Scores for the detection and margins were significantly higher with R-LCE than V-LCE (detection, 4.7 vs. 3.8, respectively; p < 0.001; margins, 4.3 vs. 3.0, respectively; p < 0.001). There were nonsignificant trends towards higher scores with V-LCE than WLE (detection, 3.8 vs. 3.3, respectively; p = 0.089; margins, 3.0 vs. 2.7, respectively; p = 0.130). Color differences were significantly greater with V-LCE than WLE (p < 0.001) and with R-LCE than V-LCE (p < 0.001) (39.6 with R-LCE, 29.6 with V-LCE, and 18.3 with WLE). Our V-LCE has a middle performance between R-LCE and WLE in terms of lesion detection, margin, and color difference. It suggests that V-LCE potentially improves the endoscopic diagnosis of superficial ESCC.