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

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.