DESIGN AND ANALYSIS OF IODINE STAINING AND NBI IN THE DIAGNOSTIC MODEL AND DIAGNOSIS OF EARLY ESOPHAGEAL CANCER AND ITS PRECANCEROUS LESIONS.

Objective: This study aimed to compare the differences between narrowband imaging (NBI) and Lugo's iodine staining endoscopy (LCE) for detecting and elucidating the site, clarity, and diagnostic accuracy of early esophageal cancer and precancerous lesions. Methods: We included patients with a high risk of developing esophageal cancer who visited the Zhongjiang County People's Hospital between October 2020 and October 2022. Endoscopic examination was performed on each study participant. White-light endoscopy was used to observe and locate the diseased mucosa, after which NBI mode and LCE staining were used to observe the boundary between the diseased and normal mucosa. Abnormal lesions were found, and biopsies were performed on the identifiable diseased parts for pathological examination. Inflammation, LGIN, HGIN, and early esophageal cancer were diagnosed, and the differences between them were compared in terms of the two enhanced-image endoscopy techniques. A semantic segmentation model based on deep learning was adopted to assist in the diagnosis of early esophageal cancer and accurately locate cancerous areas. In order to improve its accurate diagnostic rate, we also built a semantic segmentation network model to assist in the computer-aided diagnosis of early esophageal cancer. Results: A total of 69 cases of early esophageal cancer were included in this study. The patients were aged 40–75 years, with an average age of 6 2 ± 2. 0 years. Most early esophageal cancer lesions were located in the middle of the esophagus in 45/69 (65.2%) cases. LCE obtained higher clarity of lesion boundaries than NBI (59.4% versus 45.0% and 27.7% versus 15.9%, respectively), and the proportion of unclear lesions was lower (8.6% versus 21.7% and 4.3% versus 17.4%, respectively; p < 0. 0 5). For early esophageal cancer and precancerous lesions, the missed-diagnosis rate of white-light endoscopy was 20%, that of NBI was 3.15%, and that of LCE was 0%, with statistical significance (p < 0. 0 5). The rate of missed diagnosis of LGIN (three cases) by NBI was 3.15%, which was not significantly different from that of LCE (p > 0. 0 5). We found that the lesion area could be more accurately determined using deep learning models to segment NBI images. By constructing a deep learning model for the diagnosis and classification of esophageal cancer, its diagnostic rate rose to 99.5%. Conclusion: The age range of patients with early esophageal cancer in Zhongjiang County, Sichuan Province, was 40–75 years, and tumors mostly occurred in the middle of the esophagus. The boundary clarity of early esophageal carcinomas was higher on LCE than that on NBI. The diagnostic accuracies of NBI and LCE are much higher than those of conventional white-light endoscopy. Therefore, both LCE and NBI are helpful for detecting early esophageal cancer and precancerous lesions. Moreover, the diagnostic rate of clinical esophageal cancer can be effectively improved using a dedicated deep learning network model. [ABSTRACT FROM AUTHOR]