Usefulness of magnifying endoscopy with acetic acid and narrow-band imaging for the diagnosis of duodenal neoplasms: proposal of a diagnostic algorithm

Background This study aimed to clarify the features of superficial non-ampullary duodenal epithelial tumors (SNADETs) on magnifying endoscopy with narrow-band imaging (M-NBI) and magnifying endoscopy with acetic acid and narrow-band imaging (M-AANBI), and evaluate the efficacy of M-NBI/M-AANBI to distinguish high-grade adenomas or adenocarcinomas (HGA/AC) from low-grade adenomas (LGA). Methods Clinicopathological data on 62 SNADETs in 58 patients who underwent preoperative M-NBI/M-AANBI and endoscopic resection were retrospectively reviewed. The pathological results were classified into two categories, LGA and HGA/AC. We evaluated microvascular patterns (MVPs) and microsurface patterns (MSPs) observed by M-NBI and MSPs observed by M-AANBI for characterizing LGA and HGA/AC. The kappa value was calculated to assess the interobserver and intraobserver agreements of evaluation of M-AANBI images. Results Pathologically, 38 lesions (61.3%) were LGA and 24 lesions (38.7%) were HGA/AC. HGA/AC tended to have irregular MVP and/or MSP on M-NBI. M-NBI diagnostic performance to distinguish HGA/AC from LGA showed 62.5% sensitivity, 68.4% specificity, and 66.1% accuracy. SNADETs had irregular MSP on M-AANBI. Three irregularity grades (iG) of MSP were observed by M-AANBI as follows: iG1, mild; iG2, moderate; iG3, significant. HGA/AC lesions had a significantly higher rate of iG3 than LGA lesions (p < 0.001). The iG2 was associated with HGA/AC in elevated lesions and LGA in depressed lesions. The diagnostic performance of M-AANBI was as follows: 95.8% sensitivity, 97.4% specificity, and 96.8% accuracy. The diagnostic accuracy of M-AANBI was significantly higher than that of M-NBI (p < 0.001). The kappa value for interobserver agreement on the diagnosis and irregularity grading of M-AANBI images was 0.742 and 0.719, respectively. These data indicate substantial interobserver agreement. Based on the above-mentioned results, we developed a M-AANBI diagnostic algorithm for SNADETs. Conclusion The diagnostic algorithm for SNADETs using M-AANBI may be useful for differentiating between LGA and HGA/AC.