Background/Aims The discrepancies between the diagnosis of preoperative endoscopic forceps biopsy (EFB) and endoscopic submucosal dissection (ESD) in patients with early gastric neoplasm (EGN) exist objectively. Among them, pathological upgrading… Click to show full abstract
Background/Aims The discrepancies between the diagnosis of preoperative endoscopic forceps biopsy (EFB) and endoscopic submucosal dissection (ESD) in patients with early gastric neoplasm (EGN) exist objectively. Among them, pathological upgrading directly influences the accuracy and appropriateness of clinical decisions. The aims of this study were to investigate the risk factors for the discrepancies, with a particular focus on pathological upgrading and to establish a prediction model for estimating the risk of pathological upgrading after EFB. Methods We retrospectively collected the records of 978 patients who underwent ESD from December 1, 2017 to July 31, 2021 and who had a final histopathology determination of EGN. A nomogram to predict the risk of pathological upgrading was constructed after analyzing subgroup differences among the 901 lesions enrolled. Results The ratio of pathological upgrading was 510 of 953 (53.5%). Clinical, laboratorial and endoscopic characteristics were analyzed using univariable and binary multivariable logistic regression analyses. A nomogram was constructed by including age, history of chronic atrophic gastritis, symptoms of digestive system, blood high density lipoprotein concentration, macroscopic type, pathological diagnosis of EFB, uneven surface, remarkable redness, and lesion size. The C-statistics were 0.804 (95% confidence interval, 0.774 to 0.834) and 0.748 (95% confidence interval, 0.664 to 0.832) in the training and validation set, respectively. We also built an online webserver based on the proposed nomogram for convenient clinical use. Conclusions The clinical value of identifying the preoperative diagnosis of EGN lesions is limited when using EFB separately. We have developed a nomogram that can predict the probability of pathological upgrading with good calibration and discrimination value.
               
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