Nomogram for pre-procedural prediction of non-curative endoscopic resection in patients with early gastric cancer

BackgroundNon-curative resection (non-CR) after endoscopic submucosal dissection (ESD) requires additional surgery due to the possibility of lymph node metastasis (LNM). Therefore, it is important to accurately predict the risk of non-CR to avoid unnecessary preoperative procedures. Thus, we aimed to develop and verify a nomogram to predict the risk of non-CR prior to ESD.MethodsPatients who underwent ESD for early gastric cancer (EGC) were divided into CR and non-CR groups based on the present ESD criteria. The pre-procedural factors, such as endoscopic features, radiologic findings, and pathology of the lesion, were compared between the groups to identify the risk factors associated with non-CR. A nomogram was developed using multivariate analysis, and its predictive value was assessed using an external validation group.ResultsAmong 824 patients, 682 were curative (82.7%) and 142 were non-curative (17.3%). By comparing two groups, endoscopic features including redness, whitish mucosal change, fold convergence, and large lesion size; histologic features such as moderately or poorly differentiated or signet ring cell carcinoma; and abnormal CT findings including non-specific lymph node enlargement and fold thickening were identified as significant predictors of non-CR. The nomogram was developed based on these predictors and showed good predictive performance in the external validation, with an area under the curve of 0.87.ConclusionsWe developed a nomogram to predict the risk of non-CR prior to ESD. These predictive factors in addition to the existing ESD criteria can help provide the best treatment option for patients with EGC.

Automated summary

By analyzing the characteristics of patients with early gastric cancer who underwent ESD, we developed a nomogram to predict the risk of non-CR. This nomogram includes endoscopic features, radiologic findings, and pathology results, and it showed good predictive performance in the external validation.