• Yunqing Zeng, Jinhou Li, Yuan Zheng, Di Zhang, Ning Zhong, Xiuli Zuo, Yanqing Li, Wenbin Yu, and Jiaoyang Lu.
• Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
• Ann. Med. 2024 Dec 1; 56 (1): 23915362391536.

BackgroundSubmucosal fibrosis is associated with adverse events of endoscopic submucosal dissection (ESD). The present study mainly aimed to establish a predictive model for submucosal fibrosis in patients with early gastric cancer (EGC) undergoing ESD.MethodsEligible patients with EGC, identified at Qilu Hospital of Shandong University from April 2013 to December 2023, were retrospectively included and randomly split into a training set and a validation set in a 7:3 ratio. Logistic regression analyses were used to pinpoint the risk factors for submucosal fibrosis. A nomogram was developed and confirmed using receiver operating characteristic (ROC) curves, calibration plots, Hosmer-Lemeshow (H-L) tests, and decision curve analysis (DCA) curves. Besides, a predictive model for severe submucosal fibrosis was further conducted and tested.ResultsA total of 516 cases in the training group and 220 cases in the validation group were recruited. The nomogram for submucosal fibrosis contained the following items: tumour location (long axis), tumour location (short axis), ulceration, and biopsy pathology. ROC curves showed high efficiency with an area under the ROC of 0.819 in the training group, and 0.812 in the validation group. Calibration curves and H-L tests indicated good consistency. DCA proved the nomogram to be clinically beneficial. Furthermore, the four items were also applicable for a nomogram predicting severe fibrosis, and the model performed well.ConclusionThe predictive models, initially constructed in this study, were validated as convenient and feasible for endoscopists to predict submucosal fibrosis and severe fibrosis in patients with EGC undergoing ESD.

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