A diagnostic prediction nomogram for small gastric stromal tumors based on features under endoscopic ultrasonography
10.3760/cma.j.cn321463-20220225-00004
- VernacularTitle:基于超声内镜下胃小间质瘤特征表现的诊断预测列线图模型建立
- Author:
Yan ZHANG
1
;
Ye CHEN
;
Huihui SUN
;
Ying CHEN
;
Jie XIONG
;
Shuchang XU
Author Information
1. 同济大学附属同济医院消化内科，上海200065
- Keywords:
Ultrasonography;
Nomogram;
Small gastric stromal tumors;
Diagnostic prediction model
- From:
Chinese Journal of Digestive Endoscopy
2023;40(2):115-120
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a nomogram based on features under endoscopic ultrasonography (EUS) for predicting the diagnosis of small gastric stromal tumors.Methods:The clinicopathological data of 189 patients with gastric submucosal tumors (diameter less than 2 cm) who underwent endoscopic resection at the Department of Gastroenterology, Tongji Hospital of Tongji University from June 2015 to August 2021 were retrospectively collected. All patients were divided into the modeling group ( n=126) and the validation group ( n=63) at 2∶1 by random function of software R. Independent influencing factors for the diagnosis of small gastric stromal tumors under EUS screened by univariable and multivariable logistic regression analysis were used to establish the diagnostic prediction nomogram. The receiver operator characteristic (ROC) curves were drawn to evaluate the discrimination of the model both in the modeling group and the validation group. Hosmer-Lemeshow test and calibration curve were used to evaluate the calibration of the model in both groups. Results:The age of patients >60 years ( OR=2.815, 95% CI:1.148-6.900, P=0.024), the lesions located in cardia/fundus ( OR=5.210, 95% CI:1.225-22.165, P=0.025), originated in muscularis propria ( OR=6.404, 95% CI:2.262-18.135, P<0.001) and of external growth ( OR=6.024, 95% CI:1.252-28.971, P=0.025) were independent influencing factors for the diagnosis of small gastric stromal tumors under EUS. The diagnostic prediction nomogram was established based on the four factors above. The areas under ROC curve of the modeling group and validation group were 0.834 (95% CI:0.765-0.903) and 0.780 (95% CI:0.667-0.893). Hosmer-Lemeshow test indicated that this model fit the data well ( χ2=10.23, P=0.176 in the modeling group; χ2=2.62, P=0.918 in the validation group). Calibration charts of the model drawn by Bootstrap method showed that the calibration curves fit well with the standard curves in both groups. Conclusion:The nomogram based on features under EUS for predicting the diagnosis of small gastric stromal tumors provides a visual reference for endoscopists to diagnose small gastric stromal tumors under EUS with good discrimination and calibration.
