Establishment and clinical validation of a predictive scoring system for malignant gastric stromal tumors based on endoscopic and endoscopic ultrasound findings
10.3760/cma.j.cn321463-20240415-00054
- VernacularTitle:基于胃镜及超声内镜下表现预测胃间质瘤恶性潜能评分系统的建立和临床验证
- Author:
Ling LIU
1
;
Yang LI
;
Yangyang JIANG
;
Suyan QIU
;
Ying ZHOU
;
Jie SU
;
Juanjuan HUANG
;
Yiwei FU
;
Tingsheng LING
Author Information
1. 南京中医药大学，南京 210029
- Keywords:
Nomograms;
Gastric stromal tumors;
Malignant potential
- From:
Chinese Journal of Digestive Endoscopy
2024;41(8):633-639
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a scoring system for preoperative prediction of the malignant potential of gastric stromal tumors based on gastroscopic and endoscopic ultrasound features, along with validation.Methods:A total of 286 patients who were treated in Jiangsu Province Hospital of Chinese Medicine from January 1, 2017 to December 31, 2023 and diagnosed as having gastric stromal tumors by postoperative pathology were enrolled in the modeling group. According to National Institutes of Health classification system, 227 very-low/low-risk patients were classified into the low malignant potential (LMP) group, and the 59 intermediate/high-risk patients into the high malignant potential (HMP) group. LASSO regression analysis was performed to identify predictive factors for HMP gastric stromal tumors, and a nomogram prediction model was developed. Internal validation using the Bootstrap method was performed on the modeling group, and external validation was performed on data from 85 patients who were treated and diagnosed as having gastric stromal tumors by postoperative pathology in Taizhou People's Hospital from January 1, 2021 to December 31, 2023. The receiver operator characteristic (ROC) curves, calibration curves, and decision curve analyses were employed in both the modeling and external validation groups.Results:Tumor size (coef=0.755), tumor shape (coef=0.015), tumor location (coef=0.008), growth pattern (coef=-0.026), cystic change (coef=0.685), and surface unceration change (coef=-0.545) were the independent predictive factors for HMP gastric stromal tumors. The nomogram-based prediction model constructed using these factors achieved an area under the ROC curve of 0.959 (95% CI: 0.898-0.903) in the modeling group and 0.959 (95% CI: 0.857-1.000) in the external validation group. The model demonstrated good accuracy (0.917) and a Kappa value of 0.737 in internal validation. Calibration curve and decision curve analyses indicated strong calibration and high net benefit in both the modeling and the external validation groups. Conclusion:Tumor size, tumor shape, tumor location, growth pattern, cystic change, and surface ulceration change are independent predictive factors for HMP gastric stromal tumors. The nomogram model developed based on these factors offers effective and convenient visualization for clinicians to predict the malignant potential of gastric stromal tumors preoperatively.
