Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, 17 Yongwaizheng Street, Nanchang, 330006, Jiangxi, China.
Jiangxi Clinical Research Center for Gastroenterology, Nanchang, Jiangxi, China.
Surg Endosc. 2024 Apr;38(4):1933-1943. doi: 10.1007/s00464-024-10674-5. Epub 2024 Feb 9.
Gastrointestinal stromal tumors (GIST) carry a potential risk of malignancy, and the treatment of GIST varies for different risk levels. However, there is no systematic preoperative assessment protocol to predict the malignant potential of GIST. The aim of this study was to develop a reliable and clinically applicable preoperative nomogram prediction model to predict the malignant potential of gastric GIST.
Patients with a pathological diagnosis of gastric GIST from January 2015 to December 2021 were screened retrospectively. Univariate and multivariate logistic analyses were used to identify independent risk factors for gastric GIST with high malignancy potential. Based on these independent risk factors, a nomogram model predicting the malignant potential of gastric GIST was developed and the model was validated in the validation group.
A total of 494 gastric GIST patients were included in this study and allocated to a development group (n = 345) and a validation group (n = 149). In the development group, multivariate logistic regression analysis revealed that tumor size, tumor ulceration, CT growth pattern and monocyte-to- lymphocyte ratio (MLR) were independent risk factors for gastric GIST with high malignancy potential. The AUC of the model were 0.932 (95% CI 0.890-0.974) and 0.922 (95% CI 0.868-0.977) in the development and validation groups, respectively. The best cutoff value for the development group was 0.184, and the sensitivity and specificity at this value were 0.895 and 0.875, respectively. The calibration curves indicated good agreement between predicted and actual observed outcomes, while the DCA indicated that the nomogram model had clinical application.
Tumor size, tumor ulceration, CT growth pattern and MLR are independent risk factors for high malignancy potential gastric GIST, and a nomogram model developed based on these factors has a high ability to predict the malignant potential of gastric GIST.
胃肠道间质瘤（GIST）具有恶性潜能风险，其治疗因风险水平而异。然而，目前尚无系统的术前评估方案来预测 GIST 的恶性潜能。本研究旨在建立一种可靠且临床适用的术前列线图预测模型，以预测胃 GIST 的恶性潜能。
回顾性筛选了 2015 年 1 月至 2021 年 12 月期间经病理诊断为胃 GIST 的患者。采用单因素和多因素逻辑回归分析确定胃 GIST 恶性潜能高的独立危险因素。基于这些独立危险因素，建立了预测胃 GIST 恶性潜能的列线图模型，并在验证组中进行验证。
本研究共纳入 494 例胃 GIST 患者，分为开发组（n=345）和验证组（n=149）。在开发组中，多因素逻辑回归分析显示，肿瘤大小、肿瘤溃疡、CT 生长模式和单核细胞-淋巴细胞比值（MLR）是胃 GIST 恶性潜能高的独立危险因素。模型在开发组和验证组中的 AUC 分别为 0.932（95%CI 0.890-0.974）和 0.922（95%CI 0.868-0.977）。开发组的最佳截断值为 0.184，该值的敏感性和特异性分别为 0.895 和 0.875。校准曲线表明预测结果与实际观察结果之间具有良好的一致性，而 DCA 表明该列线图模型具有临床应用价值。
肿瘤大小、肿瘤溃疡、CT 生长模式和 MLR 是胃 GIST 恶性潜能高的独立危险因素，基于这些因素建立的列线图模型具有较高的预测胃 GIST 恶性潜能的能力。
