Department of General Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China.
Department of General Surgery, Xinyang Central Hospital, Xin Yang, 464000, Henan Province, China.
BMC Surg. 2020 Nov 25;20(1):298. doi: 10.1186/s12893-020-00969-4.
Liver metastasis (LIM) of gastrointestinal stromal tumor (GIST) is associated with poor prognosis. The present study aimed at developing and validating nomogram to predict LIM in patients with GIST, thus helping clinical diagnosis and treatment.
The data of GIST patients derived from Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016, which were then screened by univariate and multivariate logistic regression for the construction of LIM nomogram. The model discrimination of LIM nomogram was evaluated by concordance index (C-index) and calibration plots, while the predictive accuracy and clinical values were measured by decision curve analysis (DCA) and clinical impact plot. Furthermore, we validated predictive nomogram in the internal testing set.
A total of 3797 patients were enrolled and divided randomly into training and validating groups in a 3-to-1 ratio. After logistic regression, the significant variables were sex, tumor location, tumor size, N stage and mitotic rate. The calibration curves showed the perfect agreement between nomogram predictions and actual observations, while the DCA and clinical impact plot showed the clinical utility of LIM nomogram. C-index of the nomogram was 0.812. What's more, receiver operating characteristic curves (ROC) also showed good discrimination and calibration in the training set (AUC = 0.794, 95% CI 0.778-0.808) and the testing set (AUC = 0.775, 95% CI 0.748-0.802).
The nomogram for patients with GIST can effectively predict the individualized risk of liver metastasis and provide insightful information to clinicians to optimize therapeutic regimens.
胃肠道间质瘤（GIST）的肝转移（LIM）与预后不良相关。本研究旨在开发和验证用于预测 GIST 患者 LIM 的列线图，从而帮助临床诊断和治疗。
从 2010 年至 2016 年，从监测、流行病学和最终结果（SEER）数据库中获取 GIST 患者的数据，然后通过单变量和多变量逻辑回归进行筛选，以构建 LIM 列线图。通过一致性指数（C 指数）和校准图评估 LIM 列线图的模型区分度，通过决策曲线分析（DCA）和临床影响图测量预测准确性和临床价值。此外，我们还在内部测试集中验证了预测列线图。
共纳入 3797 例患者，并以 3:1 的比例随机分为训练组和验证组。经过逻辑回归，显著变量为性别、肿瘤位置、肿瘤大小、N 分期和有丝分裂率。校准曲线显示列线图预测与实际观察之间具有完美的一致性，而 DCA 和临床影响图显示了 LIM 列线图的临床实用性。该列线图的 C 指数为 0.812。此外，训练集（AUC=0.794，95%CI 0.778-0.808）和测试集（AUC=0.775，95%CI 0.748-0.802）的 ROC 曲线也显示出良好的区分度和校准度。
用于 GIST 患者的列线图可有效预测肝转移的个体化风险，并为临床医生提供有价值的信息以优化治疗方案。
