Lin Jiejun, Su Huang, Zhou Qingjie, Pan Jie, Zhou Leying
Department of Gastroenterology, Wenzhou Central Hospital, Wenzhou, China.
Scand J Gastroenterol. 2022 May;57(5):574-580. doi: 10.1080/00365521.2021.2023626. Epub 2022 Jan 7.
It is important to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the selection of the best treatment strategy and the prognosis of patients. The study aimed to evaluate the utility of a predictive nomogram based on the Kyoto classification of gastritis for GC.
It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2019 to November 2019 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using the bootstrap method. A nomogram was formulated according to multivariate analysis of the training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by the validation set.
Among all patients enrolled, 102 of 2636 cases showed LGIN, HGIN and gastric cancer pathology results, whereas the rest cases showed benign pathological results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classification scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. The C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence in the validation set.
The nomogram formulated was proven to be of high predictive value for GC.
对于内镜医师而言，预测胃癌（GC）风险至关重要，因为早期发现胃癌决定了最佳治疗策略的选择以及患者的预后。本研究旨在评估基于胃炎京都分类法的预测列线图对胃癌的应用价值。
这是一项回顾性研究，纳入了2019年1月至2019年11月在温州中心医院消化内科内镜中心接受食管胃十二指肠镜检查和血清胃蛋白酶原（PG）检测的2639例患者。进行常规活检以病理确定良性和恶性病变。采用自助法将所有病例随机分为训练集（70%）和验证集（30%）。根据训练集的多因素分析制定列线图。通过一致性指数（C指数）、受试者操作特征曲线（ROC）的曲线下面积（AUC）以及校准曲线评估列线图的预测准确性和判别能力，并在验证集中进行验证。
在所有纳入患者中，2636例中有102例显示低级别上皮内瘤变、高级别上皮内瘤变和胃癌病理结果，其余病例显示良性病理结果。多因素分析表明，年龄、性别、PG I/II比值和京都分类评分是胃癌的独立预测变量。训练集列线图的C指数为0.79（95%CI：0.74至0.84），ROC的AUC为0.79。列线图的校准曲线显示预测概率与胃癌风险的观察概率之间具有最佳一致性。验证集中C指数为0.86（95%CI：0.79至0.94），校准曲线的一致性更好。
所制定的列线图被证明对胃癌具有较高的预测价值。
