[A nomogram for predicting lymph node metastasis in early gastric cancer].

Objective: To explore the independent risk factors of lymph node metastasis (LNM) in early gastric cancer, and to use nomogram to construct a prediction model for above LNM. Methods: A retrospective cohort study was conducted. Inclusion criteria: (1) primary early gastric cancer as stage pT1 confirmed by postoperative pathology; (2) complete clinicopathological data. Exclusion criteria: (1) patients with advanced gastric cancer, stump gastric cancer or history of gastrectomy; (2) early gastric cancer patients confirmed by pathology after neoadjuvant chemotherapy; (3) other types of gastric tumors, such as lymphoma, neuroendocrine tumor, stromal tumor, etc.; (4) primary tumors of other organs with gastric metastasis. According to the above criteria, 1633 patients with early gastric cancer who underwent radical gastrectomy at the Department of General Surgery of the Chinese PLA General Hospital First Medical Center from December 2005 to December 2020 were enrolled as training set, meanwhile 239 patients with early gastric cancer who underwent gastrectomy at the Department of General Surgery of the Chinese PLA General Hospital Fourth Medical Center from December 2015 to December 2020 were enrolled as external validation set. Risk factors of LNM in early gastric cancer were identified by using univariate and multivariate logistic regression analyses. A nomogram prediction model was established with significant factors screened by multivariate analysis. Area under the receiver operating characteristic curve (AUC) was used for assessing the predictive value of the model. Calibration curve was drawn for external validation. Results: Among 1633 patients in training set, the mean number of retrieved lymph nodes was 20 (13-28), and 209 patients (12.8%) had lymph node metastasis. Univariate analysis showed that gender, resection range, tumor location, tumor morphology, lymph node clearance, vascular invasion, lymphatic cancer thrombus, tumor length, tumor differentiation, microscopic presence of signet ring cells and depth of tumor invasion were associated with LNM (all P<0.05). Multivariate analysis revealed that females, tumor morphology as ulcer type, vascular invasion, lymphatic cancer thrombus, tumor length≥3 cm, deeper invasion of mucosa, and poor differentiation were independent risk factors for LNM in early gastric cancers (all P<0.05). Receiver operating characteristic curve indicated that AUC of training set was 0.818 (95%CI: 0.790-0.847) and AUC of external validation set was 0.765 (95%CI: 0.688-0.843). The calibration curve showed that the LNM probability predicted by nomogram was consistent with the actual situation (C-index: 0.818 in training set and 0.765 in external validation set). Conclusions: Females, tumor morphology as ulcer type, vascular invasion, lymphatic cancer thrombus, tumor length≥3 cm, deeper invasion of mucosa and poor differentiation are independent risk factors for LNM of early gastric cancer. The establishment of a nomogram prediction model for LNM in early gastric cancer has great diagnostic value and can provide reference for treatment selection.目的： 探究早期胃癌淋巴结转移的危险因素，并应用诺曼图构建预测模型。 方法： 采用回顾性队列研究的方法。病例纳入标准：（1）术后病理分期为pT(1)期原发性早期胃癌患者；（2）临床病理资料完整。排除标准：（1）进展期胃癌、残胃癌或既往行胃切除术的患者；（2）行新辅助化疗后病理分期为早期的胃癌患者；（3）其他类型胃肿瘤如淋巴瘤、神经内分泌肿瘤、胃肠间质瘤等；（4）其他脏器原发性肿瘤胃转移患者。根据以上标准，收集2005年12月至2020年12月期间，于解放军总医院第一医学中心普通外科医学部行胃癌根治术的1 633例早期胃癌患者作为训练集；按照同样标准，收集解放军总医院第四医学中心普通外科同一时间段行胃癌根治术的239例早期胃癌患者临床病理资料作为外部验证集。采用单因素及多因素Logistic回归方式筛选早期胃癌淋巴结转移的危险因素，将多因素分析有意义指标构建诺曼图预测模型，并通过受试者工作特征（ROC）曲线下面积（AUC）和校准曲线对其有效性进行评价。 结果： 1 633例训练集患者中，平均淋巴结清扫数目20（13~28）枚，209例（12.8%）出现淋巴结转移。单因素分析结果显示，患者性别、肿瘤切除范围、肿瘤位置、肿瘤形态、淋巴结清扫数、脉管浸润、淋巴管癌栓、肿瘤长径、肿瘤分化程度、镜下是否含有印戒细胞以及肿瘤浸润深度与本组患者淋巴结转移有关（均P<0.05）。多因素分析结果显示，女性、肿瘤形态为溃疡型、有脉管浸润和淋巴管癌栓、肿瘤长径≥3 cm、肿瘤浸润深度较深和低分化均为早期胃癌淋巴结转移的独立危险因素（均P<0.05）。绘制ROC曲线结果提示，训练集AUC为0.818（95%CI：0.790~0.847），外部验证集AUC为0.765（95%CI：0.688~0.843）。校准曲线显示，诺曼图预测的淋巴结转移概率与实际情况相符（训练集C-index：0.818，外部验证C-index：0.765）。 结论： 训练集女性、肿瘤形态为溃疡型、有脉管浸润和淋巴管癌栓、肿瘤长径≥3 cm，浸润黏膜下层和低分化者淋巴结转移危险性高。通过列线图构建早期胃癌淋巴结转移临床预测模型，具有较高诊断价值，可为临床治疗方式的选择提供参考。.