Objective: Peripheral nerve invasion (PNI) is associated with local recurrence and poor prognosis in patients with advanced gastric cancer. A risk-assessment model based on preoperative indicators for predicting PNI of gastric cancer may help to formulate a more reasonable and accurate individualized diagnosis and treatment plan. Methods: Inclusion criteria: (1) electronic gastroscopy and enhanced CT examination of the upper abdomen were performed before surgery; (2) radical gastric cancer surgery (D2 lymph node dissection, R0 resection) was performed; (3) no distant metastasis was confirmed before and during operation; (4) postoperative pathology showed an advanced gastric cancer (T2-4aN0-3M0), and the clinical data was complete. Those who had other malignant tumors at the same time or in the past, and received neoadjuvant radiochemotherapy or immunotherapy before surgery were excluded. In this retrospective case-control study, 550 patients with advanced gastric cancer who underwent curative gastrectomy between September 2017 and June 2019 were selected from the Affiliated Hospital of Qingdao University for modeling and internal verification, including 262 (47.6%) PNI positive and 288 (52.4%) PNI negative patients. According to the same standard, clinical data of 50 patients with advanced gastric cancer who underwent radical surgery from July to November 2019 in Qingdao Municipal Hospital were selected for external verification of the model. There were no statistically significant differences between the clinical data of internal verification and external verification (all P>0.05). Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for PNI in advanced gastric cancer, and the clinical indicators with statistically significant difference were used to establish a preoperative nomogram model through R software. The Bootstrap method was applied as internal verification to show the robustness of the model. The discrimination of the nomogram was determined by calculating the average consistency index (C-index). The calibration curve was used to evaluate the consistency of the predicted results with the actual results. The Hosmer-Lemeshow test was used to examine the goodness of fit of the discriminant model. During external verification, the corresponding C-index index was also calculated. The area under ROC curve (AUC) was used to evaluate the predictive ability of the nomogram in the internal verification and external verification groups. Results: A total of 550 patients were identified in this study, 262 (47.6%) of which had PNI. Multivariate logistic regression analysis revealed that carcinoembryonic antigen level ≥ 5 μg/L (OR=5.870, 95% CI: 3.281-10.502, P<0.001), tumor length ≥5 cm (OR=5.539,95% CI: 3.165-9.694, P<0.001), mixed Lauren classification (OR=2.611, 95%CI: 1.272-5.360, P=0.009), cT3 stage (OR=13.053, 95% CI: 5.612-30.361, P<0.001) and the presence of lymph node metastasis (OR=4.826, 95% CI: 2.729-8.533, P<0.001) were significant independent risk factors of PNI in advanced gastric cancer (all P<0.05). Based on these results, diffused Lauren classification and cT4 stage were included to establish a predictive nomogram model. CEA ≥ 5 μg/L was for 68 points, tumor length ≥ 5 cm was for 67 points, mixed Lauren classification was for 21 points, diffused Lauren classification was for 38 points, cT3 stage was for 75 points, cT4 stage was for 100 points, and lymph node metastasis was for 62 points. Adding the scores of all risk factors was total score, and the probability corresponding to the total score was the probability that the model predicted PNI in advanced gastric cancer before surgery. The internal verification result revealed that the AUC of nomogram was 0.935, which was superior than that of any single variable, such as CEA, Lauren classification, cT stage, tumor length and lymph node metastasis (AUC: 0.731, 0.595, 0.838, 0.757 and 0.802, respectively). The external verification result revealed the AUC of nomogram was 0.828. The C-ndex was 0.931 after internal verification. External verification showed a C-index of 0.828 from the model. The calibration curve showed that the predictive results were good in accordance with the actual results (P=0.415). Conclusion: A nomogram model constructed by CEA, tumor length, Lauren classification (mixed, diffuse), cT stage, and lymph node metastasis can predict the PNI of advanced gastric cancer before surgery.
目的： 对于进展期胃癌患者，周围神经侵犯（PNI）与局部复发和预后不良有关，故建立术前预测胃癌PNI的风险评估模型，能够为制定更加合理、准确的个体化诊疗方案提供参考。 方法： 纳入标准：（1）术前均行电子胃镜和上腹部增强CT检查；（2）行胃癌根治性手术（D(2)淋巴结清扫，R(0)切除）；（3）术前及术中均证实无远处转移；（4）术后病理确诊为进展期胃癌（T(2~4)aN(0~3)M(0)），且临床资料完整。排除同时或既往有其他恶性肿瘤、术前接受新辅助放化疗或免疫治疗者。采用回顾性病例对照研究方法，收集青岛大学附属医院2017年9月至2019年6月接受根治性手术的550例进展期胃癌患者临床资料，用于建模及内部验证，包括262例（47.6%）PNI阳性和288例（52.4%）PNI阴性患者。按照同样标准，选取青岛市立医院2019年7—11月接受根治性手术的50例进展期胃癌患者临床资料进行模型的外部验证。内部验证与外部验证患者临床资料的比较，差异均无统计学意义（均P>0.05）。采用单因素分析和多因素Logistic回归分析方法，确定进展期胃癌PNI的独立危险因素，将差异有统计学意义的临床指标通过R软件建立列线图模型。采用Bootstrap法重复抽样进行内部验证，并计算一致性指数（C-index）评价模型的区分度。绘制校准曲线评估预测结果与实际结果的一致性，使用Hosmer-Lemeshow检验判别模型的拟合效果。外部验证时，计算相应的C-index指数。利用受试者工作特征曲线（ROC）及曲线下面积（AUC）评估内部验证及外部验证组中列线图模型的预测效能。 结果： 全组550例患者中，PNI阳性者262例（47.6%）。多因素分析结果显示，癌胚抗原水平≥5 μg/L（OR=5.870，95% CI：3.281~10.502，P<0.001）、肿瘤长径≥5 cm（OR=5.539，95% CI：3.165~9.694，P<0.001）、Lauren分型为混合型（OR=2.611，95% CI：1.272~5.360，P=0.009）、cT(3)期（OR=13.053，95% CI：5.612~30.361，P<0.001）以及存在淋巴结转移（OR=4.826，95% CI：2.729~8.533，P<0.001）是进展期胃癌发生PNI的独立危险因素（均P<0.05）。依据此结果，并纳入Lauren分型（弥漫型）和cT(4)期，建立列线图预测模型，癌胚抗原水平≥5 μg/L为68分、肿瘤长径≥5 cm为67分、Lauren分型（混合型为21分、弥漫型为38分）、cT(3)期为75分、cT(4)期为100分、有淋巴结转移为62分。将所有危险因素的评分相加，得到总分对应的概率即为该模型术前预测进展期胃癌PNI的概率。绘制受试者工作特征曲线，内部验证显示，列线图预测模型AUC值为0.935，优于癌胚抗原、Lauren分型、T分期、肿瘤长径淋巴结转移的AUC值（分别为0.731、0.595、0.838、0.757和0.802）；外部验证显示，列线图预测模型AUC值为0.828。内部验证显示模型的C-index值为0.931，外部验证结果显示模型的C-index值为0.828。校正曲线显示预测结果与实际结果之间具有良好的一致性（P=0.415）。 结论： 由癌胚抗原、肿瘤大小、Lauren分型（混合型、弥漫型）、cT分期和淋巴结转移构建的列线图模型可在术前预测进展期胃癌PNI。.
Keywords: Gastric neoplasms,advanced; Perineural invasion; Predictive nomogram model.