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中国临床研究英文版:2024,37(9):1353-1358
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基于周围神经侵犯的列线图模型在预测Ⅰ~Ⅲ期早发性结直肠癌预后中的应用
(南京医科大学第二附属医院消化内科,江苏 南京 210011)
Application of peripheral nerve invasion based nomogram model in predicting the prognosis of stage Ⅰ-Ⅲ early-onset colorectal cancer
(Department of Gastroenterology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210011, China)
摘要
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Received:January 31, 2024   Published Online:September 20, 2024
中文摘要: 目的 评估周围神经侵犯(PNI)对Ⅰ~Ⅲ期早发性结直肠癌(EO-CRC)患者预后的影响,并构建基于PNI的列线图预测模型。方法 本研究分析了2010年至2015年美国监测、流行病学和最终结果(SEER)数据库中登记的50岁及以下5 920例EO-CRC患者数据。通过7∶3比例分为训练组(n=4 164)和验证组(n=1 756),运用Kaplan-Meier绘制生存曲线,Cox比例风险模型进行单变量及多变量预后分析。基于多变量模型显著变量构建列线图,通过受试者工作特征曲线(ROC)、校准曲线、决策曲线分析(DCA)评估其预测能力。结果 相较于PNI阴性患者,PNI阳性患者更年轻、病变位于左侧结肠、分化程度更低、TNM分期更高、接受化学治疗、CEA水平偏高(P<0.05)。Kaplan-Meier曲线提示PNI阳性组较PNI阴性组有更差的预后。COX单变量分析提示年龄、性别、肿瘤分化程度、TNM分期、T分期、N分期、是否接受放射治疗、是否接受化学治疗、CEA水平、肿瘤最大直径以及PNI状态为影响患者生存预后的变量(P<0.05)。多变量分析显示PNI是Ⅰ~Ⅲ期EO-CRC患者的独立预后指标(HR=1.73,95%CI:1.44~2.08,P<0.01)。基于PNI构建的列线图模型在 3年与5年生存期的ROC曲线下面积均超过0.7。结论 PNI可作为Ⅰ~Ⅲ期EO-CRC患者的独立预后标志,基于PNI的列线图模型可作为一个有效的预测预后工具。
Abstract:Objective To evaluate the impact of peripheral nerve aggression (PNI) on prognosis in patients with stage Ⅰ-Ⅲ early-onset colorectal cancer (EO-CRC) and to create a PNI-based nomogram prediction model. Methods This study analyzed data on 5 920 EO-CRC patients aged 50 years and younger enrolled in the U.S. Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. The patients were divided into the training group (n=4 164) and the validation group (n=1 756) by a ratio of 7∶3. Kaplan-Meier survival curve was drawn and Cox proportional risk model was used for univariate and multivariate prognosis analysis. Based on the significant variables of the multivariate model, a nomogram was constructed, and the predictive ability was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). Results Compared with PNI negative patients, PNI positive patients tended to be young, lesions located in the left colon, with poorer differentiation, higher TNM stage, chemotherapy intervention, and higher CEA level (P<0.05). Kaplan-Meier curve suggested that PNI positive group had a worse prognosis than PNI negative group. Cox univariate analysis suggested that age, sex, degree of tumor differentiation, TNM stage, T stage, N stage, whether to receive radiation therapy, whether to receive chemotherapy, CEA level, maximum tumor diameter and PNI status were variables affecting the survival and prognosis of patients. Multivariate analysis showed that PNI was an independent prognostic indicator for patients with stage Ⅰ to Ⅲ EO-CRC (HR=1.73, 95%CI: 1.44-2.08, P<0.01). The area under ROC curve of nomogram model based on PNI was more than 0.7 in both 3-year and 5-year lifetime. Conclusion PNI can be used as an independent prognostic marker in patients with stage Ⅰ-Ⅲ EO-CRC, and PNI-based nomogram model can be used as an effective prognostic tool.
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