Liu Tao, Mi Junli, Wang Yafeng, Qiao Wenjie, Wang Chenxiang, Ma Zhijun, Wang Cheng
Department of Gastrointestinal Oncology, Qinghai University Affiliated Hospital, Xining, China.
The Graduate School of Qinghai University, Xining, China.
Front Med (Lausanne). 2022 Oct 28;9:1022595. doi: 10.3389/fmed.2022.1022595. eCollection 2022.
Establishing a risk model of the survival situation of appendix cancer for accurately identifying high-risk patients and developing individualized treatment plans.
A total of 4,691 patients who were diagnosed with primary appendix cancer from 2010 to 2016 were extracted using Surveillance, Epidemiology, and End Results (SEER) Stat software. The total sample size was divided into 3,283 cases in the modeling set and 1,408 cases in the validation set at a ratio of 7:3. A nomogram model based on independent risk factors that affect the prognosis of appendix cancer was established. Single-factor Cox risk regression, Lasso regression, and multifactor Cox risk regression were used for analyzing the risk factors that affect overall survival (OS) in appendectomy patients. A nomogram model was established based on the independent risk factors that affect appendix cancer prognosis, and the receiver operating characteristic curve (ROC) curve and calibration curve were used for evaluating the model. Survival differences between the high- and low-risk groups were analyzed through Kaplan-Meier survival analysis and the log-rank test. Single-factor Cox risk regression analysis found age, ethnicity, pathological type, pathological stage, surgery, radiotherapy, chemotherapy, number of lymph nodes removed, T stage, N stage, M stage, tumor size, and CEA all to be risk factors for appendiceal OS. At the same time, multifactor Cox risk regression analysis found age, tumor stage, surgery, lymph node removal, T stage, N stage, M stage, and CEA to be independent risk factors for appendiceal OS. A nomogram model was established for the multifactor statistically significant indicators. Further stratified with corresponding probability values based on multifactorial Cox risk regression, Kaplan-Meier survival analysis found the low-risk group of the modeling and validation sets to have a significantly better prognosis than the high-risk group ( < 0.001).
The established appendix cancer survival model can be used for the prediction of 1-, 3-, and 5-year OS and for the development of personalized treatment options through the identification of high-risk patients.
建立阑尾癌生存情况的风险模型，以准确识别高危患者并制定个体化治疗方案。
使用监测、流行病学和最终结果（SEER）统计软件提取2010年至2016年期间共4691例诊断为原发性阑尾癌的患者。总样本量按7:3的比例分为建模集3283例和验证集1408例。基于影响阑尾癌预后的独立危险因素建立列线图模型。采用单因素Cox风险回归、Lasso回归和多因素Cox风险回归分析影响阑尾切除术患者总生存（OS）的危险因素。基于影响阑尾癌预后的独立危险因素建立列线图模型，并使用受试者工作特征曲线（ROC）和校准曲线评估该模型。通过Kaplan-Meier生存分析和对数秩检验分析高危组和低危组之间的生存差异。单因素Cox风险回归分析发现年龄、种族、病理类型、病理分期、手术、放疗、化疗、切除淋巴结数量、T分期、N分期、M分期、肿瘤大小和癌胚抗原（CEA）均为阑尾OS的危险因素。同时，多因素Cox风险回归分析发现年龄、肿瘤分期、手术、淋巴结切除、T分期、N分期、M分期和CEA是阑尾OS的独立危险因素。为多因素具有统计学意义的指标建立列线图模型。基于多因素Cox风险回归进一步按相应概率值分层，Kaplan-Meier生存分析发现建模集和验证集的低危组预后明显优于高危组（<0.001）。
所建立的阑尾癌生存模型可用于预测1年、3年和5年OS，并通过识别高危患者制定个性化治疗方案。
