基于常规检验指标构建区分结直肠腺瘤与结直肠癌的预测模型  

A Prediction Model for Colorectal Adenoma and Colorectal Cancer Based on Routine Test

作　　者：林俊生[1] 应紫灵 黄政渊[3] 祝先进[1] 曹颖平[1] 卢娉霞[1] LIN Junsheng;YING Ziling;HUANG Zhengyuan;ZHU Xianjin;CAO Yingping;LU Pingxia(Department of Laboratory Medicine,Fujian Medical University Union Hospital,Fuzhou 350001,China;Department of Laboratory Medicine,School of Medical Technology and Engineering,Fujian Medical University,Fuzhou 350122,China;Department of Emergency Surgery,Fujian Medical University Union Hospital,Fuzhou 350001,China)

机构地区：[1]福建医科大学附属协和医院检验科,福州350001 [2]福建医科大学医学技术与工程学院医学检验系,福州350122 [3]福建医科大学附属协和医院急诊外科,福州350001

出　　处：《肿瘤防治研究》2024年第5期353-360,共8页Cancer Research on Prevention and Treatment

基　　金：福建省科技创新联合资金项目(2020Y9088)。

摘　　要：目的回顾性分析结直肠腺瘤(CRA)患者与结直肠癌(CRC)患者的常规检验指标并建立预测模型。方法选取580例诊断为CRA患者117例和CRC患者463例,按照7∶3随机分为训练集406例、验证集174例。采用Logistic回归法建立模型,并绘制列线图。分别采用受试者工作特征曲线(ROC)、校准图、临床决策曲线(DCA)评估预测模型的区分度、校准度和临床应用的有效性。结果单因素Logistic回归分析初步筛选出13个候选变量,包括年龄、大便隐血试验(FOBT)、纤维蛋白原(FIB)、凝血酶时间(TT)、白蛋白(ALB)、白细胞计数(WBC)、中性粒细胞计数(NEUT#)、红细胞比积(HCT)、平均血红蛋白含量(MCH)、红细胞体积分布宽度(RDW)、血小板计数(PLT)、平均血小板体积(MPV)、活化部分凝血活酶时间(APTT)。多因素Logistic回归显示MPV、FIB、ALB、FOBT、TT以及HCT是CRA患者患CRC的危险因素(P<0.05)。构建预测模型,训练集和验证集发生CRC的ROC曲线下面积(AUC)分别为0.915和0.836。校准曲线显示,模型预测准确率较高,校正能力良好。DCA结果显示,当阈值概率为55%~95%时,训练集与验证集的净收益均大于2个极端模型,即临床有益。结论本研究构建的预测模型具有较好的区分度、校准度和临床应用的有效性,可作为区分CRA和CRC患者的辅助工具。Objective To analyze the routine test parameter levels of patients with colorectal adenoma and colorectal cancer,and develop a prediction model.Methods A total of 580 patients diagnosed with colorectal adenoma(117 patients)and colorectal cancer(463 patients)were included in the retrospective study.The patients were randomly divided into two groups according to a 7:3 ratio:a training set with 406 cases and a validation set with 174 cases.Logistic regression analysis was used to establish a prediction model,and a nomogram was drawn.The model′s discrimination,calibration,and clinical applicability were evaluated using receiver operating characteristic curve(ROC),calibration plot,and decision curve analysis(DCA).Results Univariate logistic regression analysis identified 13 potential predictors:age,fecal occult blood test(FOBT),fibrinogen(FIB),thrombin time(TT),albumin(ALB),white blood cell value(WBC),neutrophil count(NEUT#),hematocrit value(HCT),mean corpuscular hemoglobin(MCH),red cell distribution width(RDW),platelet count(PLT),mean platelet volume(MPV),and activated partial thromboplastin time(APTT).Multivariate logistic regression analysis showed MPV,FIB,ALB,FOBT,TT,and HCT were risk factors for colorectal cancer in patients with colorectal adenoma(P<0.05).A nomogram was constructed based on these predictors to build a prediction model.The AUC of the ROC curve was 0.915 for colorectal cancer in the training set and 0.836 in the validation set.Calibration plots demonstrated high prediction accuracy and good model calibration.DCA results indicated the prediction model provided greater net benefit compared with the extreme models at threshold probabilities of approximately 55%-95%.Conclusion The developed prediction model exhibits satisfactory discrimination,calibration,and clinical applicability.The model can serve as an auxiliary tool in distinguishing between colorectal adenoma and colorectal cancer in patients.

关 键 词：结直肠腺瘤 结直肠癌 预测模型 列线图 

分 类 号：R446.1[医药卫生—诊断学] R735.3[医药卫生—临床医学]