摘要
目的探讨CT人工智能技术与肿瘤标志物联合鉴别诊断恶性肺结节有效性。方法选取2018年1月至2021年1月我院收治的453例肺结节患者,根据病理学结果分为良性组(n=317)和恶性组(n=136),比较2组临床资料、CT人工智能参数、血清胃泌素释放肽前体(Gastrin-Releasing Peptide,Pro-GRP)、神经元特异性烯醇化酶(Neuron-Specific Enolase,NSE)、癌胚抗原(Carcinoembryonic Antigen,CEA)、细胞角蛋白片段19(Cytokeratin Fragment 19,CYFRA21-1)、鳞状细胞癌抗原(Squamous Cell Carcinoma Antigen,SCC),采用多因素Logistic回归方程分析恶性肺结节相关影响因素,采用R语言绘制预测恶性结节的列线图。结果恶性组吸烟史、既往肺部外恶性肿瘤史患者多于良性组(P<0.05);恶性组结节直径、恶性概率高于良性组,毛刺征、位于上叶、病灶形态不规则、空泡征表现多于良性组(P<0.05);各单独指标评估恶性肺结节价值:恶性概率的AUC大于结节直径、毛刺征、位于上叶、病灶形态不规则、空泡征(P<0.05);恶性组Pro-GRP、NSE、CEA、CYFRA21-1、SCC高于良性组(P<0.05)。多因素分析显示,吸烟史、既往肺部外恶性肿瘤史、恶性概率、Pro-GRP、NSE、CEA、CYFRA21-1、SCC均是恶性结节的相关危险因素(P<0.05)。基于以上多因素分析筛选出的各危险因素,绘制恶性肺结节的列线图预测模型,Bootstrap内部验证显示校正曲线与理想曲线拟合良好,C-index指数为0.984,AUC为0.925,敏感度为85.29%,特异性为83.91%,说明本研究列线图模型具有较好的预测能力。结论基于CT人工智能技术和Pro-GRP、NSE、CEA、CYFRA21-1、SCC构建的恶性肺结节预测模型,能准确、便捷鉴别肺结节的性质,可为临床诊疗提供参考依据。
Objective To investigate the efectiveness of CT artificial intelligence technology combined with tumor markers for the diferential diagnosis of malignant pulmonary nodules.Methods A total of 453 patients with pulmonary nodules admitted to our hospital from January 2018 to January 2021 were selected and divided into benign group(n=317)and malignant group(n=136)according to the pathological findings.The clinical data,CT artificial intelligence parameters,serum gastrin-releasing peptide(ProGRP),neuron-specific enolase(NSE),carcinoembryonic antigen(CEA),cytokeratin fragment 19(CYFRA21-1),squamous cell carcinoma antigen(SCC)were compared,multifactorial logistic regression equation was used to analyze the influencing factors related to malignant pulmonary nodules,and R language was used to draw a column line graph for predicting malignant nodules.Results The number of patients with smoking history and previous history of extrapulmonary malignant tumor in the malignant group was more than that in the benign group(P<0.05).The nodule diameter and malignant probability in the malignant group were higher than those in the benign group,and the appearance of burr sign,lesions located in the upper lobe,with irregular shape and vacuole sign were more than those in benign group(P<0.05).The value of each individual index to evaluate the malignant pulmonary nodules:the AUC of the malignant probability was greater than the diameter of the nodule,the burr sign,the upper lobe,the irregular shape of the lesion,and the vacuole sign(P<0.05).The Pro-GRP,NSE,CEA,CYFRA21-1,SCC in the malignant group were higher than those in the benign group(P<0.05).Multifactorial analysis showed that smoking history,previous history of extra-pulmonary malignancy,probability of malignancy,Pro-GRP,NSE,CEA,CYFRA21-1 and SCC were risk factors associated with malignant nodules(P<0.05).Based on the risk factors screened by the above multifactorial analysis,the prediction model of malignant pulmonary nodules was drawn.Bootstrap internal validation showed that th
作者
范光明
FAN Guangming(Department of Radiology,The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine,Guiyang Guizhou 550000,China)
出处
《中国医疗设备》
2023年第1期71-76,共6页
China Medical Devices
基金
贵州省科教青年英才培训工程(黔省专合字〔2017〕213号)。
关键词
CT
人工智能技术
恶性肺结节
LOGISTIC回归分析
预测模型
CT
artificial intelligence technology
malignant pulmonary nodules
Logistic regression analysis
predictive model
