摘要
目的:构建基于能谱电子计算机断层扫描(CT)参数和临床参数特征的Logistic回归模型,评估其对肺磨玻璃结节(GGN)良恶性的诊断效能。方法:选择我院2022年4月至2024年3月收治的162例GGN患者,按病理结果分为良性组和恶性组。比较两组能谱CT参数[(平扫期水含量、动脉期水含量、平扫期能谱曲线斜率、动脉期能谱曲线斜率(k值)]与临床资料的差异。应用多因素Logistic回归模型分析恶性GGN的独立影响因素。通过受试者工作特征(ROC)曲线评估模型对恶性GGN的诊断效能。结果:恶性组平扫期水含量显著高于良性组(P<0.05);恶性组平扫期能谱曲线斜率(k值)显著高于良性组(P<0.05);动脉期水含量恶性组高于良性组(P<0.05);动脉期能谱曲线斜率(k值)恶性组高于良性组(P<0.05)。良性组和恶性组在吸烟史、肿瘤家族史、慢性阻塞性肺疾病(COPD)病史比较,差异有统计学意义(P<0.05)。多因素Logistic回归模型分析结果显示,平扫期水含量、平扫期k值、动脉期k值、动脉期水含量、吸烟史、肿瘤家族史是GGN患者恶性结节的独立影响因素(P<0.05)。基于多因素Logistic回归分析结果,构建Logistic回归预测模型:logit(P)=ln(P/1-P)=0.015×动脉期水含量+1.214×吸烟史+1.506×肿瘤家族史+0.013×平扫期水含量-0.553×平扫期k值+0.202×动脉期k值。ROC曲线结果显示,联合预测模型的曲线下面积(AUC)为0.852,显著高于平扫期水含量、平扫期k值、动脉期k值、动脉期水含量、吸烟史、肿瘤家族史的0.654、0.607、0.628、0.759、0.707、0.682。结论:基于能谱CT参数与临床参数特征构建的Logistic回归模型,对GGN良恶性具有良好诊断效能。
Objective:To construct a Logistic regression model based on the energy spectral computed tomography(CT)parameters and clinical parameters characteristics,and to evaluate its diagnostic efficacy in the benign and malignant pulmonary ground glass nodules(GGN).Methods:162 GGN patients who were admitted to our hospital from April 2022 to March 2024 were selected,they were divided into benign group and malignant group according to pathological results.The difference of CT parameters[water content in normal scan period,water content in arterial period,slope of energy spectral curve in normal scan period and k value in arterial period]and clinical data between the two groups was compared.Multivariate Logistic regression model was used to analyze the independent influencing factors of malignant GGN.Receiver operating characteristic(ROC)curve was used to evaluate the diagnostic efficacy of the model for malignant GGN.Results:Water content in normal scan period in malignant group was significantly higher than that in benign group(P<0.05).k value in normal scan period in the malignant group was significantly higher than that in the benign group(P<0.05).Water content in arterial period in malignant group was higher than that in benign group(P<0.05).k value in arterial period in the malignant group was higher than in benign group(P<0.05).There were significant differences in smoking history,family history of tumor and chronic obstructive pulmonary disease(COPD)between benign group and malignant group(P<0.05).Multivariate Logistic regression model analysis showed that,water content in normal scan period,k value in normal scan period,k value in arterial period,water content in arterial period,smoking history and family history of tumor were independent influencing factors of malignant nodule in GGN patients(P<0.05).Based on the results of multi-factor Logistic regression analysis,the Logistic regression prediction model was constructed:logit(P)=ln(P/1-P)=0.015×water content in arterial period+1.214×smoking history+1.506×family history of tumor+0.013×water content in normal scan period 0.553×k value in normal scan period+0.202×k value in arterial period.ROC curve results showed that the area under the curve(AUC)of the combined prediction model was 0.852,which was significantly higher than 0.654,0.607,0.628,0.759,0.707,0.682 of water content in normal scan period,k value in normal scan period,k value in arterial period,water content in arterial period,smoking history and family history of tumor.Conclusion:The Logistic regression model constructed based on the characteristics of energy spectral CT parameters and clinical parameters characteristics has good diagnostic efficacy for benign and malignant GGN.
作者
马飞
孙庆生
叶智川
李槐
MAFei;SUN Qing-sheng;YE Zhi-chuan;LI Huai(Department of Radiology,Xiamen Susong Hospital,Xiamen,Fujian,361000,China;Department of Respiratory,Zhengzhou Traditional Chinese Medicine Hospital,Zhengzhou,Henan,451199,China;Department of Vascular Intervention,Xiamen Hongai Hospital,Xiamen,Fujian,361000,China)
出处
《现代生物医学进展》
2025年第13期2201-2207,共7页
Progress in Modern Biomedicine
基金
厦门市卫生健康高质量发展科技计划项目(2024GZL-QN150)。
关键词
能谱CT参数
临床参数特征
LOGISTIC回归模型
肺磨玻璃结节
良恶性
Energy spectral CT parameters
Clinical parameter characteristics
Logistic regression model
Pulmonary ground glass nodules
Benign and malignant
作者简介
马飞(1979-),男,本科,副主任医师,主要研究方向:骨肌及胸部影像,E-mail:15305963311@163.com。
