摘要
目的探讨^(18)F-氟脱氧葡萄糖(FDG)PET/CT纹理分析在肺癌与肺结核的高代谢孤立性肺结节(SPN)鉴别诊断中的增益价值。方法回顾性分析2017年1月至2020年6月在内蒙古赤峰市医院和北京大学肿瘤医院行^(18)F-FDG PET/CT检查且结果表现为高代谢[最大标准化摄取值(SUVmax)≥2.5]的108例SPN患者的临床资料, 其中男性68例、女性40例, 年龄35~72岁, 中位年龄50岁;肺结核患者45例(肺结核组)、肺癌患者63例(肺癌组)。所有患者均经组织病理学检查结果确诊。分析所有患者^(18)F-FDG PET/CT图像SPN的良恶性(主观定性诊断), 并计算错判率、灵敏度和特异度。采用MaZda纹理分析软件分别对CT和PET图像中的SPN横断面最大层面及相邻上下两层图像手动勾画ROI并提取纹理特征参数。分别采用Fisher系数、分类错误概率+平均相关系数、交互信息以及三者联合的方法(FPM)筛选具有鉴别意义的纹理特征。对筛选出的纹理特征进行原始数据、主成分、线性分类和非线性分类的分析, 对SPN的良恶性进行鉴别, 以错判率评价其鉴别效能。计量资料的组间比较采用独立样本t检验;计数资料的组间比较采用χ^(2)检验。对错判率最低的各纹理特征参数分别进行受试者工作特征(ROC)曲线分析, 筛选最具鉴别意义的前3位纹理特征。结果肺癌组与肺结核组患者在年龄[54 (42~72)岁对47 (35~64)岁]、SUVmax[(9.51±4.65)对(5.35±2.89)]间的差异均有统计学意义(t=2.180、2.520, 均P<0.05);在性别、SPN长径间的差异均无统计学意义(χ^(2)=0.070, t=0.675, 均P>0.05)。主观定性诊断高代谢SPN良恶性的错判率为26.9%(29/108)、灵敏度为93.7%(59/63)、特异度为35.9%(14/39)。基于SUVmax的ROC曲线分析, SUVmax临界值为5.3时错判率为25.0%(27/108), 其与主观定性诊断的错判率差异无统计学意义(χ^(2)=0.096, P>0.05)。CT和PET图像基于FPM联合非线性分类分析诊断的错判率最低, 分�
Objective To explore the complementary value of ^(18)F-fluorodeoxyglucose(FDG)PET/CT texture analysis in the differential diagnosis of solitary lung cancer and tuberculosis nodules with hypermetabolic solitary pulmonary nodules(SPN).Methods A total of 108 patients with hypermetabolic SPN(maximum standard uptake value(SUVmax)≥2.5)who were examined by ^(18)F-FDG PET/CT were recruited retrospectively in Chifeng Municipal Hospital of Inner Mongolia and Beijing Cancer Hospital,Beijing Institute for Cancer Research from January 2017 to June 2020.The patients consisted of 68 males and 40 females aged 35 to 72 years,with a median age of 50 years.Forty-five patients had tuberculosis(tuberculosis group),and 63 patients had lung cancer(lung cancer group).All the nodules were confirmed by pathological examination.The benign and malignant SPN of the ^(18)F-FDG PET/CT images of all patients were analyzed(subjective qualitative diagnosis),and misjudgment rate,sensitivity,and specificity were calculated.MaZda texture analysis software was used to delineate the region of interest manually on the maximum cross section and the adjacent upper and lower layers of the lung nodule on the CT and PET images to extract texture feature parameters.Fisher coefficient,classification error probability+average correlation coefficients,mutual information,and their combination(FPM)were used to filter texture features with differential diagnostic significance.The software program was also used for raw data analysis,principal component analysis,linear discriminant analysis,and nonlinear discriminant analysis to discriminate benign or malignant nodules.The discriminating efficacy was evaluated by misclassified rate.Independent sample t test was used to compare the measurement data between groups.Chi-square test was used to compare the count data between groups.Receiver operating characteristic(ROC)curve analysis was carried out for each texture feature parameter of the lowest misclassified rate to screen the top three most discriminative texture f
作者
高玉杰
周妮娜
李雨奇
张伟
张鹏博
罗晓燕
Gao Yujie;Zhou Nina;Li Yuqi;Zhang Wei;Zhang Pengbo;Luo Xiaoyan(Department of Nuclear Medicine,Chifeng Municipal Hospital of Inner Mongolia,Chifeng 024000,China;Department of Nuclear Medicine,Beijing Cancer Hospital,Beijing Institute for Cancer Research,Beijing 100089,China)
出处
《国际放射医学核医学杂志》
2022年第2期73-79,共7页
International Journal of Radiation Medicine and Nuclear Medicine
