基于CT形态及定量学特征构建多原发肺腺癌、腺体前驱病变风险分层模型  

To Constitution a Risk Stratification Model of Multiple Primary Lung Adenocarcinoma and Gland Precursor Lesions Based on CT Morphological and Quantitative Characteristics

作　　者：邱慎满 孟闫凯 赵恒亮 张磊 马东慎 李胜利 朱丽丽 陈志成 徐凯 QIU Shenman;MENG Yankai;ZHAO Hengliang(Department of Radiology,The Affiliated Hospital of Xuzhou Medical University,Xuzhou,Jiangsu Province 221002,P.R.China)

机构地区：[1]徐州医科大学附属医院影像科,221002 [2]徐州医科大学医学影像与数字医学研究所,221002 [3]徐州医科大学医学影像学院,221002 [4]徐州医科大学附属医院病理科,221002 [5]徐州医科大学附属医院病案统计科,221002 [6]徐州医科大学附属淮海医院医学影像科,221002 [7]台湾阳明交通大学生物医学影像暨放射科学系,中国台北

出　　处：《临床放射学杂志》2022年第5期860-865,共6页Journal of Clinical Radiology

基　　金：江苏省医学会伦琴影像科研专项资金项目(编号:SYH-3201150-0013);徐州市科学技术局重点研发计划(社会发展)项目(编号:KC20159)。

摘　　要：目的利用基线CT形态及定量学特征构建多原发肺腺癌(MPLC)、腺体前驱病变不同结节危险度分层的多因素预测模型。方法回顾性分析经手术病理证实的72例MPLC、腺体前驱病变患者临床及影像学资料。根据手术病理类型将不典型腺瘤样增生(AAH)及原位癌(AIS)归为低危组,而将微浸润腺癌(MIA)及浸润性腺癌(IAC)归为高危组。结节形态学特征[成分、边缘(毛刺和分叶)、空腔(空泡、扩张支气管征)、胸膜牵拉]由人工智能(AI)肺结节分析软件自动获得,然后由1名具有5年胸部CT经验的影像科医师再次评估。结节定量CT参数(最大径、长短径之比、平均CT值及体积)由后处理工作站(Lung VCAR,GE)分析获得。采用SPSS 22.0软件进行统计分析,Logistic回归分析筛选预测因子,构建预测模型,受试者工作特征曲线(ROC)分析评价模型效能。P<0.05为差异有统计学意义。结果72例患者经手术病理证实160枚结节,其中双原发结节57例(79.17%),女性52例(72.22%)。高危组、低危组结节最大径分别为(13.85±6.14)mm、(6.10±2.45)mm,长短径之比分别为1.38±0.25、1.30±0.25,平均CT值分别为(-532.71±175.88)HU、(-669.23±103.92)HU,体积分别为(1618.00±2149.79)mm^(3)、(178.75±198.02)mm^(3)。高危组、低危组结节最大径、平均CT值和结节体积间差异均有统计学意义(P=0.000,0.001,0.000)。结节最大径预测高危组的最佳截断值为7 mm,曲线下面积(AUC)为0.910,敏感度和特异度分别为81.25%和86.61%。结节体积预测高危组的最佳截断值为323 mm^(3),AUC值、敏感度、特异度分别为0.904、91.67%、77.68%。将定量参数结节最大径、体积、平均CT值纳入Logistic回归分析,构建组合变量。ROC曲线分析显示,组合变量预测高危组结节的AUC值为0.939,敏感度90.00%,特异度85.71%,差异具有统计学意义(P<0.0001)。形态学特征上,结节内部是否含有实性成分、实性成分多少[纯磨玻璃密度结节(pGObjective To constitute a multi-parameter risk stratification predictive model based on the morphological and quantitative features of baseline CT to distinguish the multiple primary lung cancer(MPLC)and glandular prodrome lesions.Methods The clinical and imaging data of enrolled 72 patients confirmed by operative pathology were retrospectively analyzed.According to the pathological classification,atypical adenomatous hyperplasia(AAH)and adenocarcinoma in situ(AIS)were classified into the low risk group,while minimally invasive adenocarcinoma(MIA)and invasive adenocarcinoma(IAC)were classified into the high risk group.The morphological features(composition,margin[spiculation and lobulation],airspace[vacuole,air bronchogram],pleural retraction)of the different MPLC nodules were automatically achieved by the artificial intelligence(AI)pulmonary nodule analysis software,then reevaluated by one radiologist with 5 years experience in chest CT imaging.The quantitative parameters of CT(maximum diameter,ratio of long to short diameter,volume and mean CT value)were measured in the post processing workstation(Lung VCAR software,GE).SPSS 22.0 software was used to analyze.Logistic regression analysis was used to generate a combined risk model for predicting high risk lesions.ROC analysis was used to evaluate the efficacy of the model.P values less than 0.05 were considered statistically significant.Results 160 nodules were confirmed by surgery and pathology in 72 patients,including 57 cases(79.17%)of dual primary lesions and 52 cases(72.22%)of females.The maximum diameter of lesion and the ratio of long to short diameter of lesion in high and low risk group were(13.85±6.14)mm,(6.10±2.45)mm and 1.38±0.25,1.30±0.25,respectively.The mean CT values were(-532.71±175.88)HU for high-risk group,and(-669.23±103.92)HU for low-risk group.And,the volumes of lesions were(1618.00±2149.79)mm~3 and(178.75±198.02)mm~3 for two groups.The differences in maximum diameter,mean CT value and the volume of lesion between high and low risk g

关 键 词：多原发肺癌 腺癌 体层摄影术 X线计算机 分层 

分 类 号：R734.2[医药卫生—肿瘤]