【作者】 王亚丽；

【导师】 孙希文；

【作者基本信息】 苏州大学 ， 影像医学与核医学（专业学位）， 2017， 硕士

【摘要】 第一部分≤10mm纯磨玻璃结节的CT表现与病理侵袭性的对照研究目的:探讨肺部10mm以下纯磨玻璃结节的CT表现与其病理侵袭性的关系,提高肺部纯磨玻璃结节诊断准确率。资料和方法:回顾性分析2014年6月至2015年12月102例直径≤10mm的pGGN患者的临床、病理和影像资料。所有患者均经手术切除病理证实为肺腺癌。浸润前病变组包括25例AAH和35例AIS,浸润性病变组包括27例MIA和15例IAC。CT表现包括pGGN的大小、CT值、部位、形状、瘤肺界面、分叶、毛刺、空泡征、空气支气管征、胸膜凹陷征、血管改变。结果:在浸润前病变组和浸润性病变组间仅病灶大小、平均CT值和分叶征有明显统计学差异(P<0.05)。经ROC曲线分析,病灶大小、CT值的最佳临界值分别为8.7mm、-527HU。性别、年龄、部位、瘤肺界面、毛刺、空泡、空气支气管征、胸膜凹陷征及血管改变在浸润前病变组和浸润性病变组间均无统计学差异(P>0.05)。结论:大小、CT值和分叶征对鉴别10mm以下pGGN浸润前病变和浸润性病变有帮助。pGGN直径大于8.7mm或CT值大于-527HU时提示为微浸润性腺癌或浸润性腺癌。第二部分影像组学对肺部10mm以下纯磨玻璃结节侵袭性的预测价值目的:影像组学可以在医学图像数据上应用特征算法无创性地定量肿瘤的表型特征。本研究旨在探讨10mm以下p GGN肺腺癌影像组学特征与病理侵袭性之间的关系,并评估基于影像组学特征的分类器模型对此类病变病理侵袭性的预测效果。资料和方法:共102个病人其中浸润前病变组60人,浸润性病变组42人入组本次回顾性研究。从治疗前CT图像上分割的肿瘤容积感兴趣区共提取93个影像组学特征,包括肿瘤的形状特征、强度和纹理特征。采用Mann-Whitney U检验和信息增益算法选择特征,根据所选特征集建立支持向量机、朴素贝叶斯、逻辑回归分类器模型并绘制ROC曲线评估分类器的预测效果。结果:从每一个容积感兴趣区提取93个影像组学特征,经Mann-Whitney U检验筛选及信息增益算法过滤后共选择48个影像组学特征。支持向量机、朴素贝叶斯分类器和逻辑回归分类器模型的曲线下面积依次为:0.822、0.848和0.874。结论:基于CT图像的影像组学特征可以反映10mm以下p GGN肺腺癌浸润前病变和浸润性病变的差异。基于影像组学特征的分类器模型可以提高p GGN病理侵袭性的术前预测准确性。更多还原

【Abstract】 Part One Comparasions of CT findings and histological invasiveness inpGGNs less than or equal to 10 mmObjective: To investigate the correlation between CT findings and pathological invasion of pure ground-glass nodules(GGNs)less than or equal to 10 mm in size,and to improve the diagnostic accuracy of pure ground glass nodules.Materials and methods: This retrospective study included 102 patients with 102 pGGNs≤ 10 mm who were confirmed by surgery and pathology.There were altogether of 60 pre-invasive lesions(25 AAH and 35 AIS)and 42 invasive lesions(27 MIA and 15 IAC).CT finds of pGGNs including size,mean CT value,location,shape,tumor-lung interface,lobulation,spiculation,bubble luccency,air bronchogram,pleural indentation and vascular changes were evaluated.Results: There were significant differences in size,mean CT value and lobulation between preinvasive group and invasive group(P?<?0.05),while no significant differences in age,gender,shape,tumor-lung interface,bubble lucency,air bronchogram,pleural indentation,and vascular convergence or dilatation were found(P?>?0.05).ROC analyses revealed that the optimal cut-off value for discriminating pre-invasive from invasive lesions was 8.7 mm for size and for mean CT value was-572 HU.Conclusion: The lesion size,mean CT value and lobulation can help differentiate pre-invasive lesions and invasive lesion appearing as pure ground-glass nodules ≤10 mm.A maximum diameter ≥8.7 mm or a mean CT value greater than-527 HU indicate MIA or IAC.Part Two Predictive value of Radiomic features of p GGNs less than or equal to 10 mm for histological invasivenessObjective: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data.In this study of lung adenocarcinoma,we investigated the association between radiomic features and the tumor histologic invasiveness(pre-invasive lesions and invasive lesions).Furthermore,in order to predict the histologic invasiveness,we employed machine-learning methods and evaluated their prediction performance.Methods: We included all of the102 patients with lung adenocarcinoma(60 in pre-invasive group and 42 in invasive group)for this retrospective study.A total of 93 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images.These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape,intensity statistics,and texture.A Mann-Whitney U-test and information gain algorithm were used to select radiomic features,and the support vector machine,naive Bayes and logistic regression classifier model was established and the ROC curve was used to evaluate their prediction performance.Results: Among 93 features were derived from each VOI,48 features were was selected by Mann-Whitney U-test and the information gain algorithm.The area under the receiver operating characteristic curve of support vector machine,Naive Baye’s classifier and logistic regression classifier model were 0.822,0.848 and 0.874.Conclusion: The radiomic features from CT images can reflect the difference between preinvasive lesion and invasive lesion of p GGN lung adenocarcinoma less than or equal to 10 mm.Radiomic features capturing detailed information of the tumor phenotype can be used to identify tumor invasiveness.The classification model based on the radiomic features can improve the preoperative for the prediction of invasiveness of p GGN.更多还原