探讨肺结节内钙化密度与钙化征象的临床意义  

Clinical value of calcification density and signs in the diagnosis of lung nodules

作　　者：王秋萍[1] 金晨望[1] 邓蕾[1] 于楠[1] 强永乾[1] 冯筠[2] 郭佑民[1] 

机构地区：[1]西安交通大学第一附属医院医学影像中心,陕西西安710061 [2]西北大学信息科学与技术学院,陕西西安710127

出　　处：《医学影像学杂志》2014年第10期1729-1733,共5页Journal of Medical Imaging

基　　金：陕西省科技计划攻关项目(No.2011K12-05-08);卫生部行业专项资助项目(201402013);陕西省科技统筹创新工程计划项目(2012KTCL03-07)

摘　　要：目的研究肺结节内钙化密度与钙化征象对肺结节良恶性预判的性能。方法经病理或随访证实的肺结节240例(良性70例,恶性170例)。利用最大方差和阈值生长法提取肺结节内具有钙化点和钙化密度的像素,并计算每个钙化点的面积(AreaCa)及面积比(Ar)、同一层面内钙化点的总面积(S)及钙化总面积比(Sr)、同一层面内具有钙化密度的总面积(Cs)及钙化密度面积比(Csr)。结果含有钙化密度的结节明显多于含钙化点的结节(49vs 26,2=8.360,P=0.004),这一现象在恶性结节中尤为突出(良性23vs 16;恶性26vs 10);良性肺结节钙化点和钙化密度的面积均大于恶性(P=0.000);以钙化点(AreaCa、S、Ar、Sr)对肺结节良恶性预判的诊断性能优秀(Az=0.906),以钙化密度(Cs、Csr)对肺结节性质预判的诊断性能中等(Az=0.727,0.742)。结论在计算机辅助诊断研究中,借鉴医师经验,对肺结节的CT征象直接进行提取、挖掘,或有助于肺结节影像诊断的确立。Objective Quantitative study was performed on the calcification density and calcification signs in prediction for benign and malignant pulmonary nodules. Methods 240 cases with pulmonary nodules （malignant in 170 and benign in 70） confirmed by pathology or clinical follow-up were included in this study. All cases underwent chest CT examinations. A segmentation algorithm based on maximal variance between-class and region growing was used to extract the pulmonary nodules with calcification signs and the pixel of calcification density. The each calcification area （AreaCa） and area ratio （Ar）, the total area of calcification signs within the same level （S） and total area ratio （Sr）, the total area of calcification density within same level （Cs）, and calcification density ratio （Csr） were calculated. Results The nodules with calcifica-tion density were more than those with calcification sign （49 vs 26, χ2=8. 36, Pp=0. 004）, which were more easily to be found in malignant nodules than in benign nodules （benign nodules：23 vs 16;malignant nodules：26 vs 10）. The area of both calcification signs and calcification density in benign pulmonary nodules were larger than that in malignant nodules （ P=0. 000）. Calcification signs （AreaCa, S, Ar, Sr） had outstanding ability in prediction for benign and malignant pulmo-nary nodules（Az=0. 906）;while calcification density （Cs, Csr） had mild ability in prediction for benign and malignant pulmonary nodules （Az=0. 727, 0. 742）. Conclusion In computer aided diagnosis system, combined physician experience with direct extraction and quantifying of CT signs can help us to establish the diagnosis of pulmonary nodules.

关 键 词：肺结节 钙化模式 体层摄影术 X线计算机 

分 类 号：R734.2[医药卫生—肿瘤] R814.42[医药卫生—临床医学]