CAJ | 학술논문

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（49vs26，χ2＝8．360，P＝0．004），这一现象在恶性结节中尤为突出（良性23vs16；恶性26vs10）；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。
목적：연구폐결절내개화밀도여개화정상대폐결절량악성예판적성능。방법경병리혹수방증실적폐결절240례（량성70례，악성170례）。이용최대방차화역치생장법제취폐결절내구유개화점화개화밀도적상소，병계산매개개화점적면적（AreaCa）급면적비（Ar）、동일층면내개화점적총면적（S）급개화총면적비（Sr）、동일층면내구유개화밀도적총면적（Cs）급개화밀도면적비（Csr）。결과함유개화밀도적결절명현다우함개화점적결절（49vs26，χ2＝8．360，P＝0．004），저일현상재악성결절중우위돌출（량성23vs16；악성26vs10）；량성폐결절개화점화개화밀도적면적균대우악성（P＝0．000）；이개화점（AreaCa、S、Ar、Sr）대폐결절량악성예판적진단성능우수（Az＝0．906），이개화밀도（Cs、Csr）대폐결절성질예판적진단성능중등（Az＝0．727，0．742）。결론재계산궤보조진단연구중，차감의사경험，대폐결절적CT정상직접진행제취、알굴，혹유조우폐결절영상진단적학립。
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.