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对CT值≤-560HU的肺部纯磨玻璃样结节中浸润性肺腺癌的研究 被引量:2

Study of invasive adenocarcinoma as pulmonary pure ground glass nodules with CT value less than-560HU using thinslice CT
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摘要 目的探讨CT值≤-560HU的肺部纯磨玻璃样结节(pure ground glass nodule,pGGN)中浸润性腺癌(invasive adenocarcinoma,IA)浸润性判定的薄层CT表现特征。方法收集2015年10月至2017年11在大连医科大学附属第一医院经手术和病理证实并术前均有薄层CT扫描、且CT值≤-560HU的pGGN患者144例,分为IA组8例和非IA组136例(包括非典型腺瘤样增生、原位腺癌、微浸润性腺癌)。在薄层CT图像上测量所有pGGN的最长径。结果在CT值≤-560HU的肺部pGGN中,IA组最长径平均为(2.00±0.71)cm,明显大于非IA组[(0.92±0.43)cm],差异有统计学意义(t=-4.495,P=0.003)。ROC曲线分析,AUC=0.94,最佳截断值为1.10cm时,敏感度为100.00%。结论CT值≤-560HU的肺部pGGN病灶的最长径是预测IA浸润性的最佳指标,截断值为1.10cm。 Objective To explore imaging characteristics of invasive adenocarcinoma (IA) as pulmonary pure ground glass nodule (pGGN) with CT value less than -560HU using thin-slice CT.Methods One hundred and forty-four patients with pGGN of CT value less than -560HU confirmed by surgical pathology in the First Affiliated Hospital of Dalian Medical University were enrolled in this study. They were divided into two groups: 8 patients in IA and 136 in non-IA (including atypical adenomatous hyperplasia, adenocarcinoma in situ, microinvasive adenocarcinoma). All patients performed thin-slice CT scan. We measured the maximum diameters of pGGN.Results The maximum diameter of pGGN with CT value less than -560HU in IA group (2.00±0.71cm) was significantly larger than non-IA group (0.92±0.43cm)(t=-4.495,P=0.003),AUC=0.94, best cut-off value was 1.10cm, sensitivity was 100.00%.Conclusion The maximum diameter of pGGN with CT value less than -560HU is an optimum parameter to predict IA when cut-off value of maximum diameter is set up 1.10cm.
作者 胡帅 李梦颖 车思雨 韩广庆 刘芯言 刘爱连 李智勇 Shuai HU;Meng-ying LI;Si-yu CHE;Guang-qing HAN;Xin-yan LIU;Ai-lian LIU;Zhi-yong LI(The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China)
出处 《结核病与肺部健康杂志》 2018年第4期236-240,共5页 Journal of Tuberculosis and Lung Health
基金 大连市医学科学研究计划项目(2016QN009)
关键词 腺癌 细支气管肺泡 孤立性肺结节 体层摄影术 X线计算机 肿瘤分期 预测 Adenocarcinoma, bronchiolo-alveolar Solitary pulmonary nodule Tomography, X-ray computed Neoplasm staging Forecasting
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