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MSCT对T_(1)期周围型小细胞肺癌的诊断价值

MSCT diagnostic value for peripheral small cell lung cancer at stage T_(1)
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摘要 目的:探讨MSCT对T_(1)期周围型小细胞肺癌的诊断价值。方法:回顾性分析经手术病理证实的113例T_(1)期实性周围型肺癌患者的术前CT及临床资料。将患者分为小细胞肺癌组28例和非小细胞肺癌组85例。对2组间CT征象及临床特征行单因素和多因素logistic回归分析,确定小细胞肺癌的预测因素,并构建预测模型。通过ROC曲线验证预测模型对小细胞肺癌的诊断效能。结果:单因素和多因素logistic回归分析显示,CT上肿瘤边缘光滑、无毛刺征、支气管铸形征及淋巴结肿大是小细胞肺癌的独立预测因素,联合以上特征建立的模型对小细胞肺癌有较高的诊断效能,其AUC、敏感度、特异度及准确率分别为0.936、89.3%、81.2%和87.6%。结论:CT上肿瘤边缘光滑、无毛刺征、支气管铸形征及淋巴结肿大是T_(1)期周围型小细胞肺癌的独立预测因素,联合以上特征对小细胞肺癌有较高的诊断效能。 Objective:To investigate the diagnostic value of MSCT for the peripheral small cell lung cancer(SCLC)patients at satge T_(1).Methods:The preoperative CT and clinical data from 113 patients with solid peripheral lung cancer at stage T_(1) confirmed by surgical pathology were analyzed retrospectively.All patients were divided into the SCLC group(28 cases)and the non-SCLC group(85 cases).Univariate and multivariate logistic regression analysis of the CT signs and clinical factors between the two groups were performed to identify the predictive factors for SCLC.The AUC of ROC curve was used to evaluate the diagnostic ability for SCLC of each independent and combined predictive factor.Results:Univariate and multivariate logistic regression analysis results revealed that the smooth edge,absence of spiculation,bronchial cast sign and lymph node enlargement on CT were the independent predictors for SCLC.The diagnostic ability for SCLC of the combination of the four factors above was trustworthy,and the AUC,sensitivity,specificity and accuracy were 0.936,89.3%,81.2%and 87.6%,respectively.Conclusions:Smooth margin,absence of spiculation,bronchial cast sign and lymph node enlargement on CT are the independent predictors for peripheral SCLC at stage T_(1).The diagnostic ability for SCLC of the combination factors is trustworthy.
作者 杨薪玉 王锡明 王萍 蔡晓婷 马恒 YANG Xinyu;WANG Ximing;WANG Ping;CAI Xiaoting;MA Heng(不详;Department of Imaging,Yantai Yuhuangding Hospital Affilitated of Qingdao University,Yantai 264099,China)
出处 《中国中西医结合影像学杂志》 2023年第5期492-496,共5页 Chinese Imaging Journal of Integrated Traditional and Western Medicine
基金 山东省自然科学基金(ZR2022MH274) 泰山学者工程专项经费(tsqn202103197)。
关键词 肺肿瘤 小细胞肺 非小细胞肺 体层摄影术 X线计算机 诊断 Lung neoplasms Carcinoma,small cell lung Carcinoma,non-small cell lung Tomography,X-ray computed Diagnosis
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