山东大学学报 (医学版) ›› 2021, Vol. 59 ›› Issue (4): 100-107.doi: 10.6040/j.issn.1671-7554.0.2021.0109
赵洁1,李岩1,李明2,于德新1
ZHAO Jie1, LI Yan1, LI Ming2, YU Dexin1
摘要: 目的 探讨螺旋动态增强CT检查对黏液性软组织肿瘤良恶性的鉴别诊断价值。 方法 选取2016年8月至2020年3月在山东大学齐鲁医院诊治的147例(男52例,女95例)经病理诊断为黏液性软组织肿瘤患者的临床和CT资料进行回顾性分析。根据病理分为良性组(n=59例)和恶性组(n=88例)。由两名放射科医生评估并记录肿瘤的平扫及三期动态增强CT特征:病灶的长/短径、边界、形状,以及瘤内黏液、实性、钙化和液化坏死成分的分布和数量,有无增强的血管,黏液和实性成分在平扫和各增强期的CT值。分析患者的年龄、性别和病灶部位与上述CT特征对鉴别肿瘤良恶性的价值,根据方差膨胀系数(VIF)检验筛选变量,并建立含不同变量的多因素Logistic 回归预测模型,包括基于分段多项式模式(MFP)的模型0,具完整变量的模型1和基于赤池信息准则(AIC)的模型2,绘制受试者工作特征(ROC)曲线评价模型效能。 结果 患者年龄、病灶的部位、长/短径、边界、形状、瘤内钙化、三期增强的血管、平扫时实性成分CT值在良性和恶性肿瘤之间均差异具有统计学意义(P<0.05); 基于上述临床资料和CT特征变量的Logistic回归模型对良、恶性肿瘤的鉴别具有一定价值(模型0: OR=29.714 3, AUC=0.867 8; 模型1: OR=37.142 9, AUC=0.874 6; 模型2: OR=9.730 8, AUC=0.833 6)。 结论 利用螺旋CT特征和患者临床资料联合建立的Logistic 回归预测模型可对黏液性软组织肿瘤进行良恶性的鉴别。
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