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作 者:龚灵毓 袁瑛[1] 陶晓峰[1] GONG Lingyu;YUAN Ying;TAO Xiaofeng(Department of Radiology,Shanghai Ninth People's Hospital,Shanghai Jiaotong University School of Medicine,Shanghai 2000ll,China)
机构地区:[1]上海交通大学医学院附属第九人民医院放射科,上海200011
出 处:《实用放射学杂志》2024年第3期435-438,共4页Journal of Practical Radiology
摘 要:目的探讨基于MR表观扩散系数(ADC)值鉴别隆突性皮肤纤维肉瘤(DFSP)和神经纤维瘤(NF)的价值.方法回顾性分析50例经病理或临床确诊为DFSP(36例)和NF(14例)患者的增强MR图像.评估并比较2组患者的临床基本资料和常规及功能MRI上的特点,后者包括最大径、边界、浸润深度、强化模式以及ADC值.采用卡方检验、独立样本t检验或Mann-Whitney U检验选取有统计学差异的参数,使用二元logistic回归分析建立新的诊断模型,绘制受试者工作特征(ROC)曲线用于评估模型的诊断效能.结果分别以年龄、最大径和ADC值建立单因素诊断模型,通过logistic回归分析建立联合模型,其曲线下面积(AUC)分别为0.756、0.837、0.826和0.923,灵敏度分别为88.89%、80.56%、86.11%和91.67%,特异度分别为64.29%、85.71%、78.57%和92.86%.结论ADC值联合年龄、最大径建立的预测模型对DFSP和NF具有良好的鉴别诊断价值.Objective To investigate the value of differentiating dermatofibrosarcoma protuberans(DFSP)from neurofibroma(NF)based on MR apparent diffusion coefficient(ADC)value.Methods The enhanced MR images data of 50 patients with patho-logically or clinically confirmed DFSP(36 patients)and NF(14 patients)were analyzed retrospectively.Clinical data and characteris-tics of conventional and functional MRI,including maximum diameter,margin,depth of invasion,enhancement pattern,and ADC value,were evaluated and compared between the two groups.Chi square test,independent samples t-test or Mann-Whitney U test were used to select statistically significant parameters.New diagnostic models were established via binary logistic regression analysis.The receiver operating characteristic(ROC)curves were drawn to evaluate the diagnostic performance of these models.Results Univariate diag-nostic models were developed based on age,maximum diameter,and ADC value,while the combined model was established by logis-tic regression analysis.The area under the curve(AUC)of these models were 0.756,0.837,0.826 and 0.923,with sensitivities of 88.89%,80.56%,86.11%and 91.67%,and specificities of 64.29%,85.71%,78.57%and 92.86%,respectively.Conclusion The combined model based on ADC value,age,and maximum diameter is highly valuable for differential diagnosis between DFSP and NF.
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