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瘤周2cm水肿带ADC灰度直方图对胶质母细胞瘤空间复发模式的预测价值

Predictive value of ADC histogram analysis in 2 cm peritumoral edema zone for spatial pattern of recurrence in glioblastoma
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摘要 目的探讨术前磁共振成像(magnetic resonance imaging,MRI)瘤周2 cm水肿带的表观扩散系数(apparent diffusion coefficient,ADC)灰度直方图对分析IDH野生型胶质母细胞瘤(glioblastoma,GB)术后空间复发模式的预测价值。方法采用病例-对照研究设计方案,分析陆军特色医学中心神经外科2012年1月至2021年12月采用标准方案治疗且术后定期MRI随访的50例复发性IDH野生型GB患者的影像资料。以复发灶距术腔边缘距离是否>2 cm为标准,根据术后空间复发模式分为原位复发组(n=28)和非原位复发组(n=22)。选取术前轴位MRI增强图像上肿瘤强化最大层面对应的ADC图像层面,采用Mazda软件沿瘤周水肿2 cm范围边缘勾画提取感兴趣区(region of interest,ROI)进行灰度直方图分析,对提取的特征参数进行统计学分析并应用受试者工作特征曲线计算曲线下面积(area under the curve,AUC),评估其诊断效能。结果原位复发组无进展生存期(progression-free survival,PFS)及总生存期(overall survival,OS)均长于非原位复发组(中位PFS:6.6 vs 4.6个月;中位OS:15.4 vs 12.4个月),差异均有统计学意义(PFS:log-rank χ^(2)=4.325,P=0.038;OS:log-rank χ^(2)=4.022,P=0.045)。在ADC灰度直方图提取的9个特征参数中,两组间方差、峰度、第90百分位数的差异有统计学意义(P<0.05)。以方差鉴别不同空间复发模式的诊断效能最佳(AUC:0.804,敏感度:75.00%,特异度:81.82%)。在基于方差、峰度、第90百分位数两两或3个参数联合构建的4种多因素Logistic回归模型中,以3个参数联合构建的模型诊断效能最佳(AUC:0.878,敏感度:78.57%,特异度:86.36%)。结论术前MRI瘤周2 cm水肿带的ADC灰度直方图特征参数可作为预测IDH野生型GB患者术后不同空间复发模式的影像学标记。 Objective To evaluate the predictive value of apparent diffusion coefficient(ADC)histogram in preoperative magnetic resonance imaging(MRI)of the 2 cm peritumoral edema zone for spatial pattern of recurrence in IDH wild-type glioblastoma(GB).Methods A case-control study was performed on 50 patients with IDH wild-type GB treated with standard protocol,diagnosed by pathology as tumor recurrence and accepted regular follow-up MRI in Department of Neurosurgery of Army Medical Center of PLA from January 2012 to December 2021.The postoperative spatial recurrence patterns were divided into the local recurrence group(n=28)and the non-local recurrence group(n=22)according to whether the distance between the recurrence foci and the operative cavity was more than 2 cm or not.According to the maximum level of tumor enhancement in axial planes of enhanced MRI,the corresponding level of ADC images were selected.Mazda software was used to outline the region of interest(ROI)along the edge of the 2 cm range of peritumoral edema for histogram analysis.The histogram parameters of the 2 groups with different spatial patterns of recurrence were statistically analyzed,and the area under the curve(AUC)was obtained by receiver operating characteristic curve to evaluate the diagnostic efficacy.Results Among the 50 patients with recurrent GB,progression-free survival(PFS)and overall survival(OS)in the local recurrence group were longer than those in the non-local recurrence group(median PFS:6.6 vs 4.6 months;median OS:15.4 vs 12.4 months),and the differences were statistically significant(PFS:log-rank Chi-square=4.325,P=0.038;OS:log-rank Chi-square=4.022,P=0.045).Variance,kurtosis and Perc.90%of the 9 features extracted from ADC histogram were statistically significant differences between the 2 groups(P<0.05).Variance had the best diagnostic efficiency(AUC:0.804,sensitivity:75.00%,specificity:81.82%).Among the 4 multivariate logistic regression models constructed based on the variance,kurtosis and Perc.90%,the model constructed by the combination of variance,kurtosis and Perc.90%had the best diagnostic efficiency(AUC:0.878,sensitivity:78.57%,specificity:86.36%).Conclusion The characteristic parameters of the preoperative ADC histogram in peritumoral edema zone can be used as an imaging marker to predict the different spatial patterns of postoperative recurrence in patients with IDH wild-type GB.
作者 曾琳岚 王瀚苇 田静 冉启胜 张乐天 易良 王舒楠 ZENG Linlan;WANG Hanwei;TIAN Jing;RAN Qisheng;ZHANG Letian;YI Liang;WANG Shunan(Department of Radiology,Clinical Research Center of Radiology and Nuclear Medicine,Army Medical Center of PLA,Chongqing,400042,China;Department of Neurosurgery,Army Medical Center of PLA,Chongqing,400042,China)
出处 《陆军军医大学学报》 CAS CSCD 北大核心 2023年第4期318-325,共8页 Journal of Army Medical University
基金 国家自然科学基金青年科学基金(81701661) 陆军军医大学人才项目(2019XLC3049) 重庆市影像医学与核医学临床研究中心科技计划项目(CSTC2015YFPT-gcjsyjzx0175)。
关键词 胶质母细胞瘤 肿瘤复发 磁共振成像 表观扩散系数 直方图分析 glioblastoma recurrence of tumor magnetic resonance imaging apparent diffusion coefficient histogram analysis
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