磁共振成像增强变化率联合表观扩散系数预测卵巢癌分型的可行性研究

Feasibility Study on Prediction of Ovarian Cancer Classification by MRI Enhancement Rate Combined with Apparent Diffusion Coefficient

目的探讨磁共振成像(magnetic resonance imaging,MRI)增强变化率联合表观扩散系数(apparent diffusion coefficient,ADC)预测卵巢癌分型的可行性研究。方法回顾性选取2022年1月—2023年10月在济宁市兖州区人民医院行盆腔MRI增强扫描的70例卵巢癌患者的临床资料,将卵巢癌Ⅰ分型患者定义为A组(n=32),卵巢癌Ⅱ分型患者定义为B组(n=38),测量两组患者ADC值与MRI增强动脉期变化率、静脉期变化率、延迟期变化率。比较两组患者ADC值、各期相变化率值、临床数据,采用二元Logistics回归分析,筛选出预测卵巢癌分型的独立风险因素。利用受试者操作特征(receiver operating characteristic,ROC)曲线进行分析Logistic模型诊断效能。结果两组糖类抗原125、人附睾蛋白4、增强变化率与ADC比较,差异有统计学意义(P均<0.05)。二元Logistics回归方程示显示动脉期变化率、ADC值为预测卵巢癌ECO分型的独立分析因素,建立预测模型为:Logit(P)=3.513+动脉期变化率×4.553+ADC值×4.356,Logit(P)的ROC曲线下面积为0.897,截断值为10.773时,灵敏度为88.9%、特异度为84.8%。结论基于MRI增强变化率联合ADC值能预测卵巢癌ECO分型,可为临床制订治疗方案提供理论依据。

Objective To explore the feasibility of MRI enhancement rate combined with apparent diffusion coefficient(ADC)in predicting ovarian cancer classification.Methods The clinical data of 70 patients with ovarian cancer who underwent pelvic MRI enhancement scan in Yanzhou District People's Hospital of Jining City from January 2022 to October 2023 were retrospectively selected,and the patients with ovarian cancer subtype I were defined as group A(n=32)and those with ovarian cancer subtype II were defined as group B(n=38).ADC values and MRI enhanced rate of change in arterial phase,venous phase and delayed phase were measured in both groups.ADC values,phase change rates and clinical data of the two groups were compared,and binary Logistics regression analysis was used to screen out independent risk factors for predicting ovarian cancer typing.The receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficiency of the Logistic model.Results There were statistically significant differences in the change rates of carbohydrate antigen 125,human epididymis protein 4 and enhancement between the two groups and ADC(all P<0.05).The binary Logistics regression equation showed that the change rate of arterial phase and ADC value were independent analysis factors for predicting ECO typing of ovarian cancer.The prediction model is as follows:Logit(P)=3.513+arterial phase change rate×4.553+ADC value×4.356,the area under ROC curve of Logit(P)is 0.897,and when the threshold is 10.773,the sensitivity is 88.9%and the specificity is 84.8%.Conclusion The combination of MRI enhancement rate and ADC value can predict the ECO classification of ovarian cancer and provide theoretical basis for clinical treatment。