影像组学预测原发性中枢神经系统淋巴瘤的Ki-67标记指数

Radiomics predicts Ki-67 labeling index in primary central nervous system lymphomas

目的 研究原发性中枢神经系统淋巴瘤(primary central nervous system lymphomas, PCNSL)中表观扩散系数(apparent diffusion coefficient, ADC)、弥散加权成像(diffusion weighted imaging, DWI)和T1对比增强(T1 contrast enhanced, T1-CE)与Ki-67标记指数(labeling index, LI)的相关性,并评估基于多参数MRI影像组学模型区分低增殖PCNSL和高增殖PCNSL的性能。方法 本项回顾性研究纳入83例PCNSL患者的MRI图像及临床信息,并利用Spearman相关性分析检验它们与Ki-67 LI的相关性。分别提取三个序列(ADC、DWI和T1-CE)的影像组学特征,并构建不同的影像组学模型。受试者工作特征(receiver operating characteristic, ROC)曲线用于评估模型性能,Delong检验用于比较模型差异。结果 相对平均ADC(relative mean ADC, rADCmean)(ρ=-0.354,P=0.019)、相对平均DWI(relative mean DWI, rDWImean)(b=1 000)(ρ=0.273,P=0.013)和相对平均T1-CE(relative mean T1-CE, rT1-CEmean)(ρ=0.385,P=0.001)与Ki-67显著相关。最佳预测模型是组合模型(ADC+DWI+T1-CE)(AUC=0.869)。结论 rDWImean、 rADCmean和rT1-CEmean与Ki-67 LI相关。基于多参数MRI影像组学模型有望区分低增殖PCNSL和高增殖PCNSL。

Objective To examine the correlation of apparent diffusion coefficient(ADC),diffusion weighted imaging(DWI),and T1 contrast enhanced(T1-CE)with Ki-67 labeling index(LI)in primary central nervous system lymphomas(PCNSL),and to assess the diagnostic performance of MRI radiomics-based models in differentiating the high-proliferation and low-proliferation groups of PCNSL.Methods MRI images and clinical information of 83 PCNSL patients were included,and their correlation with Ki-67 LI was examined using Spearman correlation analysis.The imaging histological features of three sequences were extracted separately and seven different imaging histological models were constructed.The receiver operating characteristic(ROC)curve was used to evaluate the predictive performance of all models.Delong test was utilised to compare the differences of models.Results Relative mean ADC(rADCmean)(ρ=-0.354,P=0.019),relative mean DWI(rDWImean)(b=1,000)(ρ=0.273,P=0.013)and relative mean T1-CE(rT1-CEmean)(ρ=0.385,P=0.001)were significantly correlated with Ki-67.The best prediction model is ADC+DWI+T1-CE(AUC=0.869).Conclusion rDWImean,rADCmean and rT1-CEmean are correlated with Ki-67 LI.The radiomics model based on MRI sequences combination is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.