Shen Yelong, Wu Siyu, Wu Yanan, Cui Chao, Li Haiou, Yang Shuang, Liu Xuejun, Chen Xingzhi, Huang Chencui, Wang Ximing
Department of Radiology, Shandong Provincial Hospital, No. 324, Jingwu Road, Jinan, 250021, Shandong, China.
Department of Radiology, Shandong University, No. 44, West Wenhua Road, Jinan, 250021, Shandong, China.
BMC Med Imaging. 2025 Feb 17;25(1):54. doi: 10.1186/s12880-025-01585-5.
To examine the correlation of apparent diffusion coefficient (ADC), diffusion weighted imaging (DWI), and T1 contrast enhanced (T1-CE) with Ki-67 in primary central nervous system lymphomas (PCNSL). And to assess the diagnostic performance of MRI radiomics-based machine-learning algorithms in differentiating the high proliferation and low proliferation groups of PCNSL.
83 patients with PCNSL were included in this retrospective study. ADC, DWI and T1-CE sequences were collected and their correlation with Ki-67 was examined using Spearman's correlation analysis. The Kaplan-Meier method and log-rank test were used to compare the survival rates of the high proliferation and low proliferation groups. The radiomics features were extracted respectively, and the features were screened by machine learning algorithm and statistical method. Radiomics models of seven different sequence permutations were constructed. The area under the receiver operating characteristic curve (ROC AUC) was used to evaluate the predictive performance of all models. DeLong test was utilized to compare the differences of models.
Relative mean apparent diffusion coefficient (rADCmean) (ρ=-0.354, p = 0.019), relative mean diffusion weighted imaging (rDWImean) (b = 1000) (ρ = 0.273, p = 0.013) and relative mean T1 contrast enhancement (rT1-CEmean) (ρ = 0.385, p = 0.001) was significantly correlated with Ki-67. Interobserver agreements between the two radiologists were almost perfect for all parameters (rADCmean ICC = 0.978, 95%CI 0.966-0.986; rDWImean (b = 1000) ICC = 0.931, 95% CI 0.895-0.955; rT1-CEmean ICC = 0.969, 95% CI 0.953-0.980). The differences in PFS (p = 0.016) and OS (p = 0.014) between the low and high proliferation groups were statistically significant. The best prediction model in our study used a combination of ADC, DWI, and T1-CE achieving the highest AUC of 0.869, while the second ranked model used ADC and DWI, achieving an AUC of 0.828.
rDWImean, rADCmean and rT1-CEmean were correlated with Ki-67. The radiomics model based on MRI sequences combined is promising to distinguish low proliferation PCNSL from high proliferation PCNSL.
探讨原发性中枢神经系统淋巴瘤（PCNSL）中表观扩散系数（ADC）、扩散加权成像（DWI）和T1加权增强（T1-CE）与Ki-67的相关性。并评估基于MRI影像组学的机器学习算法在区分PCNSL高增殖组和低增殖组中的诊断性能。
本回顾性研究纳入83例PCNSL患者。收集ADC、DWI和T1-CE序列，采用Spearman相关性分析检验它们与Ki-67的相关性。采用Kaplan-Meier法和对数秩检验比较高增殖组和低增殖组的生存率。分别提取影像组学特征，并通过机器学习算法和统计方法进行特征筛选。构建了七种不同序列排列的影像组学模型。采用受试者操作特征曲线下面积（ROC AUC）评估所有模型的预测性能。利用DeLong检验比较模型间的差异。
相对平均表观扩散系数（rADCmean）（ρ=-0.354，p = 0.019）、相对平均扩散加权成像（rDWImean）（b = 1000）（ρ = 0.273，p = 0.013）和相对平均T1加权增强（rT1-CEmean）（ρ = 0.385，p = 0.001）与Ki-67显著相关。两位放射科医生之间对所有参数的观察者间一致性几乎完美（rADCmean ICC = 0.978，95%CI 0.966-0.986；rDWImean（b = 1000）ICC = 0.931，95%CI 0.895-0.955；rT1-CEmean ICC = 0.969，95%CI 从0.953到0.980）。低增殖组和高增殖组之间的无进展生存期（PFS）（p = 0.016）和总生存期（OS）（p = 0.014）差异具有统计学意义。本研究中最佳预测模型采用ADC、DWI和T1-CE的组合，AUC最高达到0.869，而排名第二的模型采用ADC和DWI，AUC为0.828。
rDWImean、rADCmean和rT1-CEmean与Ki-67相关。基于MRI序列组合的影像组学模型有望区分低增殖PCNSL和高增殖PCNSL。
