Liu Hao, He Mengyang, Gao Eryuan, Zhang Yong, Cheng Jingliang, Zhao Guohua
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China (H.L., E.G., Y.Z., J.C., G.Z.).
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China (M.H.).
Acad Radiol. 2025 Sep;32(9):5478-5487. doi: 10.1016/j.acra.2025.04.046. Epub 2025 May 9.
To explore the predictive potential of radiomics features extracted from preoperative multiparametric magnetic resonance imaging (MRI) for identifying pathological subtypes of primary central nervous system diffuse large B-cell lymphomas (PCNS-DLBCL).
This study recruited 186 patients with PCNS-DLBCL, including 55 with germinal center B-cell-like (GCB) subtype and 131 with non-GCB subtype. The largest abnormal signal regions of the tumor were automatically segmented in T1-weighted images (T1WI), T2-weighted images, T2 fluid-attenuated inversion recovery, contrast-enhanced T1-weighted (CE-T1WI), and apparent diffusion coefficient (ADC) maps, respectively. Radiomics features were extracted from preprocessed multiparameter preoperative MRI images. To identify GCB and non-GCB subtypes, radiomics models were constructed based on each MRI sequence and combinations of sequences. Clinical models and models combining radiomics and clinical features were also constructed to compare performance.
Radiomics models combining multiple sequences generally outperformed single-sequence radiomics models. The ADC+CE-T1WI model exhibited superior discriminative power, with an area under the curve of 0.867 (95% CI, 0.745-0.988). Models incorporating more sequences (3-5 sequences) did not demonstrate better performance. The performance of the model combining radiomics features with clinical features showed no improvement.
Radiomics based on multiparametric MRI have independent value in predicting the pathological subtypes of PCNS-DLBCL patients.
探讨从术前多参数磁共振成像（MRI）中提取的放射组学特征对原发性中枢神经系统弥漫性大B细胞淋巴瘤（PCNS-DLBCL）病理亚型的预测潜力。
本研究纳入186例PCNS-DLBCL患者，其中55例为生发中心B细胞样（GCB）亚型，131例为非GCB亚型。分别在T1加权像（T1WI）、T2加权像、T2液体衰减反转恢复序列、对比增强T1加权像（CE-T1WI）和表观扩散系数（ADC）图中自动分割肿瘤最大异常信号区域。从预处理的术前多参数MRI图像中提取放射组学特征。为识别GCB和非GCB亚型，基于每个MRI序列及其序列组合构建放射组学模型。还构建了临床模型以及结合放射组学和临床特征的模型以比较性能。
结合多个序列的放射组学模型通常优于单序列放射组学模型。ADC+CE-T1WI模型表现出卓越的鉴别能力，曲线下面积为0.867（95%CI，0.745-0.988）。纳入更多序列（3-5个序列）未显示出更好的性能表现。结合放射组学特征与临床特征的模型性能未得到改善。
基于多参数MRI的放射组学在预测PCNS-DLBCL患者的病理亚型方面具有独立价值。
