Multiparametric MRI-Based Radiomics for Identifying Primary Central Nervous System Diffuse Large B-cell Lymphomas' Pathological Subtypes.

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2025-05-09 DOI:10.1016/j.acra.2025.04.046

Hao Liu, Mengyang He, Eryuan Gao, Yong Zhang, Jingliang Cheng, Guohua Zhao

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引用次数: 0

Abstract

Rationale and objectives: 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).

Methods: 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.

Results: 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.

Conclusion: Radiomics based on multiparametric MRI have independent value in predicting the pathological subtypes of PCNS-DLBCL patients.

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多参数mri放射组学鉴定原发性中枢神经系统弥漫性大b细胞淋巴瘤病理亚型。

理由和目的：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。方法：本研究招募了186例PCNS-DLBCL患者，其中生发中心b细胞样（GCB）亚型55例，非GCB亚型131例。分别在t1加权图像（T1WI）、T2加权图像、T2液体衰减反转恢复、对比增强t1加权（CE-T1WI）和表观扩散系数（ADC）图上自动分割肿瘤最大异常信号区域。从预处理的多参数术前MRI图像中提取放射组学特征。为了鉴定GCB和非GCB亚型，基于每个MRI序列和序列组合构建放射组学模型。建立临床模型和放射组学与临床特征相结合的模型进行性能比较。结果：多序列组合放射组学模型总体优于单序列放射组学模型。ADC+CE-T1WI模型具有较好的判别能力，曲线下面积为0.867 （95% CI, 0.745 ~ 0.988）。包含更多序列（3-5个序列）的模型没有表现出更好的性能。放射组学特征与临床特征相结合的模型的性能没有改善。结论：基于多参数MRI的放射组学在预测PCNS-DLBCL患者病理亚型方面具有独立价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.