Prediction of early recurrence in primary central nervous system lymphoma based on multimodal MRI-based radiomics: A preliminary study

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-04-18 DOI:10.1016/j.ejrad.2025.112125

Xiaochen Wang , Sihui Wang , Xuening Zhao , Lingxu Chen , Mengyuan Yuan , Ying Yan , Xuefei Sun , Yuanbo Liu , Shengjun Sun

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引用次数: 0

Abstract

Objectives

To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis.

Materials and methods

A retrospective analysis was conducted on 145 patients with PCNSL who were treated with high-dose methotrexate-based chemotherapy. Clinical data and MRI images were collected, with tumor regions segmented using ITK-SNAP software. Radiomics features were extracted via Pyradiomics, and predictive models were developed using various machine learning algorithms. The predictive performance of these models was assessed using receiver operating characteristic (ROC) curves. Additionally, Cox regression analysis was employed to identify risk factors associated with progression-free survival (PFS).

Results

In the cohort of 145 PCNSL patients (72 recurrence, 73 non-recurrence), clinical characteristics were comparable between groups except for multiple lesion frequency (61.1% vs. 39.7%, p < 0.05) and not receiving consolidation therapy (44.4% vs. 13.7%, p < 0.05). A total of 2392 radiomics features were extracted from CET1 and T2WI MRI sequence. Combining clinical variables, 10 features were retained after the feature selection process. The logistic regression (LR) model exhibited superior predictive performance in the test set to predict PCNSL early relapse, with an area under the curve (AUC) of 0.887 (95 % confidence interval: 0.785–0.988). Multivariate Cox regression identified the Cli-Rad score as an independent prognostic factor for PFS. Significant difference in PFS was observed between high- and low-risk groups defined by Cli-Rad score (8.24 months vs. 24.17 months, p < 0.001).

Conclusions

The LR model based on multimodal MRI radiomics and clinical features, can effectively predict early recurrence of PCNSL, while the Cli-Rad score could independently forecast PFS among PCNSL patients.

查看原文本刊更多论文

基于多模态mri放射组学预测原发性中枢神经系统淋巴瘤早期复发的初步研究

目的评价多模态磁共振成像放射组学特征对原发性中枢神经系统淋巴瘤（PCNSL）早期复发的预测作用，并探讨其与预后的相关性。材料与方法对145例以甲氨蝶呤为主的大剂量化疗的PCNSL患者进行回顾性分析。收集临床资料和MRI图像，使用ITK-SNAP软件对肿瘤区域进行分割。通过Pyradiomics提取放射组学特征，并使用各种机器学习算法建立预测模型。使用受试者工作特征（ROC）曲线评估这些模型的预测性能。此外，采用Cox回归分析确定与无进展生存期（PFS）相关的危险因素。结果145例PCNSL患者（复发72例，未复发73例），除多发病变频率差异外，各组间临床特征具有可比性(61.1% vs. 39.7%, p <；0.05)和未接受巩固治疗(44.4%比13.7%,p <；0.05)。从CET1和T2WI MRI序列中提取了2392个放射组学特征。结合临床变量，特征选择过程后保留10个特征。logistic回归（LR）模型对PCNSL早期复发的预测效果较好，曲线下面积（AUC）为0.887（95%置信区间：0.785 ~ 0.988）。多因素Cox回归发现，Cli-Rad评分是PFS的独立预后因素。以Cli-Rad评分定义的高危组和低危组PFS有显著差异(8.24个月vs 24.17个月，p <；0.001)。结论基于多模态MRI放射组学和临床特征的LR模型可有效预测PCNSL早期复发，而Cli-Rad评分可独立预测PCNSL患者的PFS。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.