基于mri的栖息地放射组学在胶质瘤术前预测IDH状态。

IF 3 2区 医学 Q2 CLINICAL NEUROLOGY
Wan-Yi Zheng, Ri-Hui Yang, Zhi-Fang Wan, Hui Li, Chao Ma, Qun-Hui Ouyang, Si Li, Ke-Jian Wang, Gui-Hua Jiang, Ping Liu
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引用次数: 0

摘要

目的:胶质瘤内异质血管通透性和细胞密度与IDH突变状态相关。因此,作者旨在基于MRI构建血管细胞栖息地,探讨其与IDH状态的相关性。方法:回顾性分析3家医院165例经病理证实的胶质瘤患者术前行对比增强t1加权成像和弥散加权成像(DWI)检查。基于对比度增强的t1加权和dwi衍生的表观扩散系数(ADC)图像,采用K-means体素聚类方法定义了4个空间栖息地(子区域)。确定了IDH突变的敏感生境,并从整个肿瘤和四个生境中提取和筛选放射学特征。采用逻辑回归分类器构建IDH突变预测模型。结果:纳入的患者中,109例(平均年龄50.78岁)被分配到训练集,56例(平均年龄48.21岁)被分配到外部测试集。确定高对比度增强(CE)-高ADC亚区为敏感生境。四生境模型在外部测试集中的受试者工作特征曲线下面积(AUC)为0.716 (95% CI 0.553-0.879),优于传统的全肿瘤模型(AUC 0.619, 95% CI 0.446-0.792)。当关注敏感生境时,模型性能进一步提高,外部测试集AUC为0.817 (95% CI 0.676-0.958)。结论:基于对比增强t1加权和DWI序列的MRI栖息地分析对胶质瘤IDH突变状态具有较高的预测能力,可用于细化胶质瘤患者的个体化治疗方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI-based habitat radiomics for preoperatively predicting IDH status in gliomas.

Objective: The intratumoral heterogeneous vascular permeability and cell density of gliomas are associated with IDH mutation status. Therefore, the authors aimed to construct vascular-cellular habitats based on MRI to investigate their correlation with IDH status.

Methods: In this retrospective analysis, 165 patients with pathologically confirmed glioma who underwent preoperative contrast-enhanced T1-weighted imaging and diffusion-weighted imaging (DWI) at three hospitals were included. Four spatial habitats (subregions) based on contrast-enhanced T1-weighted and DWI-derived apparent diffusion coefficient (ADC) images were defined using K-means voxel-wise clustering. The sensitive habitat of IDH mutation was identified and radiomic features were extracted and screened from the whole tumor and the four habitats. Logistic regression classifiers were used to construct predictive models for IDH mutation.

Results: Of the included patients, 109 (mean age 50.78 years) were assigned to the training set and 56 (mean age 48.21 years) to the external test set. The high contrast enhancement (CE)-high ADC subregion was determined as the sensitive habitat. The four habitats model achieved an area under the receiver operating characteristic curve (AUC) of 0.716 (95% CI 0.553-0.879) in the external test set, indicating better performance than that of the traditional whole tumor model (AUC 0.619, 95% CI 0.446-0.792). Model performance was further improved when focusing on the sensitive habitat, for which the external test set AUC was 0.817 (95% CI 0.676-0.958).

Conclusions: MRI habitat analysis based on contrast-enhanced T1-weighted and DWI sequences had high prediction capabilities for glioma IDH mutation status, which could be used to refine individualized treatment regimens for patients with glioma.

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来源期刊
Neurosurgical focus
Neurosurgical focus CLINICAL NEUROLOGY-SURGERY
CiteScore
6.30
自引率
0.00%
发文量
261
审稿时长
3 months
期刊介绍: Information not localized
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