利用影像学特征评估胶质瘤患者异柠檬酸脱氢酶突变状态的可预测性。

Surgical neurology international Pub Date : 2025-06-20 eCollection Date: 2025-01-01 DOI:10.25259/SNI_323_2025
Joao Meira Goncalves, André Miranda, Carolina Silva, Bruno Carvalho, Patricia Polónia, Paulo Linhares
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引用次数: 0

摘要

背景:放射基因组学是影像学和遗传学的交叉,在改善胶质瘤的诊断和治疗方面具有重要意义。本研究旨在将影像特征与异柠檬酸脱氢酶(IDH)突变状态联系起来,为识别胶质瘤的遗传背景提供一种非侵入性诊断工具。方法:在59例IDH野生型(WT)或IDH突变型胶质瘤患者的回顾性样本中,研究采用体积和形态磁共振成像(MRI)分析来识别基于放射学特征的分子改变。关键的成像生物标志物,如T2/液体衰减反转恢复失配、对比度增强模式和扩散/灌注指标,被评估其区分IDH-WT和突变型胶质瘤的能力。采用受试者工作特征曲线评价诊断效果,并建立基于影像学的患者分类逻辑回归模型。结果:结果显示IDH突变型胶质瘤经常表现出明显的影像学特征,如均匀的高T2信号和缺乏对比增强。此外,灌注和扩散指标在IDH-WT和突变组之间有显著差异,提供了潜在的放射基因组标记。我们建立了一个逻辑回归模型来准确预测IDH状态,识别诸如肿瘤增强大小、中心坏死、肿瘤周围水肿和患者年龄等因素。结论:该研究的结果证实了放射基因组学相关性在预测IDH状态方面的重要性,与先前的研究结果相一致。我们强调在MRI分析中多模态方法的必要性,以提高胶质瘤患者的非侵入性诊断准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the predictability of isocitrate dehydrogenase mutational status in glioma patients using imaging features.

Background: Radiogenomics, the intersection of imaging and genetics, is important in improving glioma diagnosis and treatment. This study aims to correlate imaging features with isocitrate dehydrogenase (IDH) mutation status, providing a non-invasive diagnostic tool to identify the genetic background of gliomas.

Methods: In a retrospective sample of 59 patients with either IDH wild-type (WT) or IDH mutant gliomas, the study employed volumetric and morphologic magnetic resonance imaging (MRI) analyses to discern molecular alterations based on radiographic signatures. Key imaging biomarkers, such as the T2/fluid-attenuated inversion recovery mismatch, contrast enhancement patterns, and diffusion/perfusion metrics, were evaluated for their ability to differentiate between IDH-WT and mutant gliomas. Receiver operating characteristic curves were employed to evaluate diagnostic performance, and a logistic regression model was developed for patient classification based on imaging.

Results: The results revealed that IDH mutant gliomas frequently exhibited distinct imaging characteristics, such as homogenous hyperintense T2 signals and absence of contrast enhancement. In addition, perfusion and diffusion metrics varied significantly between the IDH-WT and mutant groups, offering potential radiogenomic markers. A logistic regression model was developed to predict IDH status with high accuracy, identifying factors such as tumor enhancement size, presence of central necrosis, peritumoral edema, and patient age.

Conclusion: The study's results affirm the significance of radiogenomic correlations in predicting IDH status, resonating findings from prior research. We highlight the necessity of a multimodal approach in MRI analysis to enhance the non-invasive diagnostic accuracy for glioma patients.

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