胶质母细胞瘤的高级磁共振成像:肿瘤学与放射学的整合。

Surgical neurology international Pub Date : 2024-08-30 eCollection Date: 2024-01-01 DOI:10.25259/SNI_498_2024
Abdulsalam Mohammed Aleid, Abdulrahim Saleh Alrasheed, Saud Nayef Aldanyowi, Sami Fadhel Almalki
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引用次数: 0

摘要

背景:多形性胶质母细胞瘤(GBM)等侵袭性脑肿瘤预后较差。虽然磁共振成像(MRI)对 GBM 的治疗至关重要,但使用传统方法很难将其与其他病变区分开来。本研究探索先进的磁共振成像技术,以更好地了解 GBM 的特性及其与患者预后的关系:我们研究了 2020 年 1 月至 2024 年 3 月期间 157 例 GBM 手术患者的 MRI 扫描图像,以提取放射学特征,并使用统计方法、比例危险回归和 Kaplan-Meier 生存分析,分析流体增强反转恢复(FLAIR)切除对生存的影响:预测模型对胶质瘤分级预测的准确率很高(曲线下面积为 0.902)。FLAIR异常切除能显著提高生存率,而弥散加权图像能最好地显示肿瘤浸润情况。与转移瘤相比,胶质母细胞瘤的浸润在晚期磁共振成像中显示得最好。与转移瘤相比,胶质母细胞瘤显示出明显的特征,包括不规则的形状、边缘和强化,而转移瘤呈椭圆形或圆形,边缘清晰,对比度均匀,瘤周变化广泛:结论:先进的磁共振成像放射学和机器学习分析可提供无创胶质瘤分级和具有临床意义的肿瘤特性特征。将先进的神经影像学与组织病理学相结合,可以更好地整合肿瘤学和放射学,优化胶质母细胞瘤的治疗。不过,还需要进一步的研究,用更大的数据集来验证这些发现,并评估更多的磁共振成像序列和放射学特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced magnetic resonance imaging for glioblastoma: Oncology-radiology integration.

Background: Aggressive brain tumors like glioblastoma multiforme (GBM) pose a poor prognosis. While magnetic resonance imaging (MRI) is crucial for GBM management, distinguishing it from other lesions using conventional methods can be difficult. This study explores advanced MRI techniques better to understand GBM properties and their link to patient outcomes.

Methods: We studied MRI scans of 157 GBM surgery patients from January 2020 to March 2024 to extract radiomic features and analyze the impact of fluid-attenuated inversion recovery (FLAIR) resection on survival using statistical methods, proportional hazards regression, and Kaplan-Meier survival analysis.

Results: Predictive models achieved high accuracy (area under the curve of 0.902) for glioma-grade prediction. FLAIR abnormality resection significantly improved survival, while diffusion-weighted image best-depicted tumor infiltration. Glioblastoma infiltration was best seen with advanced MRI compared to metastasis. Glioblastomas showed distinct features, including irregular shape, margins, and enhancement compared to metastases, which were oval or round, with clear edges and even contrast, and extensive peritumoral changes.

Conclusion: Advanced radiomic and machine learning analysis of MRI can provide noninvasive glioma grading and characterization of tumor properties with clinical relevance. Combining advanced neuroimaging with histopathology may better integrate oncology and radiology for optimized glioblastoma management. However, further studies are needed to validate these findings with larger datasets and assess additional MRI sequences and radiomic features.

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