Aleid Abdulsalam Mohammed, Alrasheed Abdulrahim Saleh, Aldanyowi Saud Nayef, Almalki Sami Fadhel
Department of Surgery, College of Medicine, King Faisal University, AlAhsa, Saudi Arabia.
Surg Neurol Int. 2024 Aug 30;15:309. doi: 10.25259/SNI_498_2024. eCollection 2024.
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.
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.
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.
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.
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，但使用传统方法将其与其他病变区分开来可能很困难。本研究探索先进的MRI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，提取影像组学特征，并使用统计方法、比例风险回归和Kaplan-Meier生存分析来分析液体衰减反转恢复（FLAIR）切除对生存的影响。
预测模型在胶质瘤分级预测方面达到了较高的准确率（曲线下面积为0.902）。FLAIR异常切除显著改善了生存率，而扩散加权图像最能清晰显示肿瘤浸润情况。与转移瘤相比，先进的MRI能更好地显示胶质母细胞瘤的浸润情况。胶质母细胞瘤表现出独特的特征，包括形状不规则、边缘不规则和强化情况，而转移瘤呈椭圆形或圆形，边缘清晰，对比度均匀，且瘤周改变广泛。
对MRI进行先进的影像组学和机器学习分析可以提供具有临床相关性的非侵入性胶质瘤分级和肿瘤特性表征。将先进的神经影像学与组织病理学相结合，可能会更好地整合肿瘤学和放射学，以优化胶质母细胞瘤的治疗。然而，需要进一步的研究用更大的数据集来验证这些发现，并评估其他MRI序列和影像组学特征。
