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基于从肿瘤内和瘤周MRI成像中提取的影像组学特征预测胶质母细胞瘤患者的MGMT甲基化状态。

Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging.

作者信息

Chen Wang-Sheng, Fu Fang-Xiong, Cai Qin-Lei, Wang Fei, Wang Xue-Hua, Hong Lan, Su Li

机构信息

Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, 570311, China.

Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, 385a Glossop Road, Sheffield, S10 2HQ, South Yorkshire, UK.

出版信息

Sci Rep. 2025 Jul 29;15(1):27533. doi: 10.1038/s41598-025-08608-9.

DOI:10.1038/s41598-025-08608-9
PMID:40730593
Abstract

Assessing MGMT promoter methylation is crucial for determining appropriate glioblastoma therapy. Previous studies have focused on intratumoral regions, overlooking the peritumoral area. This study aimed to develop a radiomic model using MRI-derived features from both regions. We included 96 glioblastoma patients randomly allocated to training and testing sets. Radiomic features were extracted from intratumoral and peritumoral regions. We constructed and compared radiomic models based on intratumoral, peritumoral, and combined features. Model performance was evaluated using the area under the receiver-operating characteristic curve (AUC). The combined radiomic model achieved an AUC of 0.814 (95% CI: 0.767-0.862) in the training set and 0.808 (95% CI: 0.736-0.859) in the testing set, outperforming models based on intratumoral or peritumoral features alone. Calibration and decision curve analyses demonstrated excellent model fit and clinical utility. The radiomic model incorporating both intratumoral and peritumoral features shows promise in differentiating MGMT methylation status, potentially informing clinical treatment strategies for glioblastoma.

摘要

评估O6-甲基鸟嘌呤-DNA甲基转移酶(MGMT)启动子甲基化对于确定合适的胶质母细胞瘤治疗方法至关重要。以往的研究主要集中在肿瘤内部区域,而忽略了肿瘤周围区域。本研究旨在利用来自这两个区域的磁共振成像(MRI)衍生特征开发一种放射组学模型。我们纳入了96例胶质母细胞瘤患者,将其随机分配到训练集和测试集。从肿瘤内部和肿瘤周围区域提取放射组学特征。我们基于肿瘤内部、肿瘤周围和联合特征构建并比较了放射组学模型。使用受试者操作特征曲线(ROC)下面积(AUC)评估模型性能。联合放射组学模型在训练集中的AUC为0.814(95%置信区间:0.767 - 0.862),在测试集中为0.808(95%置信区间:0.736 - 0.859),优于仅基于肿瘤内部或肿瘤周围特征的模型。校准和决策曲线分析显示模型拟合良好且具有临床实用性。结合肿瘤内部和肿瘤周围特征的放射组学模型在区分MGMT甲基化状态方面显示出前景,可能为胶质母细胞瘤的临床治疗策略提供参考。

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MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.基于磁共振成像的瘤内及瘤周影像组学用于术前预测胶质瘤分级:一项多中心研究
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通过基于遗传算法的机器学习方法优化放射组学特征来提高胶质母细胞瘤 MGMT 甲基化状态的预测。
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Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial.基于前瞻性DIRECTOR试验的多中心磁共振成像的放射组学分析预测复发性胶质母细胞瘤的预后
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