Koska İlker Özgür, Koska Çağan
Department of Radiology, Behçet Uz Children's Hospital, Izmir, Turkey.
Department of Biomedical Technologies, Dokuz Eylül Universtiy The Graduate School of Natural and Applied Sciences, Buca, Izmir, Turkey.
Sci Rep. 2025 Jan 25;15(1):3273. doi: 10.1038/s41598-025-87803-0.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used. A comprehensive mask fusion approach was developed to select relevant image crops of diseased tissue. These fusion masks, which were guided by multiple sequences, helped collect information from the regions that seem disease-free to radiologists in standard MRI sequences while harboring pathology. Integrating the information in different MRI sequences and leveraging the high entropic capacity of deep neural networks, we built a 3D ROI-based custom CNN classifier for the automatic prediction of MGMT methylation status of glioblastoma in multi-parametric MRI. Single sequence-based classifiers reached intermediate predictive performance with 0.65, 0.71, 0.77, and 0.82 accuracy for T1W, T2W, T1 contrast-enhanced, and FLAIR sequences, respectively. The multiparametric classifier using T1 contrast-enhanced and FLAIR images reached 0.88 accuracy. The accuracy of the four-input model that used all sequences was 0.81. The best model reached 0.90 ROC AUC value. Integrating human knowledge in the form of relevant target selection was a useful approach in MGMT methylation status prediction in MRI. Exploration of means to integrate radiology knowledge into the models and achieve human-machine collaboration may help to develop better models. MGMT methylation status of glioblastoma is an important prognostic marker and is also important for treatment decisions. The preoperative non-invasive predictive ability and the explanation tools of the developed model may help clinicians to better understand imaging phenotypes of MGMT methylation status of glial tumors.
我们旨在构建一个用于多参数磁共振成像（MRI）中胶质母细胞瘤MGMT甲基化状态的强大分类器。我们将重点放在多栖息地深度图像描述符上。使用了包含MGMT类别标签和分割掩码的BRATS 2021 MGMT甲基化数据集的一个子集。开发了一种综合掩码融合方法来选择患病组织的相关图像区域。这些由多个序列引导的融合掩码有助于从标准MRI序列中放射科医生看似无病变但存在病理的区域收集信息。通过整合不同MRI序列中的信息并利用深度神经网络的高熵能力，我们构建了一个基于三维感兴趣区域（ROI）的定制卷积神经网络（CNN）分类器，用于在多参数MRI中自动预测胶质母细胞瘤的MGMT甲基化状态。基于单序列的分类器分别在T1加权（T1W）、T2加权（T2W）、T1增强对比、液体衰减反转恢复（FLAIR）序列上达到了中等预测性能，准确率分别为0.65、0.71、0.77和0.82。使用T1增强对比和FLAIR图像的多参数分类器准确率达到0.88。使用所有序列的四输入模型准确率为0.81。最佳模型达到了0.90的受试者工作特征曲线下面积（ROC AUC）值。以相关目标选择的形式整合人类知识是MRI中MGMT甲基化状态预测的一种有用方法。探索将放射学知识整合到模型中并实现人机协作的方法可能有助于开发更好的模型。胶质母细胞瘤的MGMT甲基化状态是一个重要的预后标志物，对治疗决策也很重要。所开发模型的术前非侵入性预测能力和解释工具可能有助于临床医生更好地理解胶质肿瘤MGMT甲基化状态的影像表型。
