Yang Zhen, Zhang Peng, Ding Yi, Deng Liyi, Zhang Tong, Liu Yong
Department of Neurosurgery, The Second People's Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, China.
Department of Neurosurgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Front Neurol. 2025 Jan 29;16:1518815. doi: 10.3389/fneur.2025.1518815. eCollection 2025.
To explore the value of deep learning based on magnetic resonance imaging (MRI) in the classification of glioma subtypes.
This study retrospectively included 747 adult patients with surgically pathologically confirmed gliomas from a public database and 64 patients from our hospital. Patients were classified into IDH-wildtype (IDHwt) (490 cases), IDH-mutant/1p19q-noncodeleted (IDHmut-intact) (105 cases), and IDH-mutant/1p19q-codeleted (IDHmut-codel) (216 cases) based on their pathological findings, with the public database of patients were divided into training and validation sets, and patients from our hospital were used as an independent test set. The models were developed based on five categories of preoperative T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted and T2-weighted fluid-attenuated inversion recovery (T1w, T1c, T2w and FLAIR) magnetic resonance imaging (MRI) of four sequences and mixed imaging of the four sequences, respectively. The receiver operating characteristic curve (ROC), area under the curve (AUC) of the ROC were generated in the jupyter notebook tool using python language to evaluate the accuracy of the models in classification and comparing the predictive value of different MRI sequences.
IDHwt, IDHmut-intact and IDHmut-codel were the best classified in the model containing only FLAIR sequences, with test set AUCs of 0.790, 0.737 and 0.820, respectively; and the worst classified in the model containing only T1w sequences, with test set AUCs of 0.621, 0.537 and 0.760, respectively.
We have developed a set of models that can effectively classify glioma subtypes and that work best when only the FLAIR sequence model is included.
探讨基于磁共振成像（MRI）的深度学习在胶质瘤亚型分类中的价值。
本研究回顾性纳入了来自公共数据库的747例经手术病理证实的成人胶质瘤患者以及我院的64例患者。根据病理结果，将患者分为异柠檬酸脱氢酶野生型（IDHwt）（490例）、异柠檬酸脱氢酶突变型/1p19q未缺失型（IDHmut完整型）（105例）和异柠檬酸脱氢酶突变型/1p19q缺失型（IDHmut缺失型）（216例），公共数据库中的患者分为训练集和验证集，我院的患者作为独立测试集。分别基于术前T1加权、T1加权钆对比增强、T2加权和T2加权液体衰减反转恢复（T1w、T1c、T2w和FLAIR）四种序列的磁共振成像（MRI）以及这四种序列的混合成像开发模型。使用Python语言在Jupyter Notebook工具中生成受试者操作特征曲线（ROC）及其曲线下面积（AUC），以评估模型在分类中的准确性并比较不同MRI序列的预测价值。
在仅包含FLAIR序列的模型中，IDHwt、IDHmut完整型和IDHmut缺失型分类效果最佳，测试集AUC分别为0.790、0.737和0.820；在仅包含T1w序列的模型中分类效果最差，测试集AUC分别为0.621、0.537和0.760。
我们开发了一套能够有效分类胶质瘤亚型的模型，其中仅包含FLAIR序列模型时效果最佳。
