To explore the application value of MRI in the diagnosis of brain glioma (BG), in the study, a deep learning-based multimodal feature fusion model was established, which was then applied in BG classification. 60 BG patients who came to our hospital for treatment were selected as research subjects. They all accepted the MRI scan and the enhanced scan, and the MRI results were compared with the pathological results. The results showed that the sensitivity of the algorithm was above 90%, and the sensitivity to diagnose grade IV glioma was as high as 98.28%; the specificity was above 78%, and the specificity to diagnose grade IV glioma was as high as 95.85%; the detection accuracy was above 95%. The relative fractional anisotropy (rFA) values of the tumor body were smaller than those of peritumoral edema in both the high-grade group and low-grade group, and the difference was notable ; the relative apparent diffusion coefficients (rADC) values of the peritumoral edema were greater than those of tumor bodies of the same grade in both the high-grade group and the low-grade group, and the difference was notable ; notable differences were noted in the rADC values of tumor bodies between the high-grade group and the low-grade group and in the rADC values of the glioma peritumoral edema between the high-grade group and the low-grade group . In summary, MRI based on deep learning raises the sensitivity, specificity, and accuracy to diagnose BG and can more accurately classify BG pathologically, providing reference for clinical treatment of BG.

中文翻译：

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基于深度学习的磁共振成像图像特征分析用于脑胶质瘤病理分类

为探讨MRI在脑胶质瘤（BG）诊断中的应用价值，本研究建立了基于深度学习的多模态特征融合模型，并将其应用于BG分类。选取来我院就诊的60例BG患者作为研究对象。他们都接受了MRI扫描和增强扫描，并将MRI结果与病理结果进行了比较。结果表明，该算法的灵敏度在90%以上，诊断IV级胶质瘤的灵敏度高达98.28%；特异性在78%以上，诊断IV级胶质瘤的特异性高达95.85%；检测准确率在95%以上。; 高级别组和低级别组瘤周水肿相对表观扩散系数（rADC）值均大于同级别瘤体，差异显着; 高级别组与低级别组瘤体rADC值存在显着差异 以及高级别组和低级别组之间胶质瘤瘤周水肿的rADC值 . 综上所述，基于深度学习的MRI提高了诊断BG的敏感性、特异性和准确性，可以更准确地对BG进行病理分类，为BG的临床治疗提供参考。