Department of Neurosurgery, The First People's Hospital of Lianyungang/The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang 222000, Jiangsu, China.
Contrast Media Mol Imaging. 2022 May 24;2022:4938587. doi: 10.1155/2022/4938587. eCollection 2022.
The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on a convolutional neural network (CNN) algorithm in glioma diagnosis and tumor segmentation. 66 patients with gliomas who were diagnosed and treated in the hospital were selected as the research objects. The patients were rolled into the high-grade glioma group (HGG, 46 cases) and the low-grade glioma group (LGG, 20 cases) according to the World Health Organization glioma grading standard. All patients received a conventional plain scan and a DCE-MRI. Parameters such as volume transfer constant ( ), rate constant ( ), extracellular volume ( ), and mean plasma volume ( ) were calculated, and the parameters of patients of each grade were analyzed. The efficacy of each parameter in diagnosing glioma was analyzed through a receiver operating characteristic curve. All images were segmented by the CNN algorithm. The CNN algorithm showed good performance in DCE-MRI image segmentation. The mean, standard deviation, kurtosis, and skewness of and , the standard deviation and skewness of , and the mean and standard deviation of were statistically considerable in differentiating HGG and LGG ( < 0.05). ROC analysis showed that the standard deviation of (0.885) had the highest diagnostic accuracy in distinguishing HGG and LGG. The values of , , and were positively correlated with Ki-67 ( = 0.346, = 0.014; = 0.335, = 0.017; = 0.323, = 0.022). In summary, the CNN-based DCE-MRI technology had high application value in glioma diagnosis and tumor segmentation.
本研究旨在探讨基于卷积神经网络（CNN）算法的动态对比增强磁共振成像（DCE-MRI）在胶质瘤诊断和肿瘤分割中的应用价值。选取在医院诊断和治疗的 66 例胶质瘤患者为研究对象，根据世界卫生组织胶质瘤分级标准将患者分为高级别胶质瘤组（HGG，46 例）和低级别胶质瘤组（LGG，20 例）。所有患者均接受常规平扫和 DCE-MRI 检查，计算容积转移常数（）、速率常数（）、细胞外容积（）和平均血浆容积（）等参数，并对各级患者的参数进行分析，通过受试者工作特征曲线分析各参数诊断胶质瘤的效能。所有图像均由 CNN 算法进行分割。CNN 算法在 DCE-MRI 图像分割中表现出良好的性能。和的平均值、标准差、峰度和偏度，的标准差和偏度，以及的平均值和标准差在区分 HGG 和 LGG 方面具有统计学意义（<0.05）。ROC 分析显示，的标准差（0.885）在区分 HGG 和 LGG 方面具有最高的诊断准确性。、和与 Ki-67 呈正相关（=0.346，=0.014；=0.335，=0.017；=0.323，=0.022）。综上所述，基于 CNN 的 DCE-MRI 技术在胶质瘤诊断和肿瘤分割中具有较高的应用价值。
