Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo Institute of Life and Health Industry University of Chinese Academy of Sciences, Ningbo, Zhejiang 315010, China.
Ninghai First Hospital, Ningbo, Zhejiang 315600, China.
J Healthc Eng. 2021 Nov 24;2021:6088322. doi: 10.1155/2021/6088322. eCollection 2021.
The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination.
Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC).
This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other.
The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.
本研究旨在通过磁共振成像（MRI）检查，探讨计算机半自动分割算法对乳腺肿瘤组织学分级的预测分类准确性。
采用计算机半自动分割方法获取 5 个动态对比增强（DCE）MRI 感兴趣区（ROI），分别为全肿瘤区、肿瘤边界区、近腺区、中腺区和远腺区。根据互信息最大化协议，从扩散加权成像（DWI）与 DCE-MRI 图像中提取相应的 5 个 ROI。以 DWI 图像和 DCE-MRI 图像非重叠区的特征为元素，建立单变量逻辑回归模型，对应元素特征。经过多次训练，使用受试者工作特征（ROC）曲线和曲线下面积（AUC）对模型进行评估。
DCE-MRI 联合 DWI 预测肿瘤区特征优于 DCE-MRI 和 DW。单独使用 DCE-MRI 或 DWI 效果不如 DCE-MRI 联合 DWI。DWI 联合 DCE-MRI 在肿瘤区域具有良好的区域分割效果，管腔 A 值为 0.767，曲线下面积（AUC）值为 0.758。经过优化，肿瘤区的 AUC 值为 0.929，表明两种成像方法的结合可以增强分类效果，相互补充。
DWI 联合 DCE-MRI 成像通过对生物标志物的标记预测乳腺癌的发生，提高了乳腺癌的早期诊断效果。
