Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification.
The highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively.
Our results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.
方法:本研究纳入了26例经活检证实的PCa患者。定量T2值,使用多回波T2图像计算表观扩散系数(ADC)和信号增强率(α),弥散加权成像(DWI)和动态对比增强MRI(DCE-MRI),用于带注释的兴趣区域(ROI)。纹理特征分析后,进行ROI范围扩展和特征过滤。然后将获得的数据放入支持向量机(SVM),K-最近邻(KNN)和其他用于二元分类的分类器。
结果:区分有临床意义(格里森3+4及以上)和无意义癌症(格里森3+3)的最高分类准确率为73.96%,区分格里森3+4和格里森4+3及以上的最高分类准确率为83.72%。这是使用放射科医生绘制的初始ROI实现的。当使用扩展ROI时,使用SVM将准确性提高到80.67%,使用贝叶斯分类将临床显着和非显着癌症以及Gleason34与Gleason43及以上区分开来为88.42%。分别。
结论:我们的结果表明了这项研究对使用ROI区域扩展确定前列腺癌GS的研究意义和价值。