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Abstract：
Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI).
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.
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.
摘要：
背景：前列腺癌（PCa）是全球男性最常见的癌症之一，及时诊断和治疗变得越来越重要。MRI越来越多地用于诊断癌症，并区分非临床意义和临床意义的PCa。导致更精确的诊断和治疗。这项研究的目的是提出一种基于影像组学的方法，用于使用多参数MRI（mp-MRI）上的肿瘤异质性来确定PCa的Gleason评分（GS）。
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。
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。
