Ogbonnaya Chidozie N, Zhang Xinyu, Alsaedi Basim S O, Pratt Norman, Zhang Yilong, Johnston Lisa, Nabi Ghulam
Division of Imaging Science and Technology, University of Dundee, Dundee DD1 4HN, UK.
College of Basic Medical and Health Sciences, Abia State University, Uturu 441103, Nigeria.
Cancers (Basel). 2021 Dec 9;13(24):6199. doi: 10.3390/cancers13246199.
Texture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer.
This was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal-Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm-Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves.
We identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859-0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients ( < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI.
Quantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.
基于像素空间关系的纹理特征，即灰度共生矩阵（GLCM），可能在疑似前列腺癌的准确分类中发挥重要作用。本研究的目的是使用活检前多参数磁共振成像（mpMRI）的定量成像参数来预测具有临床意义的前列腺癌。
这是一项前瞻性研究，招募了200名疑似患有前列腺癌的男性。参与者在活检前使用基于协议的3T MRI进行成像。从T2WI中提取放射组学参数，并从感兴趣区域勾勒出灰度共生矩阵的ADC纹理特征。根治性前列腺切除术组织病理学用作参考标准。首先应用Kruskal-Wallis检验来识别三组Gleason评分（即G1、G2和G3）之间的显著放射组学特征。随后，应用Holm-Bonferroni方法来校正和控制错误拒绝的概率。我们使用受试者操作特征曲线下的面积，比较了解释性GLCM放射组学特征、前列腺影像报告和数据系统（PIRADS）以及前列腺特异抗原密度（PSAD）在正确预测具有临床意义的前列腺癌方面的概率。
我们确定了三组Gleason评分之间放射组学特征的显著差异。在总共22个放射组学特征中，有12个特征与Gleason组相关。我们的模型显示出优异的判别能力（C统计量=0.901，95%置信区间0.859-0.943）。当通过ROC曲线下的面积比较解释性GLCM放射组学特征（T2WI的和方差、T2WI的和熵、T2WI 的差异方差、ADC的熵和ADC的差异方差）、PSAD和PIRADS在正确预测具有临床意义的前列腺癌方面的概率时，在我们的患者中，放射组学特征在正确预测具有临床意义的前列腺癌方面分别比PIRADS和PSAD成功35.0%和34.4%（P<0.001）。T2WI的和熵得分影响最大，其次是T2WI的和方差。
活检前MRI的定量GLCM纹理分析有潜力作为一种非侵入性成像技术，用于预测疑似患有前列腺癌男性的具有临床意义的癌症。
