Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, China.
Acad Radiol. 2013 Aug;20(8):930-8. doi: 10.1016/j.acra.2013.03.011.
The purpose of this study was to determine textural features that show a significant difference between carcinomatous tissue and the bladder wall on magnetic resonance imaging (MRI) and explore the feasibility of using them to differentiate malignancy from the normal bladder wall as an initial step for establishing MRI as a screening modality for the noninvasive diagnosis of bladder cancer.
Regions of interest (ROIs) were manually placed on foci of bladder cancer and uninvolved bladder wall in 22 patients and on the normal bladder wall of 23 volunteers to calculate 40 known textural features. Statistical analysis was applied to determine the difference in these features in bladder cancer versus uninvolved bladder wall versus normal bladder wall of volunteers. The significantly different features were then analyzed using a support vector machine (SVM) classifier to determine their accuracy in differentiating malignancy from the bladder wall.
Thirty-three of 40 features show significant differences between bladder cancer and the bladder wall. Nine of 40 features were significantly different in uninvolved bladder wall of patients versus normal bladder wall of volunteers. Further study indicates that seven of these 33 features were significantly different between uninvolved bladder wall of patients with early cancer and that of volunteers, whereas 15 of 33 features were different between that of patients with advanced cancer and normal wall. With the testing dataset consisting of ROIs acquired from patients, the classification accuracy using 33 textural features fed into the SVM classifier was 86.97%.
The initial experience demonstrates that texture features are sensitive to reveal the differences between bladder cancer and the bladder wall on MRI. The different features can be used to develop a computer-aided system for the evaluation of the entire bladder wall.
本研究旨在确定磁共振成像（MRI）上癌组织与膀胱壁之间存在显著差异的纹理特征，并探讨其是否可用于区分良恶性肿瘤与正常膀胱壁，为将 MRI 作为膀胱癌无创诊断的筛查手段奠定基础。
对 22 例膀胱癌患者的肿瘤焦点及无肿瘤累及的膀胱壁和 23 例志愿者的正常膀胱壁进行感兴趣区（ROI）手动勾画，计算 40 种已知纹理特征。应用统计学分析方法确定这些特征在膀胱癌与无肿瘤累及的膀胱壁、志愿者正常膀胱壁之间的差异。然后，使用支持向量机（SVM）分类器对具有显著差异的特征进行分析，以确定其在区分肿瘤与膀胱壁良恶性方面的准确性。
40 种特征中有 33 种在膀胱癌与膀胱壁之间存在显著差异。与志愿者正常膀胱壁相比，患者无肿瘤累及的膀胱壁有 40 种特征中的 9 种存在显著差异。进一步研究表明，这 33 种特征中有 7 种在早期癌症患者无肿瘤累及的膀胱壁与志愿者之间存在显著差异，而 15 种在晚期癌症患者无肿瘤累及的膀胱壁与正常壁之间存在差异。使用来自患者的 ROI 数据集进行测试，将 33 种纹理特征输入 SVM 分类器的分类准确率为 86.97%。
初步经验表明，纹理特征能够敏感地揭示 MRI 上膀胱癌与膀胱壁之间的差异。这些不同的特征可用于开发用于评估整个膀胱壁的计算机辅助系统。
