A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging
A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging
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DOI：
10.1016/j.compmedimag.2021.101911
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发表时间：
2021-04-10
影响因子：
5.7
通讯作者：
El-Baz, A.
中科院分区：
文献类型：
--
作者：
Hammouda, K.;Khalifa, F.;El-Baz, A.
膀胱癌（BC）的适当治疗广泛基于准确和早期BC分期。在本文中，多参数计算机辅助诊断（MP-CAD）系统的开发，以区分BC分期，特别是T1和T2阶段，使用T2加权（T2 W）磁共振成像（MRI）和扩散加权（DW）MRI。我们的框架从膀胱壁（BW）的分割和整个BC体积（Vt）及其在壁内的范围（Vw）的定位开始。我们的分割框架基于全连接卷积神经网络（CNN），并利用自适应形状模型，然后估计一组功能，纹理和形态特征。该函数特征是由表观扩散系数的累积分布函数（CDF）导出的。纹理特征是从T2 W-MRI中估计的放射组学特征，而形态特征用于描述肿瘤的几何形状。由于壁和膀胱腔细胞之间的显著纹理差异，Vt被包裹成一组嵌套等距表面（即，等值面）。最后，对各个等值面进行特征估计，然后对其进行增强，并用于训练和测试基于神经网络的机器学习（ML）分类器。该系统已被评估使用42个数据集，并采用留一个主题的方法。总的准确性、敏感性、特异性和受试者工作特征（ROC）曲线下面积（AUC）分别为95.24%、95.24%、95.24%和0.9864。通过比较个体MRI模态的诊断准确性，突出了融合多参数等特征的优势，这由ROC分析证实。此外，我们的管道的准确性与其他统计ML分类器（即，随机森林（RF）和支持向量机（SVM））。我们的CAD系统也与其他技术（例如，端到端卷积神经网络（即，ResNet50）。
Appropriate treatment of bladder cancer (BC) is widely based on accurate and early BC staging. In this paper, a multiparametric computer-aided diagnostic (MP-CAD) system is developed to differentiate between BC staging, especially T1 and T2 stages, using T2-weighted (T2W) magnetic resonance imaging (MRI) and diffusionweighted (DW) MRI. Our framework starts with the segmentation of the bladder wall (BW) and localization of the whole BC volume (Vt) and its extent inside the wall (Vw). Our segmentation framework is based on a fully connected convolution neural network (CNN) and utilized an adaptive shape model followed by estimating a set of functional, texture, and morphological features. The functional features are derived from the cumulative distribution function (CDF) of the apparent diffusion coefficient. Texture features are radiomic features estimated from T2W-MRI, and morphological features are used to describe the tumors' geometric. Due to the significant texture difference between the wall and bladder lumen cells, Vt is parcelled into a set of nested equidistance surfaces (i.e., iso-surfaces). Finally, features are estimated for individual iso-surfaces, which are then augmented and used to train and test machine learning (ML) classifier based on neural networks. The system has been evaluated using 42 data sets, and a leave-one-subject-out approach is employed. The overall accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) are 95.24%, 95.24%, 95.24%, and 0.9864, respectively. The advantage of fusion multiparametric iso-features is highlighted by comparing the diagnostic accuracy of individual MRI modality, which is confirmed by the ROC analysis. Moreover, the accuracy of our pipeline is compared against other statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)). Our CAD system is also compared with other techniques (e.g., end-toend convolution neural networks (i.e., ResNet50).