Department of Radiology, The University of Chicago, Chicago, IL 60637, USA.
Phys Med Biol. 2011 Sep 21;56(18):5995-6008. doi: 10.1088/0031-9155/56/18/014. Epub 2011 Aug 22.
The purpose of this study is to investigate whether computerized analysis using three-class Bayesian artificial neural network (BANN) feature selection and classification can characterize tumor grades (grade 1, grade 2 and grade 3) of breast lesions for prognostic classification on DCE-MRI. A database of 26 IDC grade 1 lesions, 86 IDC grade 2 lesions and 58 IDC grade 3 lesions was collected. The computer automatically segmented the lesions, and kinetic and morphological lesion features were automatically extracted. The discrimination tasks-grade 1 versus grade 3, grade 2 versus grade 3, and grade 1 versus grade 2 lesions-were investigated. Step-wise feature selection was conducted by three-class BANNs. Classification was performed with three-class BANNs using leave-one-lesion-out cross-validation to yield computer-estimated probabilities of being grade 3 lesion, grade 2 lesion and grade 1 lesion. Two-class ROC analysis was used to evaluate the performances. We achieved AUC values of 0.80 ± 0.05, 0.78 ± 0.05 and 0.62 ± 0.05 for grade 1 versus grade 3, grade 1 versus grade 2, and grade 2 versus grade 3, respectively. This study shows the potential for (1) applying three-class BANN feature selection and classification to CADx and (2) expanding the role of DCE-MRI CADx from diagnostic to prognostic classification in distinguishing tumor grades.
本研究旨在探讨基于三分类贝叶斯人工神经网络（BANN）特征选择和分类的计算机分析是否可用于对 DCE-MRI 中的乳腺病变进行肿瘤分级（1 级、2 级和 3 级）的预后分类。收集了 26 个 IDC 1 级病变、86 个 IDC 2 级病变和 58 个 IDC 3 级病变的数据库。计算机自动对病变进行分割，自动提取动力学和形态学病变特征。研究了鉴别任务——1 级与 3 级、2 级与 3 级和 1 级与 2 级病变。通过三分类 BANN 进行逐步特征选择。使用三分类 BANN 进行分类，采用留一病变交叉验证，得出计算机估计的病变为 3 级、2 级和 1 级的概率。采用二分类 ROC 分析评估性能。对于 1 级与 3 级、1 级与 2 级和 2 级与 3 级的病变，AUC 值分别为 0.80±0.05、0.78±0.05 和 0.62±0.05。本研究表明，（1）应用三分类 BANN 特征选择和分类用于 CADx 具有潜力，（2）DCE-MRI CADx 从诊断扩展到肿瘤分级的预后分类的作用。
