Computerized three-class classification of MRI-based prognostic markers for breast cancer

被引:34
作者
Bhooshan, Neha [1 ]
Giger, Maryellen [1 ]
Edwards, Darrin [1 ]
Yuan, Yading [1 ]
Jansen, Sanaz [1 ]
Li, Hui [1 ]
Lan, Li [1 ]
Sattar, Husain [2 ]
Newstead, Gillian [1 ]
机构
[1] Univ Chicago, Dept Radiol, Chicago, IL 60637 USA
[2] Univ Chicago, Dept Pathol, Chicago, IL 60637 USA
关键词
OBSERVER VARIABILITY; AIDED-DIAGNOSIS; LESIONS; ANGIOGENESIS; SURVIVAL; GRADE; FEATURES; IMAGES; CAD;
D O I
10.1088/0031-9155/56/18/014
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页码:5995 / 6008
页数:14
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