Computerized three-class classification of MRI-based prognostic markers for breast cancer.
Computerized three-class classification of MRI-based prognostic markers for breast cancer.
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基于MRI的乳腺癌预后标记的计算机化三类分类。
DOI：
10.1088/0031-9155/56/18/014
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发表时间：
2011-09-21
影响因子：
3.5
通讯作者：
Newstead G
中科院分区：
文献类型：
--
作者：
Bhooshan N;Giger M;Edwards D;Yuan Y;Jansen S;Li H;Lan L;Sattar H;Newstead G
本研究的目的是探讨基于三类贝叶斯人工神经网络(BANN)特征选择和分类的计算机分析能否在DCE-MRI上对乳腺病变的肿瘤分级(1级、2级和3级)进行预后分类。收集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。计算机自动分割病变，并自动提取病变的动态和形态特征。对1级与3级、2级与3级、1级与2级皮损的辨别任务进行了调查。分步特征选择由三类BANN进行。用三类BANN进行分类，使用留一病变不交叉验证，以产生计算机估计的3级病变、2级病变和1级病变的概率。使用两类ROC分析对性能进行评价。1级与3级、1级与2级、2级与3级的AUC值分别为0.80±0.05、0.78±0.05和0.62±0.05。这项研究表明，(1)将三类Bann特征选择和分类应用于CADx，以及(2)将DCE-MRI CADx的作用从诊断分类扩展到预后分类，以区分肿瘤的分级。
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.
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影响因子：
120.7
作者：
Bluemke, DA;Gatsonis, CA;Schnall, MD
通讯作者：
Schnall, MD
影响因子：
19.7
作者：
Bhooshan, Neha;Giger, Maryellen L.;Newstead, Gillian M.
通讯作者：
Newstead, Gillian M.
影响因子：
4.4
作者：
Johansen, Roar;Jensen, Line R.;Gribbestad, Ingrid S.
通讯作者：
Gribbestad, Ingrid S.
影响因子：
4.8
作者：
DeMartini, Wendy;Lehman, Constance;Partridge, Savannah
通讯作者：
Partridge, Savannah
影响因子：
3.8
作者：
Chen, WJ;Giger, ML;Bick, U
通讯作者：
Bick, U