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Performance of Triple-Modality CADx on Breast Cancer Diagnostic Classification

机译：三联体CADx在乳腺癌诊断分类中的性能

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摘要

The purpose of this study is to evaluate the potential of computer-aided diagnosis (CADx) methods utilizing three breast imaging modalities: full-field digital mammography (FFDM), sonography, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for breast lesion classification. Three separate databases for each modality were retrospectively organized: FFDM (255 malignant lesions, 177 benign lesions), ultrasound (968 malignant lesions, 158 benign lesions), and DCE-MRI (347 malignant lesions, 129 benign lesions). From these single-modality databases, three dual-modality databases were constructed as well as a triple-modality database (31 malignant lesions, 17 benign lesions). Our computerized analysis methods consisted of several steps: (1) automatic lesion segmentation; (2) automatic feature extraction; (3) automatic feature selection; (4) merging of selected features into a probability of malignancy. Stepwise linear discriminant analysis using a Wilks lambda cost function in a leave-one-lesion-out method was used for feature selection. The selected features were merged using a Bayesian artificial neural network (BANN) with a leave-one-lesion-out method. The classification performance was assessed using receiver-operating characteristics (ROC) analysis. Results showed that the computerized analysis of breast lesions using image information from all three modalities yielded an AUC of 0.95±0.03. The observed trend of increasing performance as information from more modalities is included in the classifier indicates that the use of all three modalities can potentially improve the diagnostic classification of CADx.
机译：这项研究的目的是评估利用三种乳房成像模式的计算机辅助诊断（CADx）方法的潜力：全场数字乳房X线照片（FFDM），超声检查和动态对比增强磁共振成像（DCE-MRI）乳房病变分类。每种形式的三个独立数据库分别进行了回顾：FFDM（255个恶性病变，177个良性病变），超声（968个恶性病变，158个良性病变）和DCE-MRI（347个恶性病变，129个良性病变）。从这些单模态数据库中，构建了三个双模态数据库以及一个三模态数据库（31个恶性病变，17个良性病变）。我们的计算机分析方法包括几个步骤：（1）自动病变分割； （2）自动特征提取； （3）自动功能选择； （4）将选定的特征合并为恶性概率。使用Wilks lambda成本函数和“留下一个病变”方法的逐步线性判别分析用于特征选择。所选特征使用贝叶斯人工神经网络（BANN）与“留下一个病变”方法合并。使用接收者操作特征（ROC）分析评估分类性能。结果表明，使用来自所有三种方式的图像信息对乳房病变进行计算机分析得出的AUC为0.95±0.03。随着来自更多模态的信息包含在分类器中而观察到的性能提升趋势表明，使用这三种模态可以潜在地改善CADx的诊断分类。

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  • 来源
    《Digital mammography》|2010年|p.9-14|共6页
  • 会议地点 Girona(ES);Girona(ES)
  • 作者

    Neha Bhooshan; Maryellen L. Giger; Karen Drukker; Yading Yuan; Hui Li; Stephanie McCann; Gillian Newstead; Charlene Sennett;

  • 作者单位

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

    Department of Radiology, The University of Chicago 5841 South Maryland Avenue, Chicago, Illinois 60637;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;
  • 关键词

    computer-aided diagnosis; mammography; ultrasound; DCE-MRI; multi-modality;

    机译：计算机辅助诊断；乳腺摄影超声波DCE-MRI;多模态;

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