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Improved Differential Diagnosis of Breast Masses on Ultrasonographic Images with a Computer-Aided Diagnosis Scheme for Determining Histological Classifications

机译:利用计算机辅助诊断方案改善乳腺肿块对乳腺素的差异诊断,用于确定组织学分类

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摘要

Objectives: A computer-aided diagnosis (CAD) scheme for determining histological classifications of breast masses is expected to be useful for clinicians in making a differential diagnosis. The purpose of this study was to evaluate the usefulness of using the CAD scheme on ultrasonographic images. Methods: The database consisted of 390 breast ultrasonographic images with masses. Three experienced clinicians independently provided subjective ratings on the likelihood of malignancy for each of the 390 masses. Fifty benign masses (25 cysts and 25 fibroadenomas) and 50 malignant masses (25 noninvasive ductal carcinomas and 25 invasive ductal carcinomas) were selected as unknown cases for an observer study based on a stratified randomization method with the ratings. The likelihood of the histological classification in each unknown case was evaluated by the CAD scheme with image features that clinicians commonly use for describing masses. In the observer study, seven observers provided their confidence levels regarding the malignancy of the unknown case before and after viewing the likelihood of the histological classification. The usefulness of the CAD scheme was evaluated with a multireader multicase receiver operating characteristic (ROC) analysis. Results: The areas under the ROC curves (AUCs) for all observers were improved by use of the CAD scheme. The average AUC increased from 0.716 without to 0.864 with the CAD scheme (P =.006). Conclusion: The presentation of the likelihood of the histological classification evaluated by the CAD scheme improved the clinicians' performance and therefore would be useful in making a differential diagnosis of masses on ultrasonographic images.
机译:目的:用于确定乳腺菌群体组织学分类的计算机辅助诊断(CAD)方案对于诊断诊断,有用。本研究的目的是评估在超声图像上使用CAD方案的有用性。方法:数据库由390个乳房超声图像组成,具有群众。三位经验丰富的临床医生独立提供了对390群众中的每一个的恶性肿瘤的可能性的主观评级。五十份良性质量(25瓣和25个纤维肉瘤组)和50个恶性肿瘤(25个非侵袭性导管癌和25个侵入性导管癌)被选为基于具有评级的分层随机化方法的观察者研究的未知病例。通过CAD方案评估每个未知案例中的组织学分类的可能性,其具有临床医生通常用于描述群众的图像特征。在观察者研究中,七个观察者提供了他们在观看组织学分类的可能性之前和之后的未知案例的恶性水平。通过多舞蹈多级接收器操作特征(ROC)分析评估CAD方案的有用性。结果:通过使用CAD方案,改善了所有观察者的ROC曲线(AUC)下的区域。 CAD方案的平均AUC从0.716增加到0.864(p = .006)。结论:CAD方案评估的组织学分类的可能性提高了临床医生的表现,因此可用于在超声图像上进行差异诊断群体。

著录项

  • 来源
    《Academic radiology》 |2013年第4期|共7页
  • 作者单位

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

    Department of Breast Surgery Mie University School of Medicine 2-174 Edobashi Tsu Mie 514-8507;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 放射医学;
  • 关键词

    Breast mass; Computer-aided diagnosis; Ultrasonographic image;

    机译:乳房肿块;计算机辅助诊断;超声图像;
  • 入库时间 2022-08-20 00:24:28

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