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首页> 外文期刊>Academic radiology >Evaluation of the effect ofcomputer-aided classification ofbenign and malignant lesions onreader performance in automated three-dimensional breast ultrasound
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Evaluation of the effect ofcomputer-aided classification ofbenign and malignant lesions onreader performance in automated three-dimensional breast ultrasound

机译：对计算机辅助分类的影响和恶性病变对自动三维乳房超声造成性能的评价

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摘要

Rationale and Objectives: To investigate the effect of a newly developed computer-aided diagnosis (CAD) system on reader interpretation of breast lesions in automated three-dimensional (3D) breast ultrasound. Materials and Methods: A CAD system was developed to differentiate malignant lesions from benign lesions including automated lesion segmentation in three dimensions; extraction of lesion features such as spiculation, margin contrast, and posterior acoustic behavior; and a classification stage. Eighty-eight patients with breast lesions were included for an observer study: 47 lesions were malignant and 41 were benign. Eleven readers (seven radiologists and four residents) read the cases with and without CAD. We compared the performance of readers with and without CAD using receiver operating characteristic (ROC) analysis. Results: The CAD system had an area under the ROC curve (AUC) of 0.92 for discriminating benign and malignant lesions, whereas the unaided reader AUC ranged from 0.77 to 0.92. Mean performance of inexperienced readers improved when CAD was used (AUC=0.85 versus 0.90; P=.007), whereas mean performance of experienced readers did not change with CAD (AUC=0.89). Conclusions: By using the CAD system for classification of lesions in automated 3D breast ultrasound, which on its own performed as good as the best readers, the performance of inexperienced readers improved while that of experienced readers remained unaffected.
机译：理由和目标：探讨新开发的计算机辅助诊断（CAD）系统对自动三维（3D）乳房超声中乳腺病变读者解释的影响。材料和方法：开发了一种CAD系统，以区分来自良性病变的恶性病变，包括三维的自动化病变分段;提取病变特征，如刺激，边缘对比度和后声学行为;和分类阶段。八十八名乳房病变患者被包括观察者研究：47个病变是恶性的，41例是良性的。十一读者（七位放射科医生和四名居民）阅读患者，没有CAD。我们使用接收器操作特征（ROC）分析比较了读者的性能，无需CAD。结果：CAD系统的ROC曲线（AUC）为0.92的区域，用于辨别良性和恶性病变，而无可求读者AUC的范围从0.77到0.92。使用CAD时，缺乏经验的读者的平均性能（AUC = 0.85与0.90; p = .007），而有经验读者的平均性能没有CAD（AUC = 0.89）没有改变。结论：通过使用CAD系统进行自动化3D乳房超声中的病变分类，这对自己作为最佳读者的表现，缺乏经验的读者的性能得到改善，而经验丰富的读者仍未受到影响。

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  • 来源
    《Academic radiology》 |2013年第11期|共8页
  • 作者

    TanT.; PlatelB.; TwellmannT.; vanSchieG.; MusR.; GrivegnéeA.; MannR.M.; KarssemeijerN.;

  • 作者单位

    Department of Radiology Radboud University Nijmegen Medical Centre Geert Grooteplein 10 6525 GA;

    Fraunhofer MEVIS Bremen Germany;

    MeVis Medical Solutions AG Bremen Germany;

    Department of Radiology Radboud University Nijmegen Medical Centre Geert Grooteplein 10 6525 GA;

    Department of Radiology Radboud University Nijmegen Medical Centre Geert Grooteplein 10 6525 GA;

    Jules Bordet Institute Cancer Prevention and Screening Clinic Brussels Belgium;

    Department of Radiology Radboud University Nijmegen Medical Centre Geert Grooteplein 10 6525 GA;

    Department of Radiology Radboud University Nijmegen Medical Centre Geert Grooteplein 10 6525 GA;

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

    Breast cancer; Computer-assisted diagnosis; Image interpretation; Ultrasound;

    机译：乳腺癌;计算机辅助诊断;图像解释;超声波;

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