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首页> 外文期刊>Ultrasound in Medicine and Biology >CAD algorithms for solid breast masses discrimination: evaluation of the accuracy and interobserver variability.
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CAD algorithms for solid breast masses discrimination: evaluation of the accuracy and interobserver variability.

机译：乳腺肿块鉴别的CAD算法：准确性和观察者间差异的评估。

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摘要

For a successful computer-aided diagnosis (CAD) approach, investigating the benefit of the output for radiologist diagnosis is as important as developing the computer algorithm itself. To evaluate the accuracy and the interobserver variability of two newly developed CAD algorithms for breast mass discrimination, eight radiologists with varied experience in breast ultrasonography (US) independently reviewed the lesions according to Breast Imaging Reporting and Data System (BI-RADS)-US. They interpreted the original ultrasound images, provided a final assessment category to indicate the probability of malignancy and then made a further diagnosis using the images processed by the proposed CAD algorithms. The receiver operating characteristic (ROC) curve and Cohen's kappa statistics were employed to evaluate the effect of the CAD algorithms on radiologist diagnoses. By using the proposed CAD approach, the quality of the images was improved and more information was provided to the observers. With the processed images, the areas under the ROC (Az) of each reader (0.86 approximately 0.89) were greater than those with the original ultrasound images (0.81 approximately 0.86) and all the radiologists improved their performance significantly (p < 0.05) except two senior radiologists (p > 0.05). The Az values of the junior radiologists with CAD were comparable to those of the senior radiologists. Cohen's kappa statistics showed that better interobserver agreement was obtained by using the processed images. We conclude that the proposed CAD method is more helpful for the junior radiologists than for the senior ones and it also showed the advantage of decreasing interobserver variability.
机译：对于成功的计算机辅助诊断（CAD）方法，调查输出对放射线诊断的益处与开发计算机算法本身一样重要。为了评估两种新开发的用于乳腺肿块鉴别的CAD算法的准确性和观察者间的差异性，根据乳腺超声成像报告和数据系统（BI-RADS）-US，八名在乳腺超声检查（US）中具有不同经验的放射线医师独立审查了病变。他们解释了原始的超声图像，提供了最终评估类别以指示恶性肿瘤的可能性，然后使用所提出的CAD算法处理的图像进行了进一步的诊断。使用接收器工作特性（ROC）曲线和Cohen的kappa统计量来评估CAD算法对放射科医生诊断的影响。通过使用提出的CAD方法，图像的质量得到了改善，并且向观察者提供了更多的信息。使用经过处理的图像，每个阅读器的ROC（Az）下的面积（0.86约为0.89）要大于原始超声图像的面积（0.81约为0.86），所有放射线医师的表现均得到了显着改善（p <0.05）高级放射科医生（p> 0.05）。具有CAD的初级放射线医师的Az值与高级放射线医师的Az值相当。科恩的kappa统计数据表明，使用处理后的图像可获得更好的观察者之间的一致性。我们得出的结论是，所提出的CAD方法对初级放射线医师比高级放射线医师更有用，并且还显示出减少观察者间差异的优势。

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