Ultrasound Department, The Second Affiliated Hospital of Harbin Medical University, 246 Xuefu Road, Harbin, Heilongjiang 150086, People's Republic of China.
J Digit Imaging. 2010 Oct;23(5):581-91. doi: 10.1007/s10278-009-9245-1. Epub 2009 Nov 10.
The objective of this study is to retrospectively investigate whether using the newly developed algorithms would improve radiologists' accuracy for discriminating malignant masses from benign ones on ultrasonographic (US) images. Five radiologists blinded to the histological results and clinical history independently interpreted 226 cases according to the sonographic lexicon of the fourth edition of the Breast Imaging Reporting and Data System and assigned a final assessment category to indicate the probability of malignancy. For each case, each radiologist provided three diagnoses: first with the original images, subsequently with the assistant of the resulting images processed by the proposed CAD algorithms which are called as processed images, and another using the processed images only. Observers' malignancy rating data were analyzed with the receiver operating characteristic (ROC) curve. For reading only with the processed images, areas under the ROC curve (A(z)) of each reader (0.863, 0.867, 0.859, 0.868, 0.878) were better than that with the original images (0.772, 0.807, 0.796, 0.828, 0.846), difference of the average A(z) between the twice reading was significant (p < 0.001). Compared with the results single used processed images, A(z) of utilizing the combined images were increased (0.866, 0.885, 0.872, 0.894, 0.903), but the difference is not statistically significant (p = 0.081). The proposed CAD method has potential to be a good aid to radiologists in distinguishing malignant breast solid masses from benign ones.
本研究旨在回顾性探讨新开发的算法是否会提高放射科医生在超声（US）图像上区分良恶性肿块的准确性。五位放射科医生在不知道组织学结果和临床病史的情况下，根据第四版乳腺影像报告和数据系统的超声词汇，独立地对 226 例进行了解释，并对每个病例分配了最终评估类别，以指示恶性的可能性。对于每个病例，每位放射科医生提供了三种诊断：首先使用原始图像，其次是使用新开发的 CAD 算法处理后的结果图像（称为处理后的图像），然后是仅使用处理后的图像。使用接收器工作特征（ROC）曲线分析观察者的恶性评分数据。仅阅读处理后的图像时，每位读者的 ROC 曲线下面积（A（z））（0.863、0.867、0.859、0.868、0.878）优于原始图像（0.772、0.807、0.796、0.828、0.846），两次阅读的平均 A（z）差异具有统计学意义（p<0.001）。与单独使用处理后的图像相比，使用组合图像的 A（z）增加（0.866、0.885、0.872、0.894、0.903），但差异无统计学意义（p=0.081）。该 CAD 方法具有成为放射科医生区分恶性乳腺实性肿块和良性肿块的良好辅助工具的潜力。
