Cole Elodia B, Pisano Etta D, Kistner Emily O, Muller Keith E, Brown Marylee E, Feig Stephen A, Jong Roberta A, Maidment Andrew D A, Staiger Melinda J, Kuzmiak Cherie M, Freimanis Rita I, Lesko Nadine, Rosen Eric L, Walsh Ruth, Williford Margaret, Braeuning M Patricia
Department of Radiology, Lineberger Comprehensive Cancer Center, University of North Carolina School of Medicine, Chapel Hill, NC 27599-7510, USA.
Radiology. 2003 Jan;226(1):153-60. doi: 10.1148/radiol.2261012024.
To determine effects of lesion type (calcification vs mass) and image processing on radiologist's performance for area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for detection of masses and calcifications with digital mammography in women with mammographically dense breasts.
This study included 201 women who underwent digital mammography at seven U.S. and Canadian medical centers. Three image-processing algorithms were applied to the digital images, which were acquired with Fischer, General Electric, and Lorad digital mammography units. Eighteen readers participated in the reader study (six readers per algorithm). Baseline values for reader performance with screen-film mammograms were obtained through the additional interpretation of 179 screen-film mammograms. A repeated-measures analysis of covariance allowing unequal slopes was used in each of the nine analyses (AUC, sensitivity, and specificity for each of three machines). Bonferroni correction was used.
Although lesion type did not affect the AUC or sensitivity for Fischer digital images, it did affect specificity (P =.0004). For the General Electric digital images, AUC, sensitivity, and specificity were not affected by lesion type. For Lorad digital images, the results strongly suggested that lesion type affected AUC and sensitivity (P <.0001). None of the three image-processing methods tested affected the AUC, sensitivity, or specificity for the Fischer, General Electric, or Lorad digital images.
Findings in this study indicate that radiologist's interpretation accuracy in interpreting digital mammograms depends on lesion type. Interpretation accuracy was not influenced by the image-processing method.
确定病变类型（钙化与肿块）及图像处理对放射科医生在接受者操作特征曲线下面积（AUC）、敏感性和特异性方面的表现的影响，这些指标用于在乳腺钼靶摄影显示乳腺致密的女性中检测肿块和钙化。
本研究纳入了在美国和加拿大七个医疗中心接受数字乳腺钼靶摄影的201名女性。三种图像处理算法应用于数字图像，这些图像是使用菲舍尔、通用电气和洛拉德数字乳腺钼靶设备采集的。18名阅片者参与了阅片研究（每种算法6名阅片者）。通过对179张屏-片乳腺钼靶照片的额外解读获得了阅片者对屏-片乳腺钼靶照片表现的基线值。在九项分析中的每一项（AUC、敏感性和特异性，针对三台机器中的每一台）中均使用了允许不等斜率的重复测量协方差分析。采用邦费罗尼校正。
尽管病变类型未影响菲舍尔数字图像的AUC或敏感性，但确实影响了特异性（P = 0.0004）。对于通用电气数字图像，AUC、敏感性和特异性不受病变类型影响。对于洛拉德数字图像，结果强烈表明病变类型影响AUC和敏感性（P <0.0001）。所测试的三种图像处理方法均未影响菲舍尔、通用电气或洛拉德数字图像的AUC、敏感性或特异性。
本研究结果表明，放射科医生解读数字乳腺钼靶照片的准确性取决于病变类型。解读准确性不受图像处理方法的影响。
