Hadjiiski Lubomir, Chan Heang-Ping, Sahiner Berkman, Helvie Mark A, Roubidoux Marilyn A, Blane Caroline, Paramagul Chintana, Petrick Nicholas, Bailey Janet, Klein Katherine, Foster Michelle, Patterson Stephanie, Adler Dorit, Nees Alexis, Shen Joseph
Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109-0904, USA.
Radiology. 2004 Oct;233(1):255-65. doi: 10.1148/radiol.2331030432. Epub 2004 Aug 18.
To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' characterization of masses on serial mammograms.
Two hundred fifty-three temporal image pairs (138 malignant and 115 benign) obtained from 96 patients who had masses on serial mammograms were evaluated. The temporal pairs were formed by matching masses of the same view from two different examinations. Eight radiologists and two breast imaging fellows assessed the temporal pairs with and without computer aid. The classification of accuracy was quantified by using the area under receiver operating characteristic curve (A(z)). The statistical significance of the difference in A(z) between the different reading conditions was estimated with the Dorfman-Berbaum-Metz method for analysis of multireader multicase data and with the Student paired t test for analysis of observer-specific paired data.
The average A(z) for radiologists' estimates of the likelihood of malignancy was 0.79 without CAD and improved to 0.84 with CAD. The improvement was statistically significant (P =.005). The corresponding average partial area index was 0.25 without CAD and improved to 0.37 with CAD. The improvement was also statistically significant (P =.005). On the basis of Breast Imaging Reporting and Data System assessments, it was estimated that with CAD, each radiologist, on average, reduced 0.7% (0.8 of 115) of unnecessary biopsies and correctly recommended 5.7% (7.8 of 138) of additional biopsies.
CAD based on analysis of interval changes can significantly increase radiologists' accuracy in classification of masses and thereby may be useful in improving correct biopsy recommendations.
评估计算机辅助诊断（CAD）对放射科医生在系列乳腺造影片上对肿块特征的判断效果。
对96例有系列乳腺造影片上有肿块的患者获取的253对时间序列图像（138例恶性和115例良性）进行评估。时间序列对是通过匹配来自两次不同检查的同一视角的肿块形成的。八位放射科医生和两位乳腺影像专科住院医师在有和没有计算机辅助的情况下评估时间序列对。准确性分类通过使用受试者操作特征曲线下面积（A(z)）进行量化。不同阅读条件下A(z)差异的统计学显著性通过用于多读者多病例数据分析的多尔夫曼 - 伯鲍姆 - 梅茨方法以及用于观察者特定配对数据分析的学生配对t检验进行估计。
放射科医生对恶性可能性估计的平均A(z)在无CAD时为0.79，有CAD时提高到0.84。改善具有统计学显著性（P = 0.005）。相应的平均部分面积指数在无CAD时为0.25，有CAD时提高到0.37。改善也具有统计学显著性（P = 0.005）。根据乳腺影像报告和数据系统评估，估计有CAD时，每位放射科医生平均减少0.7%（115例中的0.8例）不必要的活检，并正确推荐5.7%（138例中的7.8例）额外的活检。
基于间期变化分析的CAD可显著提高放射科医生对肿块分类的准确性，从而可能有助于改善正确的活检建议。
