Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: An ROC study
Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: An ROC study
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DOI：
10.1148/radiology.212.3.r99au47817
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发表时间：
1999-09-01
期刊：
影响因子：
19.7
通讯作者：
Sanjay-Gopal, S
中科院分区：
文献类型：
--
作者：
Chan, HP;Sahiner, B;Sanjay-Gopal, S
目的：探讨计算机辅助诊断（CAD）对放射科医师对乳腺x线检查中良恶性肿块分类的影响。材料和方法：作者先前开发了一个自动计算机程序来估计肿块的相对恶性等级。在本研究中，作者采用受试者工作特征（receiver operating characteristic， ROC）方法进行了观察者表现实验，以评估计算机估计对放射科医生信心评级的影响。六名放射科医生评估了活检证实的肿块，有无CAD。进行了两个实验，一个是单视图，另一个是双视图。用ROC曲线下面积A(z)来量化分类精度。结果：对于238张图像的读取，计算机分类器的A(z)值为0.92。未加CAD时放射科医师的A(z)值为0.79 ~ 0.92，加CAD后为0.87 ~ 0.96。对于76个配对视图子集的阅读，放射科医生的a (z)值在没有CAD的情况下从0.88到0.95不等，在CAD的情况下提高到0.93-0.97。两组图像的阅读改善具有统计学意义（P = 0.022, P = 0.022）。007年,分别)。从改进的ROC曲线中预测出改进的阳性预测值作为假阴性分数的函数。结论：CAD可能有助于放射科医师对肿块进行分类，从而可能有助于减少不必要的活检。
PURPOSE: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' classification of malignant and benign masses seen on mammogram.MATERIALS AND METHODS: The authors previously developed an automated computer program for estimation of the relative malignancy rating of masses. In the present Study, the authors conducted observer performance experiments with receiver operating characteristic (ROC) methodology to evaluate the effects of computer estimates on radiologists' confidence ratings. Six radiologists assessed biopsy-proved masses with and without CAD. Two experiments, one with a single view and the other with two views, were conducted. The classification accuracy was quantified by using the area under the ROC curve, A(z).RESULTS: For the reading of 238 images, the A(z) value for the computer classifier was 0.92. The radiologists' A(z) values ranged from 0.79 to 0.92 without CAD and improved to 0.87-0.96 with CAD. For the reading of a subset of 76 paired views, the radiologists' A(z) values ranged from 0.88 to 0.95 without CAD and improved to 0.93-0.97 with CAD. Improvements in the reading of the two sets of images were statistically significant (P = .022 and .007, respectively). An improved positive predictive yalue as a function of the false-negative fraction was predicted from the improved ROC curves.CONCLUSION: CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies.