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首页> 外文期刊>Medical Physics >Repeatability in computer-aided diagnosis: application to breast cancer diagnosis on sonography.
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Repeatability in computer-aided diagnosis: application to breast cancer diagnosis on sonography.

机译：计算机辅助诊断中的可重复性：在超声检查中在乳腺癌诊断中的应用。

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PURPOSE: The aim of this study was to investigate the concept of repeatability in a case-based performance evaluation of two classifiers commonly used in computer-aided diagnosis in the task of distinguishing benign from malignant lesions. METHODS: The authors performed .632+ bootstrap analyses using a data set of 1251 sonographic lesions of which 212 were malignant. Several analyses were performed investigating the impact of sample size and number of bootstrap iterations. The classifiers investigated were a Bayesian neural net (BNN) with five hidden units and linear discriminant analysis (LDA). Both used the same four input lesion features. While the authors did evaluate classifier performance using receiver operating characteristic (ROC) analysis, the main focus was to investigate case-based performance based on the classifier output for individual cases, i.e., the classifier outputs for each test case measured over the bootstrap iterations. In this case-based analysis, the authors examined the classifier output variability and linked it to the concept of repeatability. Repeatability was assessed on the level of individual cases, overall for all cases in the data set, and regarding its dependence on the case-based classifier output. The impact of repeatability was studied when aiming to operate at a constant sensitivity or specificity and when aiming to operate at a constant threshold value for the classifier output. RESULTS: The BNN slightly outperformed the LDA with an area under the ROC curve of 0.88 versus 0.85 (p < 0.05). In the repeatability analysis on an individual case basis, it was evident that different cases posed different degrees of difficulty to each classifier as measured by the by-case output variability. When considering the entire data set, however, the overall repeatability of the BNN classifier was lower than for the LDA classifier, i.e., the by-case variability for the BNN was higher. The dependence of the by-case variability on the average by-case classifier output was markedly different for the classifiers. The BNN achieved the lowest variability (best repeatability) when operating at high sensitivity (> 90%) and low specificity (< 66%), while the LDA achieved this at moderate sensitivity (approximately 74%) and specificity (approximately 84%). When operating at constant 90% sensitivity or constant 90% specificity, the width of the 95% confidence intervals for the corresponding classifier output was considerable for both classifiers and increased for smaller sample sizes. When operating at a constant threshold value for the classifier output, the width of the 95% confidence intervals for the corresponding sensitivity and specificity ranged from 9 percentage points (pp) to 30 pp. CONCLUSIONS: The repeatability of the classifier output can have a substantial effect on the obtained sensitivity and specificity. Knowledge of classifier repeatability, in addition to overall performance level, is important for successful translation and implementation of computer-aided diagnosis in clinical decision making.
机译：目的：本研究的目的是研究在计算机辅助诊断中常用的两个分类器基于病例的性能评估中的可重复性概念，以区分良性和恶性病变。方法：作者使用1251个超声检查病变（其中212个为恶性）的数据集进行了.632+引导分析。进行了一些分析，以调查样本大小和引导程序迭代次数的影响。研究的分类器是具有五个隐藏单元的贝叶斯神经网络（BNN）和线性判别分析（LDA）。两者都使用相同的四个输入病变特征。虽然作者确实使用接收器工作特征（ROC）分析评估了分类器的性能，但主要重点是根据个别案例的分类器输出来研究基于案例的性能，即在自举迭代中测得的每个测试案例的分类器输出。在基于案例的分析中，作者检查了分类器输出的可变性，并将其与可重复性概念相关联。对可重复性进行了评估，包括单个案例的级别，数据集中所有案例的总体级别以及其对基于案例的分类器输出的依赖性。当针对分类器输出以恒定的灵敏度或特异性进行操作以及以恒定的阈值进行操作时，研究了重复性的影响。结果：BNN略胜于LDA，ROC曲线下的面积为0.88对0.85（p <0.05）。在基于个案的可重复性分析中，很明显，按个案输出的可变性来衡量，不同的个案给每个分类器带来不同的难度。但是，当考虑整个数据集时，BNN分类器的总体可重复性低于LDA分类器，即BNN的个案可变性更高。对于分类器，个案变化对平均个案分类器输出的依赖性显着不同。当在高灵敏度（> 90％）和低特异性（<66％）下操作时，BNN的变异性最低（最佳重复性），而LDA在中等灵敏度（约74％）和特异性（约84％）时达到了最低变异性。当在恒定的90％灵敏度或恒定的90％特异性下运行时，两个分类器对应的分类器输出的95％置信区间的宽度就相当大，而对于较小的样本量，则会增加。当以恒定的阈值进行分类器输出操作时，对应灵敏度和特异性的95％置信区间的宽度范围为9个百分点（pp）至30 pp。结论：分类器输出的可重复性可以有很大的提高对获得的敏感性和特异性的影响。除总体性能水平外，分类器可重复性的知识对于临床决策中计算机辅助诊断的成功翻译和实施也很重要。

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