Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging.
Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging.
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DOI：
10.3389/fpsyt.2013.00095
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发表时间：
2013
影响因子：
4.7
通讯作者：
Palaniyappan L
中科院分区：
文献类型：
--
作者：
Iwabuchi SJ;Liddle PF;Palaniyappan L
机器学习方法在神经影像学文献中越来越普遍，作为临床人群研究的潜在诊断和预后工具。然而，很少有研究提供临床信息的措施，以帮助决策和资源分配。基于神经成像的多变量分类器的头对头比较是促进这些工具向临床实践转化的重要第一步。我们系统地评估了分类器的性能，使用背靠背结构MRI在两个场强（3-和7-T）区分精神分裂症患者（n = 19）从健康对照组（n = 20）。灰质（GM）和白色物质图像被用作输入到支持向量机中，以分类患者和对照受试者。7个特斯拉分类器优于3-T分类器，7-T GM分类器的准确率高达77%，而3-T GM分类器的准确率为66.6%。此外，诊断优势比（不受样本特征变化影响的测量）和预测所需的数量（基于测试结果的贝叶斯确定性的测量）表明7-T分类器的上级性能，由此对于每个正确的诊断，需要使用7-TGM分类器检查的患者数量比使用不同分类器时需要检查的患者数量少一个。我们使用一个假设的例子来强调这些发现如何对临床决策产生重大影响。我们鼓励在未来的研究中利用机器学习方法报告这里提出的措施。这不仅将促进寻找最佳诊断工具，而且有助于将神经影像学转化为临床应用。
Machine-learning approaches are becoming commonplace in the neuroimaging literature as potential diagnostic and prognostic tools for the study of clinical populations. However, very few studies provide clinically informative measures to aid in decision-making and resource allocation. Head-to-head comparison of neuroimaging-based multivariate classifiers is an essential first step to promote translation of these tools to clinical practice. We systematically evaluated the classifier performance using back-to-back structural MRI in two field strengths (3- and 7-T) to discriminate patients with schizophrenia (n = 19) from healthy controls (n = 20). Gray matter (GM) and white matter images were used as inputs into a support vector machine to classify patients and control subjects. Seven Tesla classifiers outperformed the 3-T classifiers with accuracy reaching as high as 77% for the 7-T GM classifier compared to 66.6% for the 3-T GM classifier. Furthermore, diagnostic odds ratio (a measure that is not affected by variations in sample characteristics) and number needed to predict (a measure based on Bayesian certainty of a test result) indicated superior performance of the 7-T classifiers, whereby for each correct diagnosis made, the number of patients that need to be examined using the 7-T GM classifier was one less than the number that need to be examined if a different classifier was used. Using a hypothetical example, we highlight how these findings could have significant implications for clinical decision-making. We encourage the reporting of measures proposed here in future studies utilizing machine-learning approaches. This will not only promote the search for an optimum diagnostic tool but also aid in the translation of neuroimaging to clinical use.