Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK.
Department of General Psychology, University of Padova, Padova, Italy.
Transl Psychiatry. 2020 Apr 20;10(1):107. doi: 10.1038/s41398-020-0798-6.
A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.
精神科和神经科研究的一个关键目标是促进研究结果转化为临床实践，以改善对个体患者的诊断和预后评估。结构神经影像学具有很大的潜力，神经解剖学测量解释了高达 40%的临床结果的差异。在此基础上，已经开发了一些基于影像学的临床工具，以便从他们的结构磁共振成像扫描中对个体患者进行诊断和预后推断。本系统评价描述并比较了现有工具的技术特点，目的是评估它们在现实临床环境中的转化潜力。结果显示，共有 8 种工具。所有这些工具都是专门为神经疾病开发的，因此不适合应用于精神疾病。此外，大多数工具都是在单个数据集上进行训练和验证的，这可能导致推广性差，或者使用少数个体，这可能导致结果过于乐观。此外，所有工具都依赖于两种策略来检测单个个体的大脑异常，一种基于单变量比较，另一种基于多变量机器学习算法。我们讨论了在临床实践中采用这些工具的当前障碍，并提出了一个理想的基于神经影像学的脑疾病临床工具应包含的关键特征清单。
