Department of Psychiatry, Amsterdam Neuroscience, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Department of Child and Adolescent Psychiatry, Amsterdam University Medical Center, Amsterdam, The Netherlands.
Transl Psychiatry. 2021 Mar 15;11(1):168. doi: 10.1038/s41398-021-01286-x.
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
目前尚无工具可预测患有重度抑郁症（MDD）的患者是否对某种治疗有反应。对磁共振成像（MRI）数据进行机器学习分析已显示出预测个体患者反应的潜力，这可能使治疗决策个性化并提高治疗效果。在这里，我们评估了 MRI 引导的 MDD 反应预测的准确性。我们对所有使用 MRI 预测 MDD 患者抗抑郁治疗个体反应的研究进行了系统评价和荟萃分析。使用双变量模型计算分类性能，并表示为曲线下面积、敏感性和特异性。此外，我们还分析了不同干预措施和 MRI 方式之间分类性能的差异。荟萃分析了 22 个样本，包括 957 名患者，总体双变量汇总接收器操作特征曲线下面积为 0.84（95%CI 0.81-0.87），敏感性为 77%（95%CI 71-82），特异性为 79%（95%CI 73-84）。虽然电惊厥治疗结果预测（n=285，78%敏感性，83%特异性）的分类性能高于药物治疗结果预测（n=283，75%敏感性，72%特异性），但治疗方法或 MRI 方式之间的分类性能没有显著差异。使用 MRI 数据的机器学习分析预测治疗反应很有前景，但尚未在临床实践中实施。需要具有更具普遍性样本和外部验证的未来研究来确定 MRI 在 MDD 中实现个体化患者护理的潜力。
