Abstract
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.
Highlights
Major depressive disorder (MDD) is a debilitating disease, accounting for 40% of the global disability-adjusted life years caused by psychiatric disorders[1]
We further investigated whether classification performance was influenced by intervention type or imaging modality (i.e., structural magnetic resonance imaging (MRI), resting-state functional MRI, task-based fMRI, diffusion tensor imaging (DTI))
We did not find a difference in classification performance between studies using pharmacotherapy and electroconvulsive therapy (ECT)
Summary
Major depressive disorder (MDD) is a debilitating disease, accounting for 40% of the global disability-adjusted life years caused by psychiatric disorders[1]. Depression is associated with impaired social functioning and unemployment and is associated with a wide range of chronic physical illnesses, such as diabetes and cardiovascular disease[2,3]. MDD is estimated to have a life-time. Patients are regularly exposed to multiple failed treatments and might spend months to years waiting for successful treatment. This stresses the need for markers, which, before treatment commencement, can inform clinicians on the chance of responding to a particular treatment
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