| Literature DB >> 30713592 |
Benjamin S C Wade1,2, Jing Sui3, Stephanie Njau1, Amber M Leaver1, Megha Vasvada1, Boris A Gutman2, Paul M Thompson2, Randal Espinoza4, Roger P Woods1, Christopher C Abbott5, Katherine L Narr1, Shantanu H Joshi1.
Abstract
Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.Entities:
Keywords: Random Forest; electroconvulsive therapy; major depression; shape analysis; treatment response prediction
Year: 2017 PMID： 30713592 PMCID： PMC6354762 DOI： 10.1109/ISBI.2017.7950570
Source DB: PubMed Journal: Proc IEEE Int Symp Biomed Imaging ISSN： 1945-7928