Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression.

ABSTRACT:

Introduction

Pretreatment positron emission tomography (PET) with 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials.

Methods

This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤ 7 on 17-item

SUBMITTER: Ali FZ 

PROVIDER: S-EPMC9873411 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature