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Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression
INTRODUCTION: Pretreatment positron emission tomography (PET) with 2-deoxy-2-[(18)F]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 purp...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873411/ https://www.ncbi.nlm.nih.gov/pubmed/36699194 http://dx.doi.org/10.1016/j.neuri.2022.100110 |
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author | Ali, Farzana Z. Wengler, Kenneth He, Xiang Nguyen, Minh Hoai Parsey, Ramin V. DeLorenzo, Christine |
author_facet | Ali, Farzana Z. Wengler, Kenneth He, Xiang Nguyen, Minh Hoai Parsey, Ramin V. DeLorenzo, Christine |
author_sort | Ali, Farzana Z. |
collection | PubMed |
description | INTRODUCTION: Pretreatment positron emission tomography (PET) with 2-deoxy-2-[(18)F]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 Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. RESULTS: In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. CONCLUSIONS: The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance. |
format | Online Article Text |
id | pubmed-9873411 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-98734112023-01-24 Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression Ali, Farzana Z. Wengler, Kenneth He, Xiang Nguyen, Minh Hoai Parsey, Ramin V. DeLorenzo, Christine Neurosci Inform Article INTRODUCTION: Pretreatment positron emission tomography (PET) with 2-deoxy-2-[(18)F]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 Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation (n = 40), and 33% test (n = 20) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training. RESULTS: In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission. CONCLUSIONS: The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance. 2022-12 2022-11-11 /pmc/articles/PMC9873411/ /pubmed/36699194 http://dx.doi.org/10.1016/j.neuri.2022.100110 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Ali, Farzana Z. Wengler, Kenneth He, Xiang Nguyen, Minh Hoai Parsey, Ramin V. DeLorenzo, Christine Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
title | Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
title_full | Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
title_fullStr | Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
title_full_unstemmed | Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
title_short | Gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
title_sort | gradient boosting decision-tree-based algorithm with neuroimaging for personalized treatment in depression |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873411/ https://www.ncbi.nlm.nih.gov/pubmed/36699194 http://dx.doi.org/10.1016/j.neuri.2022.100110 |
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