Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.
Journal
Translational psychiatry
ISSN: 2158-3188
Titre abrégé: Transl Psychiatry
Pays: United States
ID NLM: 101562664
Informations de publication
Date de publication:
16 11 2022
16 11 2022
Historique:
received:
24
02
2022
accepted:
27
10
2022
revised:
21
10
2022
entrez:
17
11
2022
pubmed:
18
11
2022
medline:
22
11
2022
Statut:
epublish
Résumé
Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics texture features describe the interrelationship between voxel intensities across multiple spatial scales capturing the hidden information of underlying disease dynamics in addition to volumetric changes. Structural MR images were acquired from 77 first-episode psychosis (FEP) patients, 58 clinical high-risk subjects with no later transition to psychosis (CHR_NT), 15 clinical high-risk subjects with later transition (CHR_T), and 44 healthy controls (HC). Radiomics texture features were extracted from non-segmented images, and two-classification schemas were performed for the identification of FEP vs. HC and FEP vs. CHR_NT. The group of CHR_T was used as external validation in both schemas. The classification of a subject's clinical status was predicted by importing separately (a) the difference of entropy feature map and (b) the contrast feature map, resulting in classification balanced accuracy above 72% in both analyses. The proposed framework enhances the classification decision for FEP, CHR_NT, and HC subjects, verifies diagnosis-relevant features and may potentially contribute to identification of structural biomarkers for psychosis, beyond and above volumetric brain changes.
Identifiants
pubmed: 36385133
doi: 10.1038/s41398-022-02242-z
pii: 10.1038/s41398-022-02242-z
pmc: PMC9668814
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
481Subventions
Organisme : NIH HHS
ID : S10 OD023495
Pays : United States
Informations de copyright
© 2022. The Author(s).
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