Evidence map›Paper›PMID 32528327›Full record

ArticleFrontiers in psychiatry2020

Neuroanatomical Features That Predict Response to Electroconvulsive Therapy Combined With Antipsychotics in Schizophrenia: A Magnetic Resonance Imaging Study Using Radiomics Strategy.

Yi-Bin Xi, Long-Biao Cui, Jie Gong, Yu-Fei Fu, Xu-Sha Wu, Fan Guo, Xuejuan Yang, Chen Li, Xing-Rui Wang, Ping Li and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Yi-Bin XiDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Long-Biao CuiDepartment of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.
Jie GongSchool of Life Sciences and Technology, Xidian University, Xi'an, China.
Yu-Fei FuDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xu-Sha WuDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Fan GuoDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xuejuan YangSchool of Life Sciences and Technology, Xidian University, Xi'an, China.
Chen LiDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xing-Rui WangDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Ping LiDepartment of Radiology, Xi'an Mental Health Center, Xi'an, China.
Wei QinSchool of Life Sciences and Technology, Xidian University, Xi'an, China.
Hong YinDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Xijing Hospital · CNAir Force Medical University · CNXidian University · CNXian Mental Health Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveNeuroimaging-based brain signatures may be informative in identifying patients with psychosis who will respond to antipsychotics. However, signatures that inform the electroconvulsive therapy (ECT) health care professional about the response likelihood remain unclear in psychosis with radiomics strategy. This study investigated whether brain structure-based signature in the prediction of ECT response in a sample of schizophrenia patients using radiomics approach.

methodsThis high-resolution structural magnetic resonance imaging study included 57 patients at baseline. After ECT combined with antipsychotics, 28 and 29 patients were classified as responders and non-responders. Features of gray matter were extracted and compared. The logistic regression model/support vector machine (LRM/SVM) analysis was used to explore the predictive performance.

resultsThe regularized multivariate LRM accurately discriminated responders from non-responders, with an accuracy of 90.91%. The structural features were further confirmed in the validating data set, resulting in an accuracy of 87.59%. The accuracy of the SVM in the training set was 90.91%, and the accuracy in the validation set was 91.78%.

conclusionOur results support the possible use of structural brain feature-based radiomics as a potential tool for predicting ECT response in patients with schizophrenia undergoing antipsychotics, paving the way for utilization of markers in psychosis.

Indexed as

electroconvulsive therapypredictionradiomicsresponseschizophrenia

Identifiers

PMID32528327
PMCPMC7253706
OpenAlexW3026146147

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.