Evidence map›Paper›PMID 37439176›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2023

[Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics].

M Lu, Y Qu, A Ma, J Zhu, X Zou, G Lin, Y Li, X Liu, Z Wen

Open access · greenAbstract readEnglish Abstract
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
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

9 authors at 2 institutions in 1 country.

M LuDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
Y QuDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
A MaDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
J ZhuDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
X ZouDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
G LinDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
Y LiDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
X LiuDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
Z WenDepartment of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China.
Zhujiang Hospital · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics.

methodsWe collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T

resultsThe 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively;

conclusionMultiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.

Indexed as

GliomaMagnetic Resonance ImagingArea Under CurveChromosome AberrationsHumansROC Curve1p/19q codeletiondiffuse lower-grade gliomamagnetic resonance imagingradiomics

Identifiers

PMID37439176
PMCPMC10339309
OpenAlexW4384120502

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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.