Evidence map›Paper›PMID 33553421›Full record

ArticleBioMed research international2021

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.

Mengqiu Cao, Shiteng Suo, Xiao Zhang, Xiaoqing Wang, Jianrong Xu, Wei Yang, Yan Zhou

Registry-linked trialOpen access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06002711 (Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas), which is not on this map. Cited by 15 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 3 pooled it
1.8field-weighted citation impact, top 14% 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.

NCT06002711 recruitingnot on this mapstarted 2023, after this paper: background citation

Multi-Dimensional MRI Spatial Heterogeneity Analysis for Predicting Key Genes and Prognosis of High-Grade Gliomas: A Multi-Center Study

TypeobservationalSponsorRenJi HospitalRan2023 to 2027Enrolled500ConditionsHigh-grade GliomaArmsMR scanning, Clinical data collection
3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.

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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

7 authors at 3 institutions in 1 country.

Mengqiu CaoDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Shiteng SuoDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.ORCID https://orcid.org/0000-0003-2528-2836
Xiao ZhangZhuhai Precision Medical Center, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai 519000, China.
Xiaoqing WangDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Jianrong XuDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Wei YangGuangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.ORCID https://orcid.org/0000-0002-2161-3231
Yan ZhouDepartment of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.ORCID https://orcid.org/0000-0001-9402-1109
Shanghai Jiao Tong University · CNJinan University · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePreoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs. MATERIALS AND

methodsA total of 102 LGG patients were allocated to training (

resultsAfter feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879.

conclusionThe proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

Indexed as

Machine LearningAdolescentAdultAgedBrain NeoplasmsDecision Support Systems, ClinicalFemaleGenotypeGliomaHumansImage Processing, Computer-AssistedIsocitrate DehydrogenaseMagnetic Resonance ImagingMaleMiddle AgedMutationIDH1 protein, humanIsocitrate Dehydrogenase

Identifiers

PMID33553421
PMCPMC7847347
OpenAlexW3124944201

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.