Evidence map›Paper›PMID 39285364›Full record

ArticleBMC medical imaging2024

The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma.

He-Xin Liang, Zong-Ying Wang, Yao Li, An-Ning Ren, Zhi-Feng Chen, Xi-Zhen Wang, Xi-Ming Wang, Zhen-Guo Yuan

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Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
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7citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  5. Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

He-Xin Liang *Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Zong-Ying Wang *Department of Radiology, Weifang People's Hospital, Weifang, China.
Yao LiZhucheng People's Hospital, Weifang, China.
An-Ning RenMedical Imaging Center of the Affiliated Hospital of Weifang Medical University, Weifang, China.
Zhi-Feng ChenMonash Biomedical Imaging, Monash University, Clayton, VIC, Australia.
Xi-Zhen WangMedical Imaging Center of the Affiliated Hospital of Weifang Medical University, Weifang, China.
Xi-Ming WangDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Zhen-Guo YuanDepartment of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. yuanzg88@126.com.

Funding

Key research and development program of shandong province GG201710080117Project of Medicine and Health Development Plan of Shandong Province 2011HW067Shandong province science and technology development projection 2012 YD18064Shandong Provincial Natural Science Foundation of China ZR2013HM071
6 · The paper itself

Abstract

purposeTo investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma.

methodsThe DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation.

resultsBoth in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively.

conclusionThe radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.

Indexed as

Brain NeoplasmsGliomaIsocitrate DehydrogenaseKi-67 AntigenMutationSupport Vector MachineAdultAgedContrast MediaDiffusion Magnetic Resonance ImagingFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedMultimodal ImagingContrast MediaIDH1 protein, humanIsocitrate DehydrogenaseKi-67 AntigenGliomaIDH-1Ki-67Support vector machine

Identifiers

PMID39285364
PMCPMC11403938

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