Evidence map›Paper›PMID 35153659›Full record

ArticleFrontiers in neuroscience2021

Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma.

Shouchao Wang, Feng Xiao, Wenbo Sun, Chao Yang, Chao Ma, Yong Huang, Dan Xu, Lanqing Li, Jun Chen, Huan Li and 1 more

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  11. Radiomics in Spine Surgery.International journal of spine surgery · 2023
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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

11 authors.

Shouchao WangDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Feng XiaoDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Wenbo SunDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Chao YangDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Chao MaDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Yong HuangDepartment of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Dan XuDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Lanqing LiDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Jun ChenPrecision Health Institute, GE Healthcare, Shanghai, China.
Huan LiDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Haibo XuDepartment of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients. MATERIALS AND

methodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman's correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan-Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.

resultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman's correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (

conclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.

Indexed as

glioblastomaisocitrate dehydrogenase wildtypeMRInomogramradiomics

Identifiers

PMID35153659
PMCPMC8833841

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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.