Evidence map›Paper›PMID 38098041›Full record

ArticleBMC cancer2023

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis.

Jie Bai, Mengyang He, Eryuan Gao, Guang Yang, Hongxi Yang, Jie Dong, Xiaoyue Ma, Yufei Gao, Huiting Zhang, Xu Yan and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. 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

13 authors.

Jie Bai *Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
Mengyang He *School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China.
Eryuan GaoDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
Guang YangShanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
Hongxi YangShanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China.
Jie DongSchool of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Xiaoyue MaDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
Yufei GaoSchool of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China.
Huiting ZhangMR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China.
Xu YanMR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China.
Yong ZhangDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
Jingliang ChengDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China.
Guohua ZhaoDepartment of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, No. Jianshe Dong Road, Zhengzhou, 450052, China. ghzhao@ha.edu.cn.

Funding

the National Natural Science Foundation of China 82202270the Natural Science Foundation of Henan Province 232300421298the Scientific and Technological Research Project of Henan Province LHGJ20220403
6 · The paper itself

Abstract

backgroundWe created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM).

methodsWe conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison.

resultsNODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUC

conclusionPreoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.

Indexed as

Brain NeoplasmsGlioblastomaEdemaHumansMagnetic Resonance ImagingNecrosisNeuritesRetrospective StudiesGlioblastomaNODDIRadiomic texture analysisSolitary brain metastasis

Identifiers

PMID38098041
PMCPMC10722697

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.