Evidence map›Paper›PMID 40747552›Full record

ArticleCancer medicine2025

Predicting p53 Status in IDH-Mutant Gliomas Using MRI-Based Radiomic Model.

Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Jiamin LiDepartment of Radiology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated With Jinan University, Zhuhai, China.
Zhihong LanDepartment of Radiology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated With Jinan University, Zhuhai, China.
Xiao ZhangMedical AI Lab, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Xiaoyun LiangInstitute of Research and Clinical Innovations, Neusoft Medical Systems Co. Ltd, Guangzhou, China.
Hanwei ChenDepartment of Radiology, Guangzhou Panyu Health Management Center (Guangzhou Panyu Rehabilitation Hospital), Guangzhou, China.
Xiangrong YuDepartment of Radiology, Zhuhai People's Hospital, Zhuhai Hospital Affiliated With Jinan University, Zhuhai, China.ORCID https://orcid.org/0000-0003-2656-9847

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2022A1515220015National Natural Science Foundation of China 82071915"Spark" Scientific Research Program of the The First Hospital of Hebei Medical University XH202515
6 · The paper itself

Abstract

objectivesAccurate and noninvasive detection of p53 status in isocitrate dehydrogenase mutant (IDH-mt) glioma is clinically meaningful for molecular stratification of glioma, yet it remains challenging. We aimed to investigate the diagnostic efficacy of radiomics utilizing pre-surgery contrast-enhanced T1-weighted imaging (CE-T1WI) for predicting p53 status in IDH-mt gliomas.

methodsA total of seventy-eight patients with pathologically confirmed IDH-mutant glioma were admitted to our institution between January 2011 and October 2018. For each patient, three types of volumes of interest (VOIs) were segmented: (i) VOI

resultsThe VOI

conclusionThe CE-T1WI-based radiomic model can noninvasively predict p53 mutation status in IDH-mt gliomas. The textural differences of peritumoral edema may more accurately reflect the underlying tumor heterogeneity associated with p53 status.

Indexed as

Brain NeoplasmsGliomaIsocitrate DehydrogenaseMagnetic Resonance ImagingMutationTumor Suppressor Protein p53AdultAgedFemaleHumansMachine LearningMaleMiddle AgedNomogramsRadiomicsRetrospective StudiesIsocitrate DehydrogenaseTP53 protein, humanTumor Suppressor Protein p53contrast‐enhanced T1WIgliomaIDH‐mutantp53 statusradiomics

Identifiers

PMID40747552
PMCPMC12314547

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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.