Evidence map›Paper›PMID 41120880›Full record

ArticleBMC medical imaging2025

Predictive power of combined inflammatory markers and magnetic resonance imaging features for glioma grading using machine learning: a retrospective study.

Yaohua Liu, Yuting Wang, Junle Zhu, Shuang Qin, Yi Hong, Saifei Sun, Qin Zhang, Qi Lv

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yaohua Liu *Department of Pediatrics, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Yuting Wang *Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Junle ZhuDepartment of Neurosurgery, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Shuang QinDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Yi HongDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Saifei SunDepartment of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Qin ZhangDepartment of Clinical Research Center, Tongji Hospital, Tongji University School of Medicine, Xincun Road No. 389, Shanghai, China.
Qi LvDepartment of Medical Imaging, Fudan University Affiliated Xuhui Hospital (Shanghai Xuhui Central Hospital), 366 Longchuan North Road, Xuhui District, Shanghai, 200031, China. lye3210@vip.sina.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study aimed to explore the potential of integrating magnetic resonance imaging (MRI) features with inflammatory markers to predict glioma grading using machine learning models.

methodsA total of 179 glioma patients were analyzed. The dataset was randomly split into a training set (75%) and an independent test set (25%) to test hyperparameter. To enhance reliability, five-fold cross-validation was also applied during the training phase. Key MRI features and inflammatory markers, including relative apparent diffusion coefficient (rADC), monocyte count, lymphocyte-to-CRP ratio (LCR) and hemoglobin-albumin-lymphocyte-platelet index (HALP), were extracted and used as inputs for multiple machine learning classifiers. Model performance was assessed using metrics such as area under the curve (AUC), accuracy, and F1 score.

resultsThe support vector machine model exhibited superior predictive performance, achieving an AUC of 0.92 and an F1 score of 0.91, effectively distinguishing between high-grade and low-grade gliomas.

conclusionsThe combination of MRI features and inflammatory markers, analyzed through machine learning models like SVM, provides some clues for refining glioma prognosis and guiding personalized treatment strategies.

Indexed as

Brain NeoplasmsGliomaMachine LearningMagnetic Resonance ImagingAdultBiomarkersBiomarkers, TumorFemaleHumansInflammationMaleMiddle AgedNeoplasm GradingPredictive Learning ModelsPredictive Value of TestsReproducibility of ResultsBiomarkersBiomarkers, TumorGlioma gradingInflammatory markersMachine learningMagnetic resonance imaging

Identifiers

PMID41120880
PMCPMC12542063

What Socratic holds

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LicenceCC BY
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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 Socratic graph.