Evidence map›Paper›PMID 39497017›Full record

ArticleJournal of neuro-oncology2025

Study of prediction model for high-grade meningioma using fractal geometry combined with radiological features.

Zhaoxin Fan, Aili Gao, Jie Zhang, Xiangyi Meng, Qunxin Yin, Yongze Shen, Renjie Hu, Shang Gao, Hongge Yang, Yingqi Xu and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025
    Review
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.

Zhaoxin Fan *Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Aili Gao *School of Life Science, Northeast Agricultural University, Harbin, Heilongjiang Province, China.
Jie ZhangDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Xiangyi MengDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Qunxin YinEye Hospital, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Yongze ShenDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Renjie HuDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Shang GaoDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Hongge YangDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
Yingqi XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China. xuyingqi1987@163.com.
Hongsheng LiangDepartment of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China. lianghongsheng@hrbmu.edu.cn.

Funding

National Natural Science Foundations of China 82271340Outstanding Youth Funding of Harbin Medical University HYD2020JQ0016
6 · The paper itself

Abstract

purposeTo establish a prediction model combining fractal geometry and radiological features, which consider the complexity of tumour morphology advancing beyond the limitations of previous models.

methodsA total of 227 patients at the First Affiliated Hospital of Harbin Medical University from July 2021 to November 2023 were included. Fractal geometry was calculated and the radiomics features were extracted from regions of interest (ROIs). Weighted Gene Co-Expression Network Analysis (WGCNA) was employed for preliminary screening to identify those that were significantly associated with high-grade meningioma. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression was employed for further screening the radiomics features. Area under curve (AUC) was to evaluate models' performance.

resultsIn entire patient cohort, low-grade meningiomas had significantly lower fractal dimensions (P = 0.01), while high-grade meningiomas had higher lacunarity (P = 0.049). Fractal dimension (OR 6.8, 95% CI 1.49-36.51, P = 0.017), lacunarity (OR 3.7, 95% CI 1.36-11.75, P = 0.014), and Rscore (OR 2.8, 95% CI 1.55-5.75, P = 0.002) were independent risk factors for high-grade meningiomas. The final results demonstrated that the "fractal geometry + radiological features (semantic features + radiomics features)" model exhibited the most optimal performance in predicting high-grade meningioma, with an AUC of 0.854 in the training cohort and 0.757 in the validation cohort.

conclusionSignificant differences in fractal dimension and lacunarity exist between high-grade and low-grade meningiomas, which can be potential predictive factors. The developed predictive model demonstrated good performance in predicting high-grade meningiomas.

Indexed as

FractalsMeningeal NeoplasmsMeningiomaNeoplasm GradingAdultAgedFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedPrognosisFractal geometryHigh-grade meningiomaNomogram predictive modelRadiomicsWeighted gene co-expression network analysis

Identifiers

PMID39497017

What OpenQuestion holds

Textmetadata
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.