ArticleEuropean radiology2020
Magnetic resonance imaging-based 3-dimensional fractal dimension and lacunarity analyses may predict the meningioma grade.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 31 citations in OpenAlex.
- A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.BioMed research international · 2022Trial
- 3D fractal dimension analysis of CT imaging for microvascular invasion prediction in hepatocellular carcinoma.European radiology · 2026Article
- Diagnostic efficacy of dual-energy computed tomography-based fractal analysis for assessing extramural venous invasion/tumor deposits and peripheral nerve invasion in rectal cancer.Quantitative imaging in medicine and surgery · 2026Article
- Fractal-geometry analysis of pediatric posterior fossa tumors - a preoperative tool for prediction of histopathology.Neurosurgical review · 2026Article
- Fractal analysis of tumor subregions differentiates benign from malignant breast lesions.Polish journal of radiology · 2026Article
- Consistency and grade prediction of intracranial meningiomas based on fractal geometry analysis.Neurosurgical review · 2025Article
- A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images.NPJ precision oncology · 2025Article
- Study of prediction model for high-grade meningioma using fractal geometry combined with radiological features.Journal of neuro-oncology · 2025Article
- Combination of optical coherence tomography-derived shape and texture features are associated with development of sub-foveal geographic atrophy in dry AMD.Scientific reports · 2024Article
- Non-invasive differential diagnosis of teratomas from other intracranial germ cell tumours using MRI-based fractal and radiomic analyses.European radiology · 2024Article
- Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD.IEEE transactions on bio-medical engineering · 2023Article
- Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.European radiology · 2023Article
- Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features.International journal of computer assisted radiology and surgery · 2023Article
- Updated Systematic Review on the Role of Brain Invasion in Intracranial Meningiomas: What, When, Why?Cancers · 2022Review
- Dual-energy CT-based radiomics nomogram in predicting histological differentiation of head and neck squamous carcinoma: a multicenter study.Neuroradiology · 2022Article
- Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.Frontiers in oncology · 2022Review
- Article
- Morphological and Fractal Properties of Brain Tumors.Frontiers in physiology · 2022Article
- Shape matters: morphological metrics of glioblastoma imaging abnormalities as biomarkers of prognosis.Scientific reports · 2021Article
- Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.Circulation. Arrhythmia and electrophysiology · 2021Article
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9 authors at 2 institutions in 1 country.
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Abstract
objectiveTo assess whether 3-dimensional (3D) fractal dimension (FD) and lacunarity features from MRI can predict the meningioma grade.
methodsThis retrospective study included 131 patients with meningiomas (98 low-grade, 33 high-grade) who underwent preoperative MRI with post-contrast T1-weighted imaging. The 3D FD and lacunarity parameters from the enhancing portion of the tumor were extracted by box-counting algorithms. Inter-rater reliability was assessed with the intraclass correlation coefficient (ICC). Additionally, conventional imaging features such as location, heterogeneous enhancement, capsular enhancement, and necrosis were assessed. Independent clinical and imaging risk factors for meningioma grade were investigated using multivariable logistic regression. The discriminative value of the prediction model with and without fractal features was evaluated. The relationship of fractal parameters with the mitosis count and Ki-67 labeling index was also assessed.
resultsThe inter-reader reliability was excellent, with ICCs of 0.99 for FD and 0.97 for lacunarity. High-grade meningiomas had higher FD (p < 0.001) and higher lacunarity (p = 0.007) than low-grade meningiomas. In the multivariable logistic regression, the diagnostic performance of the model with clinical and conventional imaging features increased with 3D fractal features for predicting the meningioma grade, with AUCs of 0.78 and 0.84, respectively. The 3D FD showed significant correlations with both mitosis count and Ki-67 labeling index, and lacunarity showed a significant correlation with the Ki-67 labeling index (all p values < 0.05).
conclusionThe 3D FD and lacunarity are higher in high-grade meningiomas and fractal analysis may be a useful imaging biomarker for predicting the meningioma grade. KEY POINTS: • Fractal dimension (FD) and lacunarity are the two parameters used in fractal analysis to describe the complexity of a subject and may aid in predicting meningioma grade. • High-grade meningiomas had a higher fractal dimension and higher lacunarity than low-grade meningiomas, suggesting higher complexity and higher rotational variance. • The discriminative value of the predictive model using clinical and conventional imaging features improved when combined with 3D fractal features for predicting the meningioma grade.
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