ArticleScientific reports2025
Nomogram prediction of molecular characteristics in WHO grade 3-4 diffuse gliomas based on fractal analysis and VASARI features.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A fractal-enhanced deep learning framework for forecasting agricultural production in India.Scientific reports · 2026Article
- Radiomic features from intratumoral and peritumoral regions on portal venous phase CT for multicenter prediction of TP53 mutation in pancreatic cancer.Frontiers in oncology · 2026Article
- [Predicting Invasive Non-mucinous Lung Adenocarcinoma IASLC Grading: A Nomogram Based on Dual-energy CT Imaging and Conventional Features].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025Article
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8 authors.
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Abstract
Effective prediction of molecular features is crucial for the prognostic assessment of glioma patients. This study aims to develop a nomogram model using fractal analysis and Visually AcceSAble Rembrandt Images (VASARI) features to predict the molecular characteristics of WHO Grade 3-4 diffuse gliomas. Retrospective analysis of clinical data and VASARI features of patients with WHO grade 3-4 diffuse gliomas confirmed by pathology between January 2020 and December 2023 at our institution. Preoperative T1-weighted contrast-enhanced and T2-weighted images were used to delineate the tumor and surrounding edema regions on 3D-Slicer. Fractal dimension (FD) and lacunarity of both the tumor and surrounding edema were extracted using ImageJ software. Univariate and multivariate logistic regression analyses were performed to identify independent predictive factors for the Ki_67 proliferation index (PI), p53, and telomerase reverse transcriptase promoter (TERTp) mutations. Based on these findings, a nomogram prediction model was constructed. Model performance was comprehensively assessed using the receiver operating characteristic curve (ROC), calibration curve (CRC), and decision curve analysis (DCA). Sex, Proportion Enhancing, and Pial invasion were identified as independent predictive factors for the Ki_67 PI. FD of the tumor (FD
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