Long Changyou, Xu Dan, Sun Wenbo, Liang Weiqiang, Zhou Jie, Gui Shen, Li Huan, Xu Haibo
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Hubei Provincial Engineering Research Center of Multimodal Medical Imaging Technology and Clinical Application, Wuhan clinical research and development center of brain resuscitation and functional imaging, Wuhan, China.
Sci Rep. 2025 May 3;15(1):15485. doi: 10.1038/s41598-025-00113-3.
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) was an independent predictor for p53 expression. FD, Enhancement Quality, and Definition of the enhancing margin were independent predictors for TERTp mutations. The areas under the ROC for each nomogram model were 0.791, 0.739, and 0.601, respectively. Sensitivities were 68.75%, 78.12%, and 51.43%, and specificities were 81.03%, 64.86%, and 71.00%, respectively. CRC showed a high degree of concordance between predicted probabilities and actual observed values, while DCA demonstrated favorable net benefits for all models. VASARI features and fractal analysis effectively predict the Ki_67 PI, p53, and TERTp mutations in WHO grade 3-4 diffuse gliomas. Furthermore, combining these two approaches enhances the predictive performance for TERTp mutations.
有效预测分子特征对于胶质瘤患者的预后评估至关重要。本研究旨在开发一种列线图模型，利用分形分析和可视化可访问伦勃朗图像（VASARI）特征来预测世界卫生组织3-4级弥漫性胶质瘤的分子特征。对2020年1月至2023年12月在本机构经病理证实的世界卫生组织3-4级弥漫性胶质瘤患者的临床数据和VASARI特征进行回顾性分析。术前T1加权增强和T2加权图像用于在3D-Slicer上勾勒肿瘤和周围水肿区域。使用ImageJ软件提取肿瘤和周围水肿的分形维数（FD）和孔隙率。进行单因素和多因素逻辑回归分析，以确定Ki_67增殖指数（PI）、p53和端粒酶逆转录酶启动子（TERTp）突变的独立预测因素。基于这些发现，构建了列线图预测模型。使用受试者工作特征曲线（ROC）、校准曲线（CRC）和决策曲线分析（DCA）对模型性能进行综合评估。性别、强化比例和软脑膜侵犯被确定为Ki_67 PI的独立预测因素。肿瘤的FD是p53表达的独立预测因素。FD、强化质量和强化边缘清晰度是TERTp突变的独立预测因素。每个列线图模型的ROC曲线下面积分别为0.791、0.739和0.601。敏感性分别为68.75%、78.12%和51.43%，特异性分别为81.03%、64.86%和71.00%。CRC显示预测概率与实际观察值之间具有高度一致性，而DCA表明所有模型均具有良好的净效益。VASARI特征和分形分析可有效预测世界卫生组织3-4级弥漫性胶质瘤中的Ki_67 PI、p53和TERTp突变。此外，将这两种方法结合可提高TERTp突变的预测性能。
