Fan Zhaoxin, Gao Aili, Zhang Jie, Meng Xiangyi, Yin Qunxin, Shen Yongze, Hu Renjie, Gao Shang, Yang Hongge, Xu Yingqi, Liang Hongsheng
Department of Neurosurgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
NHC Key Laboratory of Cell Transplantation, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
J Neurooncol. 2025 Jan;171(2):431-442. doi: 10.1007/s11060-024-04867-0. Epub 2024 Nov 4.
To establish a prediction model combining fractal geometry and radiological features, which consider the complexity of tumour morphology advancing beyond the limitations of previous models.
A 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.
In 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.
Significant 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.
建立一个结合分形几何和放射学特征的预测模型，该模型考虑了肿瘤形态的复杂性，突破了以往模型的局限性。
纳入2021年7月至2023年11月在哈尔滨医科大学附属第一医院就诊的227例患者。计算分形几何并从感兴趣区域（ROI）提取放射组学特征。采用加权基因共表达网络分析（WGCNA）进行初步筛选，以识别与高级别脑膜瘤显著相关的特征。在训练队列中，采用最小绝对收缩和选择算子（LASSO）回归进一步筛选放射组学特征。采用曲线下面积（AUC）评估模型性能。
在整个患者队列中，低级别脑膜瘤的分形维数显著更低（P = 0.01），而高级别脑膜瘤的孔隙度更高（P = 0.049）。分形维数（OR 6.8，95%CI 1.49 - 36.51，P = 0.017）、孔隙度（OR 3.7，95%CI 1.36 - 11.75，P = 0.014）和Rscore（OR 2.8，95%CI 1.55 - 5.75，P = 0.002）是高级别脑膜瘤的独立危险因素。最终结果表明，“分形几何 + 放射学特征（语义特征 + 放射组学特征）”模型在预测高级别脑膜瘤方面表现出最优化性能，训练队列中的AUC为0.854，验证队列中的AUC为0.757。
高级别和低级别脑膜瘤在分形维数和孔隙度上存在显著差异，这些差异可能是潜在的预测因素。所建立的预测模型在预测高级别脑膜瘤方面表现良好。
