Gui Yuan, Hu Wei, Ren Jialiang, Tang Fuqiang, Wang Limei, Zhang Fang, Zhang Jing
Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, zhufengdadao No.1439, Zhuhai, Doumen District, China.
School of Medical Imaging, Zunyi Medical University, Zunyi, China.
Cancer Imaging. 2025 Feb 28;25(1):20. doi: 10.1186/s40644-025-00845-5.
Exploring the construction of a fusion model that combines radiomics and deep learning (DL) features is of great significance for the precise preoperative diagnosis of meningioma sinus invasion.
This study retrospectively collected data from 601 patients with meningioma confirmed by surgical pathology. For each patient, 3948 radiomics features, 12,288 VGG features, 6144 ResNet features, and 3072 DenseNet features were extracted from MRI images. Thus, univariate logistic regression, correlation analysis, and the Boruta algorithm were applied for further feature dimension reduction, selecting radiomics and DL features highly associated with meningioma sinus invasion. Finally, diagnosis models were constructed using the random forest (RF) algorithm. Additionally, the diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and AUC values of different models were compared using the DeLong test.
Ultimately, 21 features highly associated with meningioma sinus invasion were selected, including 6 radiomics features, 2 VGG features, 7 ResNet features, and 6 DenseNet features. Based on these features, five models were constructed: the radiomics model, VGG model, ResNet model, DenseNet model, and DL-radiomics (DLR) fusion model. This fusion model demonstrated superior diagnostic performance, with AUC values of 0.818, 0.814, and 0.769 in the training set, internal validation set, and independent external validation set, respectively. Furthermore, the results of the DeLong test indicated that there were significant differences between the fusion model and both the radiomics model and the VGG model (p < 0.05).
The fusion model combining radiomics and DL features exhibits superior diagnostic performance in preoperative diagnosis of meningioma sinus invasion. It is expected to become a powerful tool for clinical surgical plan selection and patient prognosis assessment.
探索构建一种结合放射组学和深度学习（DL）特征的融合模型，对脑膜瘤窦侵犯的术前精准诊断具有重要意义。
本研究回顾性收集了601例经手术病理证实的脑膜瘤患者的数据。对每位患者，从MRI图像中提取了3948个放射组学特征、12288个VGG特征、6144个ResNet特征和3072个DenseNet特征。因此，应用单因素逻辑回归、相关性分析和Boruta算法进行进一步的特征降维，选择与脑膜瘤窦侵犯高度相关的放射组学和DL特征。最后，使用随机森林（RF）算法构建诊断模型。此外，使用受试者工作特征（ROC）曲线评估不同模型的诊断性能，并使用DeLong检验比较不同模型的AUC值。
最终，选择了21个与脑膜瘤窦侵犯高度相关的特征，包括6个放射组学特征、2个VGG特征、7个ResNet特征和6个DenseNet特征。基于这些特征，构建了五个模型：放射组学模型、VGG模型、ResNet模型、DenseNet模型和DL-放射组学（DLR）融合模型。该融合模型表现出卓越的诊断性能，在训练集、内部验证集和独立外部验证集中的AUC值分别为0.818、0.814和0.769。此外，DeLong检验结果表明，融合模型与放射组学模型和VGG模型之间存在显著差异（p<0.05）。
结合放射组学和DL特征的融合模型在脑膜瘤窦侵犯的术前诊断中表现出卓越的诊断性能。有望成为临床手术方案选择和患者预后评估的有力工具。
