Li Nan, Liu Xuejun, Xia Xiaona, Liu Xushun, Wang Gang, Duan Chongfeng
Department of Information Management, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of Radiology, The Affiliated Hospital of Qingdao University, No.16, Jiang Su Road, Shinan District, Qingdao, Shandong Province, China.
Sci Rep. 2025 May 13;15(1):16614. doi: 10.1038/s41598-025-01665-0.
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas from another hospital composed the test set. The enhanced T1 WI images were used for analysis. The clinical, radiomics and DTL features were selected to construct the model. Radiomics and DTL scores were calculated. The deep transfer learning radiomics (DTLR) nomogram was developed on the basis of selected clinical features, radiomics scores and DTL scores. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were drawn. The clinical features of sex, shape, indistinct margin and peritumoral edema were selected and used to construct the clinical model. Thirty-two radiomics features and 28 DTL features were selected for model construction. The clinical model had an AUC of 0.788. (95% CI: 0.6996-0.8756), with an accuracy of 0.745, a sensitivity of 0.941, and a specificity of 0.549 in the test set. The DTLR nomogram had the highest AUC of 0.866 (95% CI: 0.7984-0.9340), with an accuracy of 0.804, a sensitivity of 0.745, and a specificity of 0.863 in the test set. Compared with the other models, the DTLR nomogram had the greatest net benefit according to the DCA. There was a significant difference between the DTLR nomogram and the clinical model, no significant difference between the rest models in DeLong test.The DTLR nomogram has superior predictive value in DCA and could be a valuable method in clinical decision-making. Given the results of DeLong test, only the radiomics model is sufficient and there is no need to add DTL features. As a new attempt, the DTLR nomogram needs to be improved in the future study.
本研究的目的是基于临床、影像组学和深度迁移学习（DTL）特征建立一种列线图，以预测脑膜瘤分级。来自一家医院的340例脑膜瘤组成训练集，另一家医院的102例脑膜瘤组成测试集。采用增强T1加权成像进行分析。选择临床、影像组学和DTL特征构建模型。计算影像组学和DTL评分。基于选定的临床特征、影像组学评分和DTL评分开发深度迁移学习影像组学（DTLR）列线图。绘制受试者操作特征（ROC）曲线和决策曲线分析（DCA）曲线。选择性别、形态、边界不清和瘤周水肿等临床特征构建临床模型。选择32个影像组学特征和28个DTL特征进行模型构建。临床模型在测试集中的AUC为0.788（95%CI：0.6996 - 0.8756），准确率为0.745，灵敏度为0.941，特异度为0.549。DTLR列线图在测试集中的AUC最高，为0.866（95%CI：0.7984 - 0.9340），准确率为0.804，灵敏度为0.745，特异度为0.863。根据DCA，与其他模型相比，DTLR列线图的净效益最大。DTLR列线图与临床模型之间存在显著差异，其余模型在DeLong检验中无显著差异。DTLR列线图在DCA中具有较高的预测价值，可能是临床决策中的一种有价值的方法。根据DeLong检验结果，仅影像组学模型就足够了，无需添加DTL特征。作为一种新的尝试，DTLR列线图在未来的研究中需要改进。
