Peng Jiao, Tang Zhen, Li Tao, Pan Xiaoyu, Feng Lijuan, Long Liling
Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Radiology, Liuzhou Workers Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
Front Oncol. 2024 Aug 14;14:1427122. doi: 10.3389/fonc.2024.1427122. eCollection 2024.
To evaluate the performance of a clinical-radiomics model based on contrast-enhanced computed tomography (CE-CT) in assessing human epidermal growth factor receptor 2 (HER2) status in urothelial bladder carcinoma (UBC).
From January 2022 to December 2023, 124 patients with UBC were classified into the training (n=100) and test (n=24) sets. CE-CT scans were performed on the patients. Univariate and multivariate analyses were conducted to identify independent predictors of HER2 status in patients with UBC. We employed eight machine learning algorithms to establish radiomic models. A clinical-radiomics model was developed by integrating radiomic signatures and clinical features. Receiver operating characteristic curves and decision curve analysis (DCA) were generated to evaluate and validate the predictive capabilities of the models.
Among the eight classifiers, the random forest radiomics model based on CE-CT demonstrated the highest efficacy in predicting HER2 status, with area under the curve (AUC) values of 0.880 (95% CI: 0.813-0.946) and 0.814 (95% CI: 0.642-0.986) in the training and test sets, respectively. In the training set, the clinical-radiomics model achieved an AUC of 0.935, an accuracy of 0.870, a sensitivity of 0.881, and a specificity of 0.854. In the test set, the clinical-radiomics model achieved an AUC of 0.857, an accuracy of 0.760, a sensitivity of 0.643, and a specificity of 0.900. DCA analysis indicated that the clinical-radiomics model provided good clinical benefit.
The radiomics nomogram demonstrates good diagnostic performance in predicting HER2 expression in patients with UBC.
评估基于对比增强计算机断层扫描（CE-CT）的临床放射组学模型在评估尿路上皮膀胱癌（UBC）中人类表皮生长因子受体2（HER2）状态的性能。
2022年1月至2023年12月，124例UBC患者被分为训练集（n = 100）和测试集（n = 24）。对患者进行CE-CT扫描。进行单因素和多因素分析以确定UBC患者HER2状态的独立预测因素。我们采用八种机器学习算法建立放射组学模型。通过整合放射组学特征和临床特征建立临床放射组学模型。生成受试者工作特征曲线和决策曲线分析（DCA）以评估和验证模型的预测能力。
在八个分类器中，基于CE-CT的随机森林放射组学模型在预测HER2状态方面表现出最高的效能，训练集和测试集的曲线下面积（AUC）值分别为0.880（95%CI：0.813 - 0.946）和0.814（95%CI：0.642 - 0.986）。在训练集中，临床放射组学模型的AUC为0.935，准确率为0.870，灵敏度为0.881，特异性为0.854。在测试集中，临床放射组学模型的AUC为0.857，准确率为0.760，灵敏度为0.643，特异性为0.900。DCA分析表明临床放射组学模型具有良好的临床效益。
放射组学列线图在预测UBC患者HER2表达方面具有良好的诊断性能。
