Abstract：
BACKGROUND: Differentiating seminomas from nonseminomas is crucial for formulating optimal treatment strategies for testicular germ cell tumors (TGCTs). Therefore, our study aimed to develop and validate a clinical-radiomics model for this purpose.
METHODS: In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility.
RESULTS: Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas.
CONCLUSIONS: The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.
METHODS: In this study, 221 patients with TGCTs confirmed by pathology from four hospitals were enrolled and classified into training (n = 126), internal validation (n = 55) and external test (n = 40) cohorts. Radiomics features were extracted from the CT images. After feature selection, we constructed a clinical model, radiomics models and clinical-radiomics model with different machine learning algorithms. The top-performing model was chosen utilizing receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was also conducted to assess its practical utility.
RESULTS: Compared with those of the clinical and radiomics models, the clinical-radiomics model demonstrated the highest discriminatory ability, with AUCs of 0.918 (95 % CI: 0.870 - 0.966), 0.909 (95 % CI: 0.829 - 0.988) and 0.839 (95 % CI: 0.709 - 0.968) in the training, validation and test cohorts, respectively. Moreover, DCA confirmed that the combined model had a greater net benefit in predicting seminomas and nonseminomas.
CONCLUSIONS: The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.
摘要：
背景：区分精原细胞瘤和非精原细胞瘤对于制定睾丸生殖细胞肿瘤（TGCT）的最佳治疗策略至关重要。因此,我们的研究旨在开发和验证用于此目的的临床-影像组学模型.
方法：在本研究中，来自四家医院的221例经病理证实的TGCT患者被纳入并分类为培训（n=126），内部验证（n=55）和外部测试（n=40）队列。从CT图像中提取影像组学特征。选择功能后，我们建立了一个临床模型,具有不同机器学习算法的影像组学模型和临床影像组学模型。利用接收器工作特性(ROC)曲线分析选择表现最好的模型。还进行了决策曲线分析（DCA）以评估其实用性。
结果：与临床和影像组学模型相比，临床-影像组学模型表现出最高的辨别能力，AUC为0.918（95%CI：0.870-0.966），训练中的0.909（95%CI：0.829-0.988）和0.839（95%CI：0.709-0.968），验证和测试队列，分别。此外，DCA证实组合模型在预测精原细胞瘤和非精原细胞瘤方面具有更大的净益处。
结论：临床影像组学模型可作为睾丸精原细胞瘤和非精原细胞瘤的非侵入性区分的潜在工具，为临床治疗提供有价值的指导。
方法：在本研究中，来自四家医院的221例经病理证实的TGCT患者被纳入并分类为培训（n=126），内部验证（n=55）和外部测试（n=40）队列。从CT图像中提取影像组学特征。选择功能后，我们建立了一个临床模型,具有不同机器学习算法的影像组学模型和临床影像组学模型。利用接收器工作特性(ROC)曲线分析选择表现最好的模型。还进行了决策曲线分析（DCA）以评估其实用性。
结果：与临床和影像组学模型相比，临床-影像组学模型表现出最高的辨别能力，AUC为0.918（95%CI：0.870-0.966），训练中的0.909（95%CI：0.829-0.988）和0.839（95%CI：0.709-0.968），验证和测试队列，分别。此外，DCA证实组合模型在预测精原细胞瘤和非精原细胞瘤方面具有更大的净益处。
结论：临床影像组学模型可作为睾丸精原细胞瘤和非精原细胞瘤的非侵入性区分的潜在工具，为临床治疗提供有价值的指导。
