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.
Conclusion: The clinical-radiomics model serves as a potential tool for noninvasive differentiation between testicular seminomas and nonseminomas, offering valuable guidance for clinical treatment.
Keywords: Computed tomography; Machine learning; Radiomics; Testicular germ cell tumors.
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