Non-invasive differential diagnosis of teratomas from other intracranial germ cell tumours using MRI-based fractal and radiomic analyses

Objectives The histologic subtype of intracranial germ cell tumours (IGCTs) is an important factor in deciding the treatment strategy, especially for teratomas. In this study, we aimed to non-invasively diagnose teratomas based on fractal and radiomic features. Materials and methods This retrospective study included 330 IGCT patients, including a discovery set ( n = 296) and an independent validation set ( n = 34). Fractal and radiomic features were extracted from T1-weighted, T2-weighted, and post-contrast T1-weighted images. Five classifiers, including logistic regression, random forests, support vector machines, K-nearest neighbours, and XGBoost, were compared for our task. Based on the optimal classifier, we compared the performance of clinical, fractal, and radiomic models and the model combining these features in predicting teratomas. Results Among the diagnostic models, the fractal and radiomic models performed better than the clinical model. The final model that combined all the features showed the best performance, with an area under the curve, precision, sensitivity, and specificity of 0.946 [95% confidence interval (CI): 0.882–0.994], 95.65% (95% CI: 88.64–100%), 88.00% (95% CI: 77.78–96.36%), and 91.67% (95% CI: 78.26–100%), respectively, in the test set of the discovery set, and 0.944 (95% CI: 0.855–1.000), 85.71% (95% CI: 68.18–100%), 94.74% (95% CI: 83.33–100%), and 80.00% (95% CI: 58.33–100%), respectively, in the independent validation set. SHapley Additive exPlanations indicated that two fractal features, two radiomic features, and age were the top five features highly associated with the presence of teratomas. Conclusion The predictive model including image and clinical features could help guide treatment strategies for IGCTs. Clinical relevance statement Our machine learning model including image and clinical features can non-invasively predict teratoma components, which could help guide treatment strategies for intracranial germ cell tumours (IGCT). Key Points • Fractals and radiomics can quantitatively evaluate imaging characteristics of intracranial germ cell tumours. • Model combing imaging and clinical features had the best predictive performance. • The diagnostic model could guide treatment strategies for intracranial germ cell tumours.