A deep learning model for differentiating paediatric intracranial germ cell tumour subtypes and predicting survival with MRI: a multicentre prospective study

Abstract

BackgroundThe pretherapeutic differentiation of subtypes of primary intracranial germ cell tumours (iGCTs), including germinomas (GEs) and nongerminomatous germ cell tumours (NGGCTs), is essential for clinical practice because of distinct treatment strategies and prognostic profiles of these diseases. This study aimed to develop a deep learning model, iGNet, to assist in the differentiation and prognostication of iGCT subtypes by employing pretherapeutic MR T2-weighted imaging.MethodsThe iGNet model, which is based on the nnUNet architecture, was developed using a retrospective dataset of 280 pathologically confirmed iGCT patients. The training dataset included 83 GEs and 117 NGGCTs, while the retrospective internal test dataset included 31 GEs and 49 NGGCTs. The model’s diagnostic performance was then assessed with the area under the receiver operating characteristic curve (AUC) in a prospective internal dataset (n = 22) and two external datasets (n = 22 and 20). Next, we compared the diagnostic performance of six neuroradiologists with or without the assistance of iGNet. Finally, the predictive ability of the output of iGNet for progression-free and overall survival was assessed and compared to that of the pathological diagnosis.ResultsiGNet achieved high diagnostic performance, with AUCs between 0.869 and 0.950 across the four test datasets. With the assistance of iGNet, the six neuroradiologists’ diagnostic AUCs (averages of the four test datasets) increased by 9.22% to 17.90%. There was no significant difference between the output of iGNet and the results of pathological diagnosis in predicting progression-free and overall survival (P = .889).ConclusionsBy leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.Graphical