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

Abstract

Background: The 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.

Methods: The 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.

Results: iGNet 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).

Conclusions: By leveraging pretherapeutic MR imaging data, iGNet accurately differentiates iGCT subtypes, facilitating prognostic evaluation and increasing the potential for tailored treatment.

Keywords: Deep learning (DL); Diagnostic model; Magnetic resonance imaging (MRI); Primary intracranial germ cell tumours (iGCTs); Prognostic evaluation.

Fig. 1

Patient enrolment process and dataset distribution for the study. The figure shows the total number of patients with pathologically confirmed intracranial germ cell tumours (iGCTs), including in the development cohort (n = 280), training cohort (n = 200), and retrospective internal test cohort (n = 80). The exclusion criteria are as follows: significant MRI artefacts, preimaging chemotherapy or radiation therapy, and preimaging biopsy. The figure also describes the prospective internal test dataset and two external test datasets from Beijing Sanbo Hospital and Tianjin Huanhu Hospital, specifying the number of patients and exclusion criteria for each

Fig. 2

Performance and application results of iGNet. A ROC curves displaying iGNet’s discriminative ability in the retrospective internal test dataset and three independent test datasets. B Representative T2-weighted MR image examples and corresponding iGNet predictions across the four independent datasets. Saliency maps highlight regions that influenced the performance of the model, with colour-coded voxel predictions for GEs (red) and NGGCTs (green). C Comparison of the performance of iGNet against a model that integrates conventional clinical information with iGNet outputs, as well as a DL model using multimodal MR images. D Subgroup performance metrics for iGNet, presented as the accuracy, specificity, sensitivity, and AUC values, alongside their 95% CIs from bootstrap analysis (N = 2000 replicates). Sensitivity and specificity were calculated at a threshold matched to average reader sensitivity. The frequency of GEs and NGGCTs per subgroup is visualized with bar plots. The full numerical values for each subgroup are available in Table S4

Fig. 3

A Enhancement of neuroradiological diagnosis with iGNet assistance. Improvement in the average AUCs for the neuroradiologists’ diagnoses was observed for the retrospective internal test and independent test datasets upon referencing the output of iGNet, with significant percentage increases (P < .05). B Kappa coefficient enhancements indicating improved diagnostic consistency among neuroradiologists utilizing iGNet, with specific percentage improvements noted (P < .001). C Kaplan‒Meier plots for PFS and OS categorized by iGNet’s MR-based predictions. D Kaplan‒Meier plots for PFS and OS categorized by pathological diagnosis

Fig. 4

A A 20-year-old male who experienced headaches for 3 months presented with a pineal lesion. In October 2020, his serum AFP level was 8.69 ng/ml (normal level: < 7 ng/ml). By November 2020, his serum AFP had increased to 10.05 ng/ml. Additionally, in November 2020, his CSF AFP level was 7.11 ng/ml, which decreased to < 0.5 ng/ml following one cycle of chemotherapy. B An 8-year-old female suffering from headaches and diminished vision for 5 months presented with a suprasellar lesion. In May 2021, her serum AFP was significantly elevated, at 353.1 ng/ml. After two cycles of chemotherapy, her serum AFP had decreased to 266.4 ng/ml, and her CSF AFP level was < 0.5 ng/ml by May 2021. C An 11-year-old male who had been experiencing weakness in one limb for 11 months presented with a serum AFP level of 1.53 ng/ml and a CSF AFP level of < 0.5 ng/ml in February 2019. After four cycles of chemotherapy, his serum AFP level was increased to 64.2 ng/ml by June 2019