La Radiologia medica · 2025
Dual-task deep learning model for prediction of medulloblastoma molecular subgroups with preoperative brain MRI.
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Luo Lingxiao, An Xu, Zhang Jing, Zhou Wentao, Zhao Xiaobin, Zhao Hanguang, Tian Yongji, Chen Ting, Zhao Fu
Аннотация
PURPOSE: To develop a deep learning model for predicting molecular subgroups of medulloblastoma (MB) using preoperative brain MRI.
MATERIALS AND METHODS: This study included a cohort of 350 patients with MB for model development. Preoperative multiparametric brain MRIs were acquired, and molecular classification data for tumor samples were analyzed. A dual-task deep learning model, composed of a 3D Swin Transformer backbone and a Transformer-based mask decoder, was developed for the prediction of MB molecular subgroups. The model was jointly optimized with a parallel task of tumor and cerebellum segmentation. Ablation analysis was conducted to verify the effectiveness of the dual-task model design. An independent test cohort of 126 patients with MB was established to validate the predictive performance of the dual-task model.
RESULTS: Our dual-task deep learning model demonstrated superior performance for MB molecular subgroup prediction, achieving an AUC of 0.877, accuracy of 88.9%, sensitivity of 71.6%, and specificity of 91.9%. The performance remained robust across both adult and pediatric age populations, with AUCs of 0.915 and 0.871, respectively. Furthermore, our approach exhibited effective generalization to the independent test cohort, yielding an AUC of 0.853, accuracy of 89.7%, sensitivity of 73.5%, and specificity of 92.1%. Ablation analysis demonstrated a significant improvement in AUC of 0.169 (95% CI 0.097-0.244) when using the dual-task model design. In comparison with the radiomics-based model, our deep learning model achieved a higher AUC by 0.156 (95% CI 0.079-0.233).
CONCLUSION: Our proposed dual-task deep learning model enables automated and accurate prediction of MB molecular subgroups.
MATERIALS AND METHODS: This study included a cohort of 350 patients with MB for model development. Preoperative multiparametric brain MRIs were acquired, and molecular classification data for tumor samples were analyzed. A dual-task deep learning model, composed of a 3D Swin Transformer backbone and a Transformer-based mask decoder, was developed for the prediction of MB molecular subgroups. The model was jointly optimized with a parallel task of tumor and cerebellum segmentation. Ablation analysis was conducted to verify the effectiveness of the dual-task model design. An independent test cohort of 126 patients with MB was established to validate the predictive performance of the dual-task model.
RESULTS: Our dual-task deep learning model demonstrated superior performance for MB molecular subgroup prediction, achieving an AUC of 0.877, accuracy of 88.9%, sensitivity of 71.6%, and specificity of 91.9%. The performance remained robust across both adult and pediatric age populations, with AUCs of 0.915 and 0.871, respectively. Furthermore, our approach exhibited effective generalization to the independent test cohort, yielding an AUC of 0.853, accuracy of 89.7%, sensitivity of 73.5%, and specificity of 92.1%. Ablation analysis demonstrated a significant improvement in AUC of 0.169 (95% CI 0.097-0.244) when using the dual-task model design. In comparison with the radiomics-based model, our deep learning model achieved a higher AUC by 0.156 (95% CI 0.079-0.233).
CONCLUSION: Our proposed dual-task deep learning model enables automated and accurate prediction of MB molecular subgroups.