现有的脑分割模型应进一步优化，以为肿瘤学分析提供更多信息。我们旨在开发一个基于nnU-Net的深度学习模型，用于自动提取带有脑肿瘤的对比增强T1加权（T1CE）图像中的大脑。这是一项多中心、回顾性研究，涉及920名患者。从私人机构收集了720例包括四种类型的颅内肿瘤，并将其分为训练组和内部测试组。使用Mann-Whitney U检验（U检验）探讨了模型性能与病理类型和肿瘤特征是否相关。然后，模型的泛化性能在包括100例胶质瘤和100例前庭神经鞘瘤的公共数据集上进行了独立测试。在内部测试中，该模型取得了令人满意的性能，Dice相似性系数（DSC）的中位数为0.989（四分位数范围（IQR）为0.988-0.991），Hausdorff距离（HD）为6.403毫米（IQR为5.099-8.426毫米）。U检验结果显示髓膜瘤和前庭神经鞘瘤组的性能略有下降。U检验的结果还表明，在周围肿瘤水肿组中存在显著差异，DSC的中位数为0.990（IQR为0.989-0.991，p = 0.002），HD的中位数为5.916毫米（IQR为5.000-8.000毫米，p = 0.049）。在外部测试中，我们的模型也显示出稳健的性能，DSC的中位数为0.991（IQR为0.983-0.998），HD为8.972毫米（IQR为6.164-13.710毫米）。对于MRI神经影像数据中存在脑肿瘤的自动处理，所提出的模型可以执行包括重要表浅结构在内的脑分割，以进行肿瘤学分析。所提出的模型在肿瘤病例中作为图像预处理的放射学工具，专注于表浅脑结构，可以简化工作流程，并提高随后的放射学评估效率。• nnU-Net模型能够对脑分割中的重要表浅结构进行分割。 • 所提出的模型显示了可行的性能，无论病理类型或肿瘤特征如何。 • 该模型在公共数据集上显示了泛化性能。© 2023. 作者(们)。

Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors.This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases.In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm).For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis.The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments.• The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets.© 2023. The Author(s).