高级胶质瘤（HGG）与转移瘤之间的鉴别在常规放射学实践中仍然具有挑战性。我们比较了基于自然语言处理（NLP）的深度学习模型，以帮助放射科医生进行基于放射学报告中的数据的比较。本回顾性研究包括了来自两个不同机构的2010年至2022年之间的185份MRI报告。共有117份报告用于训练，21份作为验证集，其余报告用作测试集。我们进行了不同深度学习模型在HGG和转移瘤分类方面的性能比较。具体而言，我们使用了卷积神经网络（CNN）、双向长短期记忆（BiLSTM）模型、BiLSTM和CNN的混合版本以及放射学特定的双向编码器表示来自Transformers（RadBERT）模型。 对于MRI报告的分类，CNN网络在所有测试中提供了最佳结果，显示出87.32％的宏平均精确度、87.45％的敏感性和87.23％的F1得分。此外，我们的NLP算法检测到了关键词，如肿瘤、颞叶等，以对放射学报告进行正面分类，归为HGG或转移瘤组。 基于CNN的深度学习模型使放射科医生能够根据MRI报告区分HGG和转移瘤，具有高精度值。这种方法应被视为诊断这些中枢神经系统病变的附加工具。 我们的NLP模型的使用使放射科医生能够根据MRI报告区分高级胶质瘤和转移瘤的患者，并可以作为传统基于图像的方法的附加工具，用于这一具有挑战性的任务。 - 在常规放射学实践中，高级胶质瘤和转移瘤之间的鉴别依然存在挑战。 - 基于自然语言处理（NLP）的深度学习模型可以帮助放射科医生根据放射学报告中的数据进行鉴别。 - 我们开发和测试了一个基于MRI报告的自然语言处理模型，用于区分高级胶质瘤和转移瘤，表现出较高的精确度。© 2023作者，独家授权给欧洲放射学会。

The differential between high-grade glioma (HGG) and metastasis remains challenging in common radiological practice. We compare different natural language processing (NLP)-based deep learning models to assist radiologists based on data contained in radiology reports.This retrospective study included 185 MRI reports between 2010 and 2022 from two different institutions. A total of 117 reports were used for the training and 21 were reserved for the validation set, while the rest were used as a test set. A comparison of the performance of different deep learning models for HGG and metastasis classification has been carried out. Specifically, Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), a hybrid version of BiLSTM and CNN, and a radiology-specific Bidirectional Encoder Representations from Transformers (RadBERT) model were used.For the classification of MRI reports, the CNN network provided the best results among all tested, showing a macro-avg precision of 87.32%, a sensitivity of 87.45%, and an F1 score of 87.23%. In addition, our NLP algorithm detected keywords such as tumor, temporal, and lobe to positively classify a radiological report as HGG or metastasis group.A deep learning model based on CNN enables radiologists to discriminate between HGG and metastasis based on MRI reports with high-precision values. This approach should be considered an additional tool in diagnosing these central nervous system lesions.The use of our NLP model enables radiologists to differentiate between patients with high-grade glioma and metastasis based on their MRI reports and can be used as an additional tool to the conventional image-based approach for this challenging task.• Differential between high-grade glioma and metastasis is still challenging in common radiological practice. • Natural language processing (NLP)-based deep learning models can assist radiologists based on data contained in radiology reports. • We have developed and tested a natural language processing model for discriminating between high-grade glioma and metastasis based on MRI reports that show high precision for this task.© 2023. The Author(s), under exclusive licence to European Society of Radiology.