为了评估自然语言处理（NLP）模型在常规磁共振放射学报告中预测弥漫性胶质瘤中异柠檬酸脱氢酶（IDH）突变状态的性能。本回顾性、多中心研究纳入了2009年5月至2021年11月之间有已知IDH突变状态的弥漫性胶质瘤病人，他们的病理诊断之前有初始磁共振放射学报告。训练了五个NLP模型（长短期记忆网络[LSTM]、双向LSTM、双向编码器表示来自Transformer的[BERT]、BERT图卷积网络[GCN]、BioBERT），并评估了受试者工作特征曲线下面积（AUC）以验证在内部和外部验证集中预测IDH突变状态的性能。将表现最佳的NLP模型与人工读者的表现进行了比较。共纳入了1427名患者（平均年龄±标准差：54±15岁；男性779例，54.6%），其中训练组有720例患者，内部验证集有180例患者，外部验证集有527例患者。在外部验证集中，BERT GCN显示出最高的性能（AUC = 0.85，95%置信区间0.81-0.89）来预测IDH突变状态，高于LSTM（AUC = 0.77，95%置信区间0.72-0.81; p = 0.003）和BioBERT（AUC = 0.81，95%置信区间0.76-0.85; p = 0.03）。这也比神经放射医师（AUC = 0.80，95%置信区间0.76-0.84; p = 0.005）和神经外科医生（AUC = 0.79，95%置信区间0.76-0.84; p = 0.04）的表现更好。BERT GCN通过外部验证验证了使用常规磁共振放射学报告预测弥漫性胶质瘤中IDH突变状态，其性能优于或至少与人工读者相当。自然语言处理可以从常规放射学报告中提取相关信息，预测癌症基因型，并提供可进行治疗策略指导和实现个体化医学的预后信息。• 基于Transformer的自然语言处理（NLP）模型在外部验证集中预测弥漫性胶质瘤中异柠檬酸脱氢酶的突变状态，AUC为0.85。• 在内部和外部验证集中，最佳NLP模型优于或至少与人工读者相当。• 基于Transformer的模型表现优于传统的NLP模型，如长短期记忆。© 2023. 作者以独家许可授予欧洲放射学学会。

To evaluate the performance of natural language processing (NLP) models to predict isocitrate dehydrogenase (IDH) mutation status in diffuse glioma using routine MR radiology reports.This retrospective, multi-center study included consecutive patients with diffuse glioma with known IDH mutation status from May 2009 to November 2021 whose initial MR radiology report was available prior to pathologic diagnosis. Five NLP models (long short-term memory [LSTM], bidirectional LSTM, bidirectional encoder representations from transformers [BERT], BERT graph convolutional network [GCN], BioBERT) were trained, and area under the receiver operating characteristic curve (AUC) was assessed to validate prediction of IDH mutation status in the internal and external validation sets. The performance of the best performing NLP model was compared with that of the human readers.A total of 1427 patients (mean age ± standard deviation, 54 ± 15; 779 men, 54.6%) with 720 patients in the training set, 180 patients in the internal validation set, and 527 patients in the external validation set were included. In the external validation set, BERT GCN showed the highest performance (AUC 0.85, 95% CI 0.81-0.89) in predicting IDH mutation status, which was higher than LSTM (AUC 0.77, 95% CI 0.72-0.81; p = .003) and BioBERT (AUC 0.81, 95% CI 0.76-0.85; p = .03). This was higher than that of a neuroradiologist (AUC 0.80, 95% CI 0.76-0.84; p = .005) and a neurosurgeon (AUC 0.79, 95% CI 0.76-0.84; p = .04).BERT GCN was externally validated to predict IDH mutation status in patients with diffuse glioma using routine MR radiology reports with superior or at least comparable performance to human reader.Natural language processing may be used to extract relevant information from routine radiology reports to predict cancer genotype and provide prognostic information that may aid in guiding treatment strategy and enabling personalized medicine.• A transformer-based natural language processing (NLP) model predicted isocitrate dehydrogenase mutation status in diffuse glioma with an AUC of 0.85 in the external validation set. • The best NLP models were superior or at least comparable to human readers in both internal and external validation sets. • Transformer-based models showed higher performance than conventional NLP model such as long short-term memory.© 2023. The Author(s), under exclusive licence to European Society of Radiology.