Natural language processing deep learning models for the differential between high-grade gliomas and metastasis: what if the key is how we report them?

• Published in: European radiology 2024-03, Vol.34 (3), p.2113-2120
• Main Authors: Martín-Noguerol, Teodoro, López-Úbeda, Pilar, Pons-Escoda, Albert, Luna, Antonio
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Central nervous system; Classification; Deep Learning; Diagnostic Radiology; Glioma; Glioma - diagnostic imaging; Humans; Imaging; Imaging Informatics and Artificial Intelligence; Internal Medicine; Interventional Radiology; Language; Long short-term memory; Machine learning; Magnetic resonance imaging; Medicine; Medicine & Public Health; Metastases; Metastasis; Natural Language Processing; Neural networks; Neural Networks, Computer; Neuroradiology; Radiology; Retrospective Studies; Ultrasound
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-023-10202-4

Objectives 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. Methods 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. Results 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. Conclusions 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. Clinical relevance statement 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. Key Points • 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.