Research Area:  Machine Learning

Abstract:

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.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.

Keywords:  

Author(s) Name:  Teodoro Martin-Noguerol, Pilar Lopez-Ubeda, Albert Pons-Escoda, Antonio Luna

Publisher name:  Springer

DOI:  10.1007/s00330-023-10202-4

Volume Information:  Volume 34, pages 2113-2120, (2024)

Paper Link:   https://link.springer.com/article/10.1007/s00330-023-10202-4