Abstract
BACKGROUND: Efficient exploration of knowledge for the treatment of recurrent glioblastoma (GBM) is critical for both clinicians and researchers. However, due to the large number of clinical trials and published articles, searching for this knowledge is very labor-intensive. In the current study, using natural language processing (NLP), we analyzed medical research corpora related to recurrent glioblastoma to find potential targets and treatments. METHODS: We fine-tuned the 'SAPBERT', which was pretrained on biomedical ontologies, to perform question/answering (QA) and name entity recognition (NER) tasks for medical corpora. The model was fine-tuned with the SQUAD2 dataset and multiple NER datasets designed for QA task and NER task, respectively. Corpora were collected by searching the terms "recurrent glioblastoma" and "drug target", published from 2000 to 2020 in the Web of science (N = 288 articles). Also, clinical trial corpora were collected from 'clinicaltrial.gov' using the searching term of 'recurrent glioblastoma" (N = 587 studies). RESULTS: For the QA task, the model showed an F1 score of 0.79. For the NER task, the model showed F1 scores of 0.90 and 0.76 for drug and gene name recognition, respectively. When asked what the molecular targets were promising for recurrent glioblastoma, the model answered that RTK inhibitors or LPA-1 antagonists were promising. From collected clinical trials, the model summarized them in the order of bevacizumab, temozolomide, lomustine, and nivolumab. Based on published articles, the model found the many drug-gene pairs with the NER task, and we presented them with a circus plot and related summarization ( https://github.com/bigwiz83/NLP_rGBM ). CONCLUSION: Using NLP deep learning models, we could explore potential targets and treatments based on medical research and clinical trial corpora. The knowledge found by the models may be used for treating recurrent glioblastoma.