Automating Precision Oncology Literature Curation: A Decision Tree Approach Using Large Language Models

The curation of precision oncology knowledge bases requires intensive screening of scientific literature. Large Language Models (LLMs) offer potential to assist with knowledge extraction and curation through automated abstract screening, though their utility in this context remains unexplored. We developed a decision tree-based pipeline utilising LLMs to automate literature screening for precision oncology knowledge bases. The system employs structured prompts to evaluate biomarker-therapeutic relationships across 8,012 abstracts from 25 oncology journals published in 2024. Performance was assessed against expert review using standard evaluation metrics; the pipeline achieved an overall accuracy of 92.9% (sensitivity [recall] 87.1%, specificity 93.7%) in shortlisting 1,328 candidate abstracts, of which 888 are considered relevant by experts, delivering a 5.26-fold enrichment in classification precision. Analysis of correctly identified abstracts revealed comprehensive coverage of key molecular targets, drug classes (immunotherapies and targeted therapies), and relevant cancer types. This pipeline automates literature triage for precision oncology knowledge bases for relevance, maintaining high specificity and potentially reducing manual screening burden whilst offering a scalable framework for sustainable knowledge base maintenance.