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The Imaging Data Commons (IDC) is a cloud-based database that providesresearchers with open access to cancer imaging data, with the goal offacilitating collaboration. However, cohort discovery within the IDC databasehas a significant technical learning curve. Recently, large language models(LLM) have demonstrated exceptional utility for natural language processingtasks. We developed Text2Cohort, a LLM-powered toolkit to facilitateuser-friendly natural language cohort discovery in the IDC. Our methodtranslates user input into IDC queries using grounding techniques and returnsthe query's response. We evaluate Text2Cohort on 50 natural language inputs,from information extraction to cohort discovery. Our toolkit successfullygenerated responses with an 88% accuracy and 0.94 F1 score. We demonstrate thatText2Cohort can enable researchers to discover and curate cohorts on IDC withhigh levels of accuracy using natural language in a more intuitive anduser-friendly way.

Text2Cohort: Facilitating Intuitive Access to Biomedical Data with Natural Language Cohort Discovery