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