Automating Lung-RADS Categorization And Follow-Up Recommendations Using In-Context Learning With Large Language Models

Abstract

Lung cancer remains a significant challenge in public health, ranking among the leading causes of cancer-related mortality. Low-dose computed tomography (LDCT) -based lung cancer screening has emerged as an effective tool for early detection, particularly in high-risk populations. However, interpreting lung nodule characteristics from radiology reports can often be time-consuming and labor-intensive due to the length and inherent ambiguity of the reports, even with standardized reporting requirements like Lung-RADS. Generating Lung-RADS assessments from original radiology reports is a significant task for radiologists. This study addresses these challenges by developing an in-context learning framework utilizing large language models (LLMs). In this process, we aimed to identify the best approach that accurately categorizes lung nodules and streamlines management decisions, providing robust and interpretable decision support. Overall, this research aims to reduce the time and effort of the radiologist in lung cancer screening, ultimately enhancing efficiency and accuracy and enabling timely and precise interventions.