Evaluating Large Language Models for Zero-Shot Disease Labeling in CT Radiology Reports Across Organ Systems

• URL: http://arxiv.org/abs/2506.03259v1
• Date: Tue, 03 Jun 2025 18:00:08 GMT
• Title: Evaluating Large Language Models for Zero-Shot Disease Labeling in CT Radiology Reports Across Organ Systems
• Authors: Michael E. Garcia-Alcoser, Mobina GhojoghNejad, Fakrul Islam Tushar, David Kim, Kyle J. Lafata, Geoffrey D. Rubin, Joseph Y. Lo,
• Abstract summary: We compare a rule-based algorithm (RBA), RadBERT, and three lightweight open-weight LLMs for multi-disease labeling of chest, abdomen, and pelvis CT reports.<n>Performance was evaluated using Cohen's Kappa and micro/macro-averaged F1 scores.
• Score: 1.1373722549440357
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Purpose: This study aims to evaluate the effectiveness of large language models (LLMs) in automating disease annotation of CT radiology reports. We compare a rule-based algorithm (RBA), RadBERT, and three lightweight open-weight LLMs for multi-disease labeling of chest, abdomen, and pelvis (CAP) CT reports. Materials and Methods: This retrospective study analyzed 40,833 CT reports from 29,540 patients, with 1,789 CAP reports manually annotated across three organ systems. External validation was conducted using the CT-RATE dataset. Three open-weight LLMs were tested with zero-shot prompting. Performance was evaluated using Cohen's Kappa and micro/macro-averaged F1 scores. Results: In 12,197 Duke CAP reports from 8,854 patients, Llama-3.1 8B and Gemma-3 27B showed the highest agreement ($\kappa$ median: 0.87). On the manually annotated set, Gemma-3 27B achieved the top macro-F1 (0.82), followed by Llama-3.1 8B (0.79), while the RBA scored lowest (0.64). On the CT-RATE dataset (lungs/pleura only), Llama-3.1 8B performed best (0.91), with Gemma-3 27B close behind (0.89). Performance differences were mainly due to differing labeling practices, especially for lung atelectasis. Conclusion: Lightweight LLMs outperform rule-based methods for CT report annotation and generalize across organ systems with zero-shot prompting. However, binary labels alone cannot capture the full nuance of report language. LLMs can provide a flexible, efficient solution aligned with clinical judgment and user needs.

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