Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large Language Models

Abstract

BACKGROUND: Cancer registries require accurate and efficient documentation of malignancies, yet current manual methods are time-consuming and error-prone. OBJECTIVES: This study evaluates the effectiveness of large language models (LLMs) in classifying malignancies and detecting tumor types from pathology reports. METHODS: Using a synthetic dataset of 227 reports, the performance of four LLMs and a score-based algorithm was compared against expert-labeled gold standards. RESULTS: The LLMs, particularly GPT-4o and Llama3.3, demonstrated high sensitivity and specificity in both malignancy detection and tumor classification, significantly outperforming traditional algorithms. CONCLUSION: LLMs enhance the accuracy and efficiency of cancer data classification and hold promise for improving public health monitoring and clinical decision-making.

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