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Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large
Sabrina B Neururer1,2, Hasan Taha1,2, Helmut Muehlboeck1
1Department of Clinical Epidemiology, Tirol Kliniken GmbH, Innsbruck, Tirol, Austria.
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.
