Work

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

Year: 2025

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250200

Authors: Sabrina B. Neururer, Hasan Taha, Helmut Muehlboeck, Christoph Hickmann, Patricia Gscheidlinger, Stefan Richter, Martin Danler, Werner O. Hackl, Marko Ueberegger, Marco Schweitzer, Bernhard Pfeifer

Institutions: UMIT TIROL - Private University for Health Sciences and Health Technology, Tirol Kliniken

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4409774204

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