Automatic text classification of prostate cancer malignancy scores in radiology reports using NLP models

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2024年6月7日

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Jaime Collado-Montañez,
Pilar López-Úbeda,
Mariia Chizhikova,
M. Carlos Díaz-Galiano,
L. Alfonso Ureña-López,
Teodoro Martín-Noguerol,
Antonio Luna,
M. Teresa Martín-Valdivia

摘要

Abstract

This paper presents the implementation of two automated text classification systems for prostate cancer findings based on the PI-RADS criteria. Specifically, a traditional machine learning model using XGBoost and a language model-based approach using RoBERTa were employed. The study focused on Spanish-language radiological MRI prostate reports, which has not been explored before. The results demonstrate that the RoBERTa model outperforms the XGBoost model, although both achieve promising results. Furthermore, the best-performing system was integrated into the radiological company’s information systems as an API, operating in a real-world environment.

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