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

Jaime Collado-Montañez, Pilar López-Úbeda, Mariia Chizhikova, Manuel Carlos Díaz-Galiano, Luis Alfonso Ureña López, Teodoro Martín-Noguerol, Antonio Luna, María Teresa Martín Valdivia

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.