Towards Automating Lung-RADS Classification: Insights from Portuguese Radiology Reports - Ferreira, Tarcísio Lima; Oliveira, Marcelo Costa; Petruceli, Juliana Simon (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à Saúde, 2026)
Early lung cancer detection requires efficient nodule classification. This study compares automated strategies for assigning Lung-RADS categories to Portuguese CT reports using DL and LLMs. Analyzing 963 reports, we employed NER (BiLSTM-CRF, BioBERTpt) and QA (GPT-4o, Gemini 1.5 Flash, Llama 3 70B) with prompt engineering. Gemini 1.5 Flash achieved the highest evaluation metrics (macro-F1: 0.58; weighted-F1: 0.67). Results demonstrate a scalable, adaptable pathway for automating radiology workflows in non-English clinical settings, enhancing diagnostic efficiency through structured NLP post-processing.