AI-Driven Lung-RADS Classification on CT Reports

Lung cancer has the highest mortality rate among all cancer types, affecting both men and women. It is estimated that lung cancer accounts for 21 % of cancer deaths in each gender worldwide. In Brazil, lung cancer is the third most common type of cancer among men and the fourth most common among women. This alarming statistic highlights the significant impact of lung cancer on overall cancer mortality, underscoring the urgent need for effective prevention, early detection, and treatment strategies to combat this disease. The Lung-RADS is a standardized classification system for lung nodules detected in imaging exams and assesses the risk of malignancy (cancer) in these nodules to guide subsequent management decisions. In this context, the main goal of this work was to evaluate the effectiveness of using question-answering natural language processing using LLMs to extract lung nodule characteristics from Portuguese chest CT reports for automated Lung-RADS classification. Our most effective model was Llama 3.3708, and this model achieved a weighted F1 score of 84.77 %. Our findings underscore the potential of LLMs to support radiologists in accurately categorizing lung nodules according to Lung-RADS criteria, thereby simplifying the diagnostic process.