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Reshaping free-text radiology notes into structured reports with generative question answering transformers

2024; Elsevier BV; Volume: 154; Linguagem: Inglês

10.1016/j.artmed.2024.102924

1873-2860

Laura Bergomi, Tommaso Mario Buonocore, Paolo Antonazzo, Lorenzo Alberghi, Riccardo Bellazzi, Lorenzo Preda, Chandra Bortolotto, Enea Parimbelli,

Radiomics and Machine Learning in Medical Imaging

Radiology reports are typically written in a free-text format, making clinical information difficult to extract and use. Recently, the adoption of structured reporting (SR) has been recommended by various medical societies thanks to the advantages it offers, e.g. standardization, completeness, and information retrieval. We propose a pipeline to extract information from Italian free-text radiology reports that fits with the items of the reference SR registry proposed by a national society of interventional and medical radiology, focusing on CT staging of patients with lymphoma.