RESUMEN
Medical imaging is critical to the diagnosis and treatment of numerous medical problems, including many forms of cancer. Medical imaging reports distill the findings and observations of radiologists, creating an unstructured textual representation of unstructured medical images. Large-scale use of this text-encoded information requires converting the unstructured text to a structured, semantic representation. We explore the extraction and normalization of anatomical information in radiology reports that is associated with radiological findings. We investigate this extraction and normalization task using a span-based relation extraction model that jointly extracts entities and relations using BERT. This work examines the factors that influence extraction and normalization performance, including the body part/organ system, frequency of occurrence, span length, and span diversity. It discusses approaches for improving performance and creating high-quality semantic representations of radiological phenomena.
RESUMEN
We report the case history and radiologic findings of a patient with a biopsy-proven dendritic cell histiocytoma presenting as a single intracranial extra-axial mass and no systemic disease. Even though this entity is relatively rare, it should nevertheless be considered in the differential diagnosis of dural-based space-occupying central nervous system lesions.