ONTOLOGIES FOR ONCOLOGICAL RADIOLOGY: CHALLENGES AND OPPORTUNITIES

Medical imaging examinations, especially Magnetic Resonance Imaging (MRI), interpreted by radiologists in the form of narrative reports, are used to produce and confirm diagnoses in clinical practice at different levels. Being able to accurately and quickly identify the information scattered in the radiologists’ narratives has the potential to reduce workloads, support clinicians in their decision processes, triage patients to get urgent care or identify and cluster patients for research purposes. This is especially critical within the context of the Tumor Boards, multidisciplinary groups made up of different specialists, who regularly meet to discuss oncological patients potentially needing pre/post-surgery treatments and to make diagnostic and therapeutic decisions for them. Nowadays, it is still difficult to access and analyze radiology reports both effectively and efficiently at scale, due to their unstructured nature, the conciseness and often crypticity of the medical jargon used, and the background knowledge usually required for interpreting them correctly and making high-level correlations and conclusions. Privacy concerns introduce further difficulties and impose the adoption of local tools rather than cloud-based ones, hence preventing the use of popular LLMs. Ontologies represent an important tool for easing the automatic process of medical records and radiology reports. Indeed, they can be used to add structure to otherwise unstructured texts; furthermore, they play a key role in data exchange and sharing, as they enable semantic interoperability. In this review paper we will introduce the problems related to the use of ontologies in the medical domain, with particular emphasis on radiology reports, and discuss the prominent advantages that a systematic use of ontologies would generate.