Investigating radiographers’ socio-demographics in relation to the detection rate of incidental adrenal nodules seen on Computed Tomography (CT) scans

Abstract

Purpose: The aim of this research was to locally investigate whether radiographers’ sociodemographic characteristics affect the detection rate of incidental adrenal nodules, also known as adrenal incidentalomas (AIs). Objectives: To retrospectively determine the monthly rate of recalled CT scans due to an AI finding (Phase 1). To prospectively correlate the radiographers’ socio-demographics to the detection rate of AIs, and to assess their self-rated confidence and opinions for further training in this area (Phase 2). Methodology: The research consisted of 2 phases. In both phases, a non-experimental, crosssectional approach was employed. Phase 1 (Quantitative) comprised a data collection sheet used to retrospectively determine the occurrence of recalled CT scans as a result of an AI finding. In phase 2, (Quantitative with an element of qualitative) a structured questionnaire including anonymised CT scans (n=30) presented on Viewdex, was completed by the radiographers working in CT and PET/CT (n=23). Both research tools were self-designed by the researcher, to suit the aim and objectives of the study. Results: Phase 1 of the study determined that AIs were present in 1.4% of contrast-enhanced CT (CECT) scans (n=12,139), out of which, 79.82% were not acknowledged by the radiographers and had to be recalled for a dedicated adrenal CT scan. In phase 2, a statistical significant correlation (p<0.05) between the radiographers’ educational level, roster status, workplace and years of experience, to the detection rate of AIs was found. When asked to self-rate their confidence, the majority of participants (52.2%) were fairly confident. In addition, most (73.9%) favoured the need for more training, with continuous professional development (CPD) being the preferred approach. Conclusions: Findings suggest that certain socio-demographics of radiographers may affect their ability to recognise AIs. Therefore, this can potentially contribute to one of the reasons for recalling patients, in turn causing an added burden to both the patient and the Medical Imaging Department (MID).