Analysis of a multicentre cloud-based CT dosimetric database: preliminary results

• Published in: European radiology experimental 2019-07, Vol.3 (1), p.27-13, Article 27
• Main Authors: Calderoni, Francesca, Campanaro, Federica, Colombo, Paola Enrica, Campoleoni, Mauro, De Mattia, Cristina, Rottoli, Federica, Galetta, Giannicola, Zucconi, Fabio, Pola, Andrea, Righini, Andrea, Triulzi, Fabio, Vanzulli, Angelo, Torresin, Alberto
• Format: Article
• Language: English
• Subjects: Diagnostic Radiology; Imaging; Internal Medicine; Interventional Radiology; Medicine; Medicine & Public Health; Neuroradiology; Original; Original Article; Radiation dosage; Radiation dose index monitoring software; Radiation exposure; Radiology; Software; Tomography scanners (x-ray computed); Ultrasound
• ISSN: 2509-9280; 2509-9280
• DOI: 10.1186/s41747-019-0105-6

Background To manage and analyse dosimetric data provided by computed tomography (CT) scanners from four Italian hospitals. Methods A radiation dose index monitoring (RDIM) software was used to collect anonymised exams stored in a cloud server. Since hospitals use different names for the same procedure, digital imaging and communications in medicine (DICOM) tags more appropriate to describe exams were selected and associated to study common names (SCNs) from a radiology playbook according to scan region and use of contrast media. Retrospective analysis was carried out to describe population and to evaluate dosimetric indexes and inaccuracies associated with SCNs. Results More than 400 procedures were clustered into 95 SCNs, but 78% of exams on adults were described with only 10 SCNs. Median values of dose-length product (DLP) and volumetric CT dose index (CTDI vol ) for three analysed SCNs were in agreement with those previously published. The percentage of inaccuracies does not heavily affect the dosimetric analysis on the whole cloud, since variations in median values reached at most 8%. Conclusions Implementation of a cloud-based RDIM software and related issues were described, showing the strength of the chosen playbook-based clustering and its usefulness for homogeneous data analysis. This approach may allow for optimisation actions, accurate assessment of the risk associated with radiation exposure, comparison of different facilities, and, last but not least, collection of information for the implementation of the 2013/59 Euratom Directive.