Abstract

To evaluate whether incorporating CT perfusion imaging can significantly enhance diagnostic CT accuracy in stroke detection. Two 3rd-year residents (3rd of 5 years of residency) reviewed CT scans of 200 patients with suspected stroke, consisting of 104 patients with a proven stroke and a control group with 96 patients. They analyzed each patient in a blinded and randomized manner in two runs. In one session, they had only non-contrast CT and CT angiography available for diagnosis; in the other session at a later time point, an additional CT perfusion imaging was available. The performance achieved by the two readers was determined in terms of AUC (area under the curve), accuracy, sensitivity, specificity, positive and negative predictive value and Cohen’s Kappa. Reader 1 achieved an AUC of 87.64% with the basic stroke-protocol vs. an AUC of 97.4% with an additional CT-perfusion given. Based on the DeLong test, these values differ significantly (p-value: 0.00017). Reader 2 achieved an AUC of 91.23% in basic stroke-protocol vs. an AUC of 96.42% with an additional CT-perfusion. These values also differ significantly (p-value: 0.02612).. The performance gain achieved with CT-perfusion is most evident in the decrease in the number of false classified cases (Reader 1: 24 to 5; Reader 2: 18 or 14 to 7) and the significant increase in Cohen’s kappa. Our study shows that additional CT-perfusion imaging in stroke diagnosis significantly improves the diagnostic reliability of residents. Therefore, it should be further investigated whether perfusion imaging should be a general standard of initial stroke diagnosis no matter of the onset.

Details

Title
Diagnostic value of routine CT perfusion imaging for radiology residents
Author
Nicolas, Philip M. 1 ; Maksoud, Ziad 1 ; Nacul, Nabila Gala 1 ; Akkurt, Burak Han 1 ; Mannil, Manoj 1 ; Musigmann, Manfred 1 

 University of Münster, University Clinic of Radiology, University Hospital Münster, Münster, Germany (GRID:grid.5949.1) (ISNI:0000 0001 2172 9288) 
Pages
25093
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3119848868
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.