Lesion detection on a combined “All-in-One” window compared to conventional window settings in thoracic oncology chest CT examinations - 14/12/19
, P. Vuylsteke b, B.J.G. Broeckx c, K. Carpentier a, R. Corthouts a, E.A. Luyckx a, S. Nicolay a, A.V. Hoyweghen a, M.J. Spinhoven a, J. Cant b, P.M. Parizel a
Benvenuto su EM|consulte, il riferimento dei professionisti della salute.
Articolo gratuito.
Si connetta per beneficiarne
Abstract |
Purpose |
The purpose of this study was to investigate if lesion detection using a single “All-in-One” (AIO) window was non-inferior to lesion detection on conventional window settings in thoracic oncology chest computed tomography (CT) examinations.
Materials and methods |
In a retrospective study, 50 consecutive chest CT examinations of 50 patients (31 men, 19 women; mean age 64±10 [SD] years, range: 35–82 years) containing 417 lesions, were reviewed by 6 radiologists, subdivided into 2 groups of 3 radiologists each, with similar levels of expertise in each group (senior staff member, junior staff member and radiology resident). All examinations were reviewed in conventional or AIO window settings by one of the groups. A ‘lesion’ was defined as any abnormality seen on the chest CT examination, including both benign and malignant lesions, findings in chest and upper abdomen, and measurable and non-measurable disease. Lesions were listed as ‘missed’ when they were not seen by at least two out of three observers. F-tests were used to evaluate the significance of the variables of interest within a mixed model framework and kappa statistics to report interobserver agreement.
Results |
On a reader level, 54/417 lesions (12.9%) were not detected by the senior staff member reading the studies in conventional window settings and 45/417 (10.8%) by the senior staff member reading the AIO images. For the junior staff member and radiology resident this was respectively 55/417 (13.2%) and 67/417 (16.1%) for the conventional window settings and 43/417 (10.3%) and 61/417 (14.6%) for the AIO window. On a lesion level, 68/417 (16.3%) were defined as ‘missed’ lesions (lesions not detected by at least 2 readers): 21/68 (30.9%) on the AIO-window, 30/68 (44.1%) on conventional views and 17/68 (25.0%) on both views. The use of the AIO window did not result in an increase of missed lesions (P>0.99). Interobserver agreement in both groups was similar (P=0.46). Regarding lesions that were categorized as ‘missed’ on the AIO window or on conventional window settings, there was no effect of location (chest or upper abdomen) (P=0.35), window (P=0.97) and organ (P=0.98).
Conclusions |
A single AIO-window is non-inferior to multiple conventional window settings for lesion detection on chest CT examinations in thoracic oncology patients.
Il testo completo di questo articolo è disponibile in PDF.Keywords : Diagnosis, Computed tomography (CT), Computer-assisted image processing, Computer-assisted image interpretation, Lung neoplasms
Mappa
Vol 101 - N° 1
P. 25-33 - Gennaio 2020 Ritorno al numero
Articolo precedente
- Endovascular stenting for chronic femoro-iliac venous obstructive disease: Clinical efficacy and short-term outcomes
- K. Guillen, N. Falvo, M. Nakai, O. Chevallier, S. Aho-Glélé, C. Galland, E. Demaistre, L. Pescatori, M. Samson, S. Audia, B. Bonnotte, M. Midulla, R. Loffroy
- Annotated normal CT data of the abdomen for deep learning: Challenges and strategies for implementation
- S. Park, L.C. Chu, E.K. Fishman, A.L. Yuille, B. Vogelstein, K.W. Kinzler, K.M. Horton, R.H. Hruban, E.S. Zinreich, D. Fadaei Fouladi, S. Shayesteh, J. Graves, S. Kawamoto
Benvenuto su EM|consulte, il riferimento dei professionisti della salute.