Is hybrid SPECT/CT necessary for pre-interventional 3D quantification of relative lobar lung function? View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Daniela Knollmann, Jerome Avondo, Wolfgang M. Schaefer

ABSTRACT

In pulmonary malignancies pre-interventional 3D estimation of relative lobar perfusion is established to predict post-interventional functional outcome particularly in patients with borderline lung function. Aim was to test whether quantification from SPECT-scanners (non-hybrid) is as accurate as from SPECT/CT-scanners (hybrid) when using dedicated software. Sixty-one patients suffering from pulmonary tumours underwent lung SPECT/CT using Tc-99m MAA to predict postoperative residual lung function prior to surgical treatment. Quantification was done using “HERMES Hybrid 3D–Lung Lobe Quantification”. In the hybrid approach SPECT and combined lowdoseCT/diagnosticCT were used. In the non-hybrid approach SPECT and diagnosticCTs were used, lowdoseCTs were omitted. Bland Altman analysis was done to test for agreement. Three hundred five lobes were quantified. Evaluation time was 6:37 ± 0.55 min (hybrid) versus 6:34 ± 0.51 min (non-hybrid). Mean lobar value was 20.0 ± 10.5% (range from 0 to 55%) for hybrid and 20.0 ± 10.6% (range from 0 to 58%) for the non-hybrid approach, mean absolute difference was 1.31%, no significant differences were found when analysing all values (p > 0.9). Correlation was excellent (R = 0.984, slope of the regression line 1.001 (p < 0.0001)). Intraclass correlation coefficient was 0.9843. Bland Altman limits were -3.67% and 3.67%. Excellent concordance was found for 3D-quantification of relative lung perfusion when comparing a hybrid vs. non-hybrid approach. Using sophisticated software combining the generally available diagnosticCT and conventional SPECT-data reliable results for lobar perfusion can be obtained without the need for costly investment of SPECT/CT systems for this clinical question. More... »

PAGES

18

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s41824-018-0036-0

DOI

http://dx.doi.org/10.1186/s41824-018-0036-0

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https://app.dimensions.ai/details/publication/pub.1105158606


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