A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images

 

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dc.contributor.author Malherbe, Stephanus T
dc.contributor.author Dupont, Patrick
dc.contributor.author Kant, Ilse
dc.contributor.author Ahlers, Petri
dc.contributor.author Kriel, Magdalena
dc.contributor.author Loxton, André G
dc.contributor.author Chen, Ray Y
dc.contributor.author Via, Laura E
dc.contributor.author Thienemann, Friedrich
dc.contributor.author Wilkinson, Robert J
dc.contributor.author Barry, Clifton E
dc.contributor.author Griffith-Richards, Stephanie
dc.contributor.author Ellman, Annare
dc.contributor.author Ronacher, Katharina
dc.contributor.author Winter, Jill
dc.contributor.author Walzl, Gerhard
dc.contributor.author Warwick, James M
dc.date.accessioned 2018-07-12T06:52:50Z
dc.date.available 2018-07-12T06:52:50Z
dc.date.issued 2018-06-25
dc.identifier.citation EJNMMI Research. 2018 Jun 25;8(1):55
dc.identifier.uri https://doi.org/10.1186/s13550-018-0411-7
dc.identifier.uri http://hdl.handle.net/11427/28298
dc.description.abstract Background There is a growing interest in the use of 18F-FDG PET-CT to monitor tuberculosis (TB) treatment response. However, TB causes complex and widespread pathology, which is challenging to segment and quantify in a reproducible manner. To address this, we developed a technique to standardise uptake (Z-score), segment and quantify tuberculous lung lesions on PET and CT concurrently, in order to track changes over time. We used open source tools and created a MATLAB script. The technique was optimised on a training set of five pulmonary tuberculosis (PTB) cases after standard TB therapy and 15 control patients with lesion-free lungs. Results We compared the proposed method to a fixed threshold (SUV > 1) and manual segmentation by two readers and piloted the technique successfully on scans of five control patients and five PTB cases (four cured and one failed treatment case), at diagnosis and after 1 and 6 months of treatment. There was a better correlation between the Z-score-based segmentation and manual segmentation than SUV > 1 and manual segmentation in terms of overall spatial overlap (measured in Dice similarity coefficient) and specificity (1 minus false positive volume fraction). However, SUV > 1 segmentation appeared more sensitive. Both the Z-score and SUV > 1 showed very low variability when measuring change over time. In addition, total glycolytic activity, calculated using segmentation by Z-score and lesion-to-background ratio, correlated well with traditional total glycolytic activity calculations. The technique quantified various PET and CT parameters, including the total glycolytic activity index, metabolic lesion volume, lesion volumes at different CT densities and combined PET and CT parameters. The quantified metrics showed a marked decrease in the cured cases, with changes already apparent at month one, but remained largely unchanged in the failed treatment case. Conclusions Our technique is promising to segment and quantify the lung scans of pulmonary tuberculosis patients in a semi-automatic manner, appropriate for measuring treatment response. Further validation is required in larger cohorts.
dc.language.iso en
dc.publisher Springer Berlin Heidelberg
dc.source EJNMMI Research
dc.source.uri https://ejnmmires.springeropen.com/
dc.subject.other 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography
dc.subject.other Tuberculosis
dc.subject.other Image analysis
dc.subject.other Lesion segmentation
dc.subject.other Lesion quantification
dc.title A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images
dc.type Journal Article
dc.date.updated 2018-07-01T04:34:08Z
dc.rights.holder The Author(s).
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Health Sciences en_ZA
dc.publisher.department Institute of Infectious Disease and Molecular Medicine en_ZA
uct.type.filetype Text
uct.type.filetype Image


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