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.authorMalherbe, Stephanus T
dc.contributor.authorDupont, Patrick
dc.contributor.authorKant, Ilse
dc.contributor.authorAhlers, Petri
dc.contributor.authorKriel, Magdalena
dc.contributor.authorLoxton, André G
dc.contributor.authorChen, Ray Y
dc.contributor.authorVia, Laura E
dc.contributor.authorThienemann, Friedrich
dc.contributor.authorWilkinson, Robert J
dc.contributor.authorBarry, Clifton E
dc.contributor.authorGriffith-Richards, Stephanie
dc.contributor.authorEllman, Annare
dc.contributor.authorRonacher, Katharina
dc.contributor.authorWinter, Jill
dc.contributor.authorWalzl, Gerhard
dc.contributor.authorWarwick, James M
dc.date.accessioned2018-07-12T06:52:50Z
dc.date.available2018-07-12T06:52:50Z
dc.date.issued2018-06-25
dc.date.updated2018-07-01T04:34:08Z
dc.description.abstractBackground 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.identifier.apacitationMalherbe, S. T., Dupont, P., Kant, I., Ahlers, P., Kriel, M., Loxton, A. G., ... Warwick, J. M. (2018). A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images. <i>EJNMMI Research</i>, http://hdl.handle.net/11427/28298en_ZA
dc.identifier.chicagocitationMalherbe, Stephanus T, Patrick Dupont, Ilse Kant, Petri Ahlers, Magdalena Kriel, André G Loxton, Ray Y Chen, et al "A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images." <i>EJNMMI Research</i> (2018) http://hdl.handle.net/11427/28298en_ZA
dc.identifier.citationEJNMMI Research. 2018 Jun 25;8(1):55
dc.identifier.ris TY - Journal Article AU - Malherbe, Stephanus T AU - Dupont, Patrick AU - Kant, Ilse AU - Ahlers, Petri AU - Kriel, Magdalena AU - Loxton, André G AU - Chen, Ray Y AU - Via, Laura E AU - Thienemann, Friedrich AU - Wilkinson, Robert J AU - Barry, Clifton E AU - Griffith-Richards, Stephanie AU - Ellman, Annare AU - Ronacher, Katharina AU - Winter, Jill AU - Walzl, Gerhard AU - Warwick, James M AB - 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. DA - 2018-06-25 DB - OpenUCT DP - University of Cape Town J1 - EJNMMI Research LK - https://open.uct.ac.za PB - University of Cape Town PY - 2018 T1 - A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images TI - A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images UR - http://hdl.handle.net/11427/28298 ER - en_ZA
dc.identifier.urihttps://doi.org/10.1186/s13550-018-0411-7
dc.identifier.urihttp://hdl.handle.net/11427/28298
dc.identifier.vancouvercitationMalherbe ST, Dupont P, Kant I, Ahlers P, Kriel M, Loxton AG, et al. A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images. EJNMMI Research. 2018; http://hdl.handle.net/11427/28298.en_ZA
dc.language.isoen
dc.publisherSpringer Berlin Heidelberg
dc.publisher.departmentInstitute of Infectious Disease and Molecular Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rights.holderThe Author(s).
dc.sourceEJNMMI Research
dc.source.urihttps://ejnmmires.springeropen.com/
dc.subject.other18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography
dc.subject.otherTuberculosis
dc.subject.otherImage analysis
dc.subject.otherLesion segmentation
dc.subject.otherLesion quantification
dc.titleA semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images
dc.typeJournal Article
uct.type.filetypeText
uct.type.filetypeImage
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