Browsing by Author "Via, Laura E."

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    Bacterial loads measured by the Xpert MTB/RIF assay as markers of culture conversion and bacteriological cure in pulmonary TB
    (Public Library of Science, 2016) Shenai, Shubhada; Ronache, Katharina; Malherbe, Stefanus; Stanley, Kim; Kriel, Magdalena; Winter, Jill; Peppard, Thomas; Barry, Charles E.; Wang, Jing; Dodd, Lori E.; Via, Laura E.; Barry, Clifton E. 3rd; Walzl, Gerhard; Alland, David
    Introduction: Biomarkers are needed to monitor tuberculosis (TB) treatment and predict treatment outcomes. We evaluated the Xpert MTB/RIF (Xpert) assay as a biomarker for TB treatment during and at the end of the 24 weeks therapy. Methods: Sputum from 108 HIV-negative, culture-positive pulmonary TB patients was analyzed using Xpert at time points before and during anti-TB therapy. Results were compared against culture. Direct Xpert cycle-threshold (Ct), a change in the Ct (delta Ct), or a novel “percent closing of baseline Ct deficit” (percent closing) were evaluated as classifiers of same-day and end-of-treatment culture and therapeutic outcomes. Results: Xpert was positive in 29/95 (30.5%) of subjects at week 24; and positive one year after treatment in 8/64 (12.5%) successfully-treated patients who remained free of tuberculosis. We identified a relationship between initial bacterial load measured by baseline Xpert Ct and time to culture conversion (hazard ratio 1.06, p = 0.0023), and to the likelihood of being among the 8 treatment failures at week 24 (AUC = 72.8%). Xpert Ct was even more strongly associated with culture conversion on the day the test was performed with AUCs 96.7%, 99.2%, 86.0% and 90.2%, at Day 7, Week 4, 8 and 24, respectively. Compared to baseline Ct measures alone, a combined measure of baseline Ct plus either Delta Ct or percent closing improved the classification of treatment failure status to a 75% sensitivity and 88.9% specificity. Conclusions: Genome loads measured by Xpert provide a potentially-useful biomarker for classifying same day culture status and predicting response to therapy.
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    Quantitative 18F-FDG PET-CT scan characteristics correlate with tuberculosis treatment response
    (SpringerOpen (part of Springer Nature), 2020-02-10) Malherbe, Stephanus T.; Chen, Ray Y.; Dupont, Patrick; Kant, Ilse; Kriel, Magdalena; Loxton, Andre G.; Smith, Bronwyn; Beltran, Caroline G. G.; Van Zyl, Susan; McAnda, Shirely; Abrahams, Charmaine; Maasdorp, Elizna; Doruyter, Alex; Via, Laura E.; Barry, Clifton E.; Alland, David; Richards, Stephanie G.; Ellman, Annare; Peppard, Thomas; Belisle, John; Tromp, Gerard; Ronacher, Katharina; Warwick, James M.; Winter, Jill; Walzl, Gerhard
    Background: There is a growing interest in the use of F-18 FDG PET-CT to monitor tuberculosis (TB) treatment response. Tuberculosis lung lesions are often complex and diffuse, with dynamic changes during treatment and persisting metabolic activity after apparent clinical cure. This poses a challenge in quantifying scan-based markers of burden of disease and disease activity. We used semi-automated, whole lung quantification of lung lesions to analyse serial FDG PET-CT scans from the Catalysis TB Treatment Response Cohort to identify characteristics that best correlated with clinical and microbiological outcomes. Results: Quantified scan metrics were already associated with clinical outcomes at diagnosis and 1 month after treatment, with further improved accuracy to differentiate clinical outcomes after standard treatment duration (month 6). A high cavity volume showed the strongest association with a risk of treatment failure (AUC 0.81 to predict failure at diagnosis), while a suboptimal reduction of the total glycolytic activity in lung lesions during treatment had the strongest association with recurrent disease (AUC 0.8 to predict pooled unfavourable outcomes). During the first year after TB treatment lesion burden reduced; but for many patients, there were continued dynamic changes of individual lesions. Conclusions: Quantification of FDG PET-CT images better characterised TB treatment outcomes than qualitative scan patterns and robustly measured the burden of disease. In future, validated metrics may be used to stratify patients and help evaluate the effectiveness of TB treatment modalities.
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    Quantitative 18F-FDG PET-CT scan characteristics correlate with tuberculosis treatment response
    (SpringerOpen (part of Springer Nature), 2020) Malherbe, Stephanus T.; Chen, Ray Y.; Dupont, Patrick; Kant, Ilse; Kriel, Magdalena; Loxton, Andre G.; Smith, Bronwyn; Beltran, Caroline G. G.; Van Zyl, Susan; McAnda, Shirely; Abrahams, Charmaine; Maasdorp, Elizna; Doruyter, Alex; Via, Laura E.; Barry, Clifton E.; Alland, David; Griffith- Richards, Stephanie; Ellman, Annare; Peppard, Thomas; Belisle, John; Tromp, Gerard; Ronacher, Katharina; Warwick, James M.; Winter, Jill; Walzl, Gerhard
    Background: There is a growing interest in the use of F-18 FDG PET-CT to monitor tuberculosis (TB) treatment response. Tuberculosis lung lesions are often complex and diffuse, with dynamic changes during treatment and persisting metabolic activity after apparent clinical cure. This poses a challenge in quantifying scan-based markers of burden of disease and disease activity. We used semi-automated, whole lung quantification of lung lesions to analyse serial FDG PET-CT scans from the Catalysis TB Treatment Response Cohort to identify characteristics that best correlated with clinical and microbiological outcomes. Results: Quantified scan metrics were already associated with clinical outcomes at diagnosis and 1 month after treatment, with further improved accuracy to differentiate clinical outcomes after standard treatment duration (month 6). A high cavity volume showed the strongest association with a risk of treatment failure (AUC 0.81 to predict failure at diagnosis), while a suboptimal reduction of the total glycolytic activity in lung lesions during treatment had the strongest association with recurrent disease (AUC 0.8 to predict pooled unfavourable outcomes). During the first year after TB treatment lesion burden reduced; but for many patients, there were continued dynamic changes of individual lesions. Conclusions: Quantification of FDG PET-CT images better characterised TB treatment outcomes than qualitative scan patterns and robustly measured the burden of disease. In future, validated metrics may be used to stratify patients and help evaluate the effectiveness of TB treatment modalities.
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    A semi-automatic technique to quantify complex tuberculous lung lesions on 18F-fluorodeoxyglucose positron emission tomography/computerised tomography images
    (Springer Open, 2018-06-25) Malherbe, Stephanus T.; Dupont, Patrick; Kant, Ilse; Ahlers, Petri; Kriel, Magdalena; Loxton, Andre G.; Chen, Ray Y.; Via, Laura E.; Thienemann, Friedrich; Wilkinson, Robert J; Barry, Clifton E.; Griffith-Richards, Stephanie; Ellman, Annare; Ronacher, Katharina; Winter, Jill; Walzl, Gerhard; Warwick, James M.
    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.
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    Visualizing the dynamics of tuberculosis pathology using molecular imaging
    (American Society for Clinical Investigation, 2021-03) Ordonez, Alvaro A.; Tucker, Elizabeth W.; Anderson, Carolyn J.; Carter, Claire L.; Ganatra, Shashank; Kaushal, Deepak; Kramnik, Igor; Lin, Philana L.; Madigan, Cressida A.; Mendez, Susana; Rao, Jianghong; Savic, Rada M.; Tobin, David M.; Walzl, Gerhard; Wilkinson, Robert J.; Lacourciere, Karen A.; Via, Laura E.; Jain, Sanjay K.
    Nearly 140 years after Robert Koch discovered Mycobacterium tuberculosis, tuberculosis (TB) remains a global threat and a deadly human pathogen. M. tuberculosis is notable for complex host-pathogen interactions that lead to poorly understood disease states ranging from latent infection to active disease. Additionally, multiple pathologies with a distinct local milieu (bacterial burden, antibiotic exposure, and host response) can coexist simultaneously within the same subject and change independently over time. Current tools cannot optimally measure these distinct pathologies or the spatiotemporal changes. Next-generation molecular imaging affords unparalleled opportunities to visualize infection by providing holistic, 3D spatial characterization and noninvasive, temporal monitoring within the same subject. This rapidly evolving technology could powerfully augment TB research by advancing fundamental knowledge and accelerating the development of novel diagnostics, biomarkers, and therapeutics.