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Abstract

There is emerging evidence of a potential role for PET-CT scan as an imaging biomarker to characterise the spectrum of tuberculosis infection (TBI) in humans and animal models.

Synthesis of available evidence from current literature is needed to understand the utility of PET-CT for characterising TBI and how this may inform application of PET-CT in future TBI research.

The aims of this review are to summarise the evidence of PET-CT scan use in immunocompetent hosts with TBI, and compare PET-CT features observed in humans and animal models.

MEDLINE, Embase and PubMed Central were searched to identify relevant publications. Studies were selected if they reported PET-CT features in human or animals with TBI. Studies were excluded if immune deficiency was present at the time of the initial PET-CT scan.

Six studies – four in humans and two in non-human primates (NHP) were included for analysis. All six studies used 2-deoxy-2-[18F]fluoro--glucose (2-[18F]FDG) PET-CT. Features of TBI were comparable between NHP and humans, with 2-[18F]FDG avid intrathoracic lymph nodes observed during early infection. Progressive TBI was characterised in NHP by increasing 2-[18F]FDG avidity and size of lesions. Two human studies suggested that PET-CT can discriminate between active TB and inactive TBI. However, data synthesis was generally limited by human studies including inconsistent and poorly characterised cohorts and the small number of eligible studies for review.

Our review provides some evidence, limited primarily to non-human primate models, of PET-CT utility as a highly sensitive imaging modality to reveal and characterise meaningful metabolic and structural change in early TBI. The few human studies identified exhibit considerable heterogeneity. Larger prospective studies are needed recruiting well characterised cohorts with TBI and adopting a standardized PET-CT protocol, to better understand utility of this imaging biomarker to support future research.

  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-09-26
2024-12-05
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References

  1. Global Tuberculosis Report 2022 Geneva: World Health Organization 2022
    [Google Scholar]
  2. Kiazyk S, Ball TB. Latent tuberculosis infection: an overview. Can Commun Dis Rep 2017; 43:62–66 [View Article] [PubMed]
    [Google Scholar]
  3. Behr MA, Edelstein PH, Ramakrishnan L. Revisiting the timetable of tuberculosis. BMJ 2018; 362:k2738 [View Article] [PubMed]
    [Google Scholar]
  4. Rangaka MX, Wilkinson KA, Glynn JR, Ling D, Menzies D et al. Predictive value of interferon-γ release assays for incident active tuberculosis: a systematic review and meta-analysis. Lancet Infect Dis 2012; 12:45–55 [View Article] [PubMed]
    [Google Scholar]
  5. Drain PK, Bajema KL, Dowdy D, Dheda K, Naidoo K et al. Incipient and subclinical tuberculosis: a clinical review of early stages and progression of infection. Clin Microbiol Rev 2018; 31:e00021-18 [View Article] [PubMed]
    [Google Scholar]
  6. Vass L, Fisk M, Lee S, Wilson FJ, Cheriyan J et al. Advances in PET to assess pulmonary inflammation: a systematic review. Eur J Radiol 2020; 130:109182 [View Article] [PubMed]
    [Google Scholar]
  7. Choi JY, Jhun BW, Hyun SH, Chung MJ, Koh WJ. 18F-fluorodeoxyglucose positron emission tomography/computed tomography for assessing treatment response of pulmonary multidrug-resistant tuberculosis. J Clin Med 2018; 7:559 [View Article] [PubMed]
    [Google Scholar]
  8. Yu W-Y, Lu P-X, Assadi M, Huang X-L, Skrahin A et al. Updates on (18)F-FDG-PET/CT as a clinical tool for tuberculosis evaluation and therapeutic monitoring. Quant Imaging Med Surg 2019; 9:1132–1146 [View Article] [PubMed]
    [Google Scholar]
  9. Esmail H, Lai RP, Lesosky M, Wilkinson KA, Graham CM et al. Characterization of progressive HIV-associated tuberculosis using 2-deoxy-2-[18F]fluoro-D-glucose positron emission and computed tomography. Nat Med 2016; 22:1090–1093 [View Article] [PubMed]
    [Google Scholar]
  10. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372:n71 [View Article] [PubMed]
    [Google Scholar]
  11. Cochrane Data extraction forms 2019 https://dplp.cochrane.org/data-extraction-forms
    [Google Scholar]
  12. Wells GA, Shea B, O’Connell D, Peterson J, Welch V et al. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses Oxford; 2000
    [Google Scholar]
  13. Hooijmans CR, Rovers MM, de Vries RBM, Leenaars M, Ritskes-Hoitinga M et al. SYRCLE’s risk of bias tool for animal studies. BMC Med Res Methodol 2014; 14:43 [View Article] [PubMed]
    [Google Scholar]
  14. Popay J, Roberts H, Sowden A, Petticrew M, Arai L et al. Guidance on the conduct of narrative synthesis in systematic reviews. In A Product from the ESRC Methods Programme Version vol 1 2006 p b92
    [Google Scholar]
  15. Coleman MT, Maiello P, Tomko J, Frye LJ, Fillmore D et al. Early changes by (18)fluorodeoxyglucose positron emission tomography coregistered with computed tomography predict outcome after Mycobacterium tuberculosis infection in cynomolgus macaques. Infect Immun 2014; 82:2400–2404 [View Article] [PubMed]
    [Google Scholar]
  16. Lin PL, Maiello P, Gideon HP, Coleman MT, Cadena AM et al. PET CT identifies reactivation risk in cynomolgus macaques with latent M. tuberculosis. PLoS Pathog 2016; 12:e1005739 [View Article] [PubMed]
    [Google Scholar]
  17. Ji Y, Shao C, Cui Y, Shao G, Zheng J. 18F-FDG positron-emission tomography/computed tomography findings of radiographic lesions suggesting old healed pulmonary tuberculosis and high-risk signs of predicting recurrence: a retrospective study. Sci Rep 2019; 9:12582 [View Article] [PubMed]
    [Google Scholar]
  18. Kim I-J, Lee JS, Kim S-J, Kim Y-K, Jeong YJ et al. Double-phase 18F-FDG PET-CT for determination of pulmonary tuberculoma activity. Eur J Nucl Med Mol Imaging 2008; 35:808–814 [View Article] [PubMed]
    [Google Scholar]
  19. Geadas C, Acuna-Villaorduna C, Mercier G, Kleinman MB, Horsburgh CR et al. FDG-PET/CT activity leads to the diagnosis of unsuspected TB: a retrospective study. BMC Res Notes 2018; 11:464 [View Article] [PubMed]
    [Google Scholar]
  20. Ghesani N, Patrawalla A, Lardizabal A, Salgame P, Fennelly KP. Increased cellular activity in thoracic lymph nodes in early human latent tuberculosis infection. Am J Respir Crit Care Med 2014; 189:748–750 [View Article] [PubMed]
    [Google Scholar]
  21. White AG, Maiello P, Coleman MT, Tomko JA, Frye LJ et al. Analysis of 18FDG PET/CT imaging as a tool for studying Mycobacterium tuberculosis infection and treatment in non-human primates. J Vis Exp 2017; 127:56375 [View Article] [PubMed]
    [Google Scholar]
  22. Houben RMGJ, Menzies NA, Sumner T, Huynh GH, Arinaminpathy N et al. Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models. Lancet Glob Health 2016; 4:e806–e815 [View Article] [PubMed]
    [Google Scholar]
  23. Chen RY, Dodd LE, Lee M, Paripati P, Hammoud DA et al. PET/CT imaging correlates with treatment outcome in patients with multidrug-resistant tuberculosis. Sci Transl Med 2014; 6:265ra166 [View Article] [PubMed]
    [Google Scholar]
  24. Malherbe ST, Chen RY, Dupont P, Kant I, Kriel M et al. Quantitative 18F-FDG PET-CT scan characteristics correlate with tuberculosis treatment response. EJNMMI Res 2020; 10:8 [View Article] [PubMed]
    [Google Scholar]
  25. Ankrah AO, van der Werf TS, de Vries EFJ, Dierckx RAJO, Sathekge MM et al. PET/CT imaging of Mycobacterium tuberculosis infection. Clin Transl Imaging 2016; 4:131–144 [View Article]
    [Google Scholar]
  26. Lawal IO, Fourie BP, Mathebula M, Moagi I, Lengana T et al. 18F-FDG PET/CT as a noninvasive biomarker for assessing adequacy of treatment and predicting relapse in patients treated for pulmonary tuberculosis. J Nucl Med 2020; 61:412–417 [View Article] [PubMed]
    [Google Scholar]
  27. Bracken MB. Why animal studies are often poor predictors of human reactions to exposure. J R Soc Med 2009; 102:120–122 [View Article] [PubMed]
    [Google Scholar]
  28. Colman K. Impact of the genetics and source of preclinical safety animal models on study design, results, and interpretation. Toxicol Pathol 2017; 45:94–106 [View Article] [PubMed]
    [Google Scholar]
  29. Borrell S, Trauner A, Brites D, Rigouts L, Loiseau C et al. Reference set of Mycobacterium tuberculosis clinical strains: a tool for research and product development. PLoS One 2019; 14:e0214088 [View Article] [PubMed]
    [Google Scholar]
  30. Dutta NK, Karakousis PC. Latent tuberculosis infection: myths, models, and molecular mechanisms. Microbiol Mol Biol Rev 2014; 78:343–371 [View Article] [PubMed]
    [Google Scholar]
  31. Ganchua SKC, Cadena AM, Maiello P, Gideon HP, Myers AJ et al. Lymph nodes are sites of prolonged bacterial persistence during Mycobacterium tuberculosis infection in macaques. PLoS Pathog 2018; 14:e1007337 [View Article] [PubMed]
    [Google Scholar]
  32. Lin PL, Coleman T, Carney JPJ, Lopresti BJ, Tomko J et al. Radiologic responses in cynomolgus macaques for assessing tuberculosis chemotherapy regimens. Antimicrob Agents Chemother 2013; 57:4237–4244 [View Article] [PubMed]
    [Google Scholar]
  33. Uzorka JW, Wallinga J, Kroft LJM, Ottenhoff THM, Arend SM. Radiological signs of latent tuberculosis on chest radiography: a systematic review and meta-analysis. Open Forum Infect Dis 2019; 6:ofz313 [View Article] [PubMed]
    [Google Scholar]
  34. Lai RS, Lee SSJ, Ting YM, Wang HC, Lin CC et al. Diagnostic value of transbronchial lung biopsy under fluoroscopic guidance in solitary pulmonary nodule in an endemic area of tuberculosis. Respir Med 1996; 90:139–143 [View Article] [PubMed]
    [Google Scholar]
  35. Lee KS, Kim HT, Cho WS, Kim PN, Bae WK et al. Active solitary tuberculoma of the lung:CT and clinical findings. J Korean Radiol Soc 1993; 29:1200 [View Article]
    [Google Scholar]
  36. Ganchua SKC, Cadena AM, Maiello P, Gideon HP, Myers AJ et al. Lymph nodes are sites of prolonged bacterial persistence during Mycobacterium tuberculosis infection in macaques. PLoS Pathog 2018; 14:e1007337 [View Article] [PubMed]
    [Google Scholar]
  37. Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 2015; 42:328–354 [View Article] [PubMed]
    [Google Scholar]
  38. Adams MC, Turkington TG, Wilson JM, Wong TZ. A systematic review of the factors affecting accuracy of SUV measurements. AJR Am J Roentgenol 2010; 195:310–320 [View Article] [PubMed]
    [Google Scholar]
  39. Lang D, Huber H, Kaiser B, Virgolini I, Lamprecht B et al. SUV as a possible predictor of disease extent and therapy duration in complex tuberculosis. Clin Nucl Med 2018; 43:94–100 [View Article] [PubMed]
    [Google Scholar]
  40. Dureja S, Sen IB, Acharya S. Potential role of F18 FDG PET-CT as an imaging biomarker for the noninvasive evaluation in uncomplicated skeletal tuberculosis: a prospective clinical observational study. Eur Spine J 2014; 23:2449–2454 [View Article] [PubMed]
    [Google Scholar]
  41. Boellaard R, Krak NC, Hoekstra OS, Lammertsma AA. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study. J Nucl Med 2004; 45:1519–1527 [PubMed]
    [Google Scholar]
  42. Kumar V, Nath K, Berman CG, Kim J, Tanvetyanon T et al. Variance of SUVs for FDG-PET/CT is greater in clinical practice than under ideal study settings. Clin Nucl Med 2013; 38:175–182 [View Article] [PubMed]
    [Google Scholar]
  43. Lodge MA. Repeatability of SUV in oncologic (18)F-FDG PET. J Nucl Med 2017; 58:523–532 [View Article]
    [Google Scholar]
  44. Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 2009; 50 Suppl 1:122S–50S [View Article] [PubMed]
    [Google Scholar]
  45. Barrington SF, Kluge R. FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas. Eur J Nucl Med Mol Imaging 2017; 44:97–110 [View Article] [PubMed]
    [Google Scholar]
  46. Paquet N, Albert A, Foidart J, Hustinx R. Within-patient variability of (18)F-FDG: standardized uptake values in normal tissues. J Nucl Med 2004; 45:784–788 [PubMed]
    [Google Scholar]
  47. Chen HHW, Chiu N-T, Su WC, Guo H-R, Lee B-F. Prognostic value of whole-body total lesion glycolysis at pretreatment FDG PET/CT in non-small cell lung cancer. Radiology 2012; 264:559–566 [View Article] [PubMed]
    [Google Scholar]
  48. Van de Wiele C, Kruse V, Smeets P, Sathekge M, Maes A. Predictive and prognostic value of metabolic tumour volume and total lesion glycolysis in solid tumours. Eur J Nucl Med Mol Imaging 2013; 40:290–301 [View Article] [PubMed]
    [Google Scholar]
  49. Quinn B, Dauer Z, Pandit-Taskar N, Schoder H, Dauer LT. Radiation dosimetry of 18F-FDG PET/CT: incorporating exam-specific parameters in dose estimates. BMC Med Imaging 2016; 16:41 [View Article] [PubMed]
    [Google Scholar]
  50. Klenk C, Gawande R, Uslu L, Khurana A, Qiu D et al. Ionising radiation-free whole-body MRI versus (18)F-fluorodeoxyglucose PET/CT scans for children and young adults with cancer: a prospective, non-randomised, single-centre study. Lancet Oncol 2014; 15:275–285 [View Article] [PubMed]
    [Google Scholar]
  51. Marcu LG, Chau M, Bezak E. How much is too much? Systematic review of cumulative doses from radiological imaging and the risk of cancer in children and young adults. Crit Rev Oncol Hematol 2021; 160:103292 [View Article] [PubMed]
    [Google Scholar]
  52. Schöder H GM. Screening for cancer with PET and PET/CT: potential and limitations. J Nucl Med 2007; 48:
    [Google Scholar]
  53. Molton JS, Thomas BA, Pang Y, Khor LK, Hallinan J et al. Sub-clinical abnormalities detected by PET/MRI in household tuberculosis contacts. BMC Infect Dis 2019; 19:83 [View Article] [PubMed]
    [Google Scholar]
  54. Ehman EC, Johnson GB, Villanueva-Meyer JE, Cha S, Leynes AP et al. PET/MRI: where might it replace PET/CT?. J Magn Reson Imaging 2017; 46:1247–1262 [View Article] [PubMed]
    [Google Scholar]
  55. van de Wiel JCM, Wang Y, Xu DM, van der Zaag-Loonen HJ, van der Jagt EJ et al. Neglectable benefit of searching for incidental findings in the Dutch-Belgian lung cancer screening trial (NELSON) using low-dose multidetector CT. Eur Radiol 2007; 17:1474–1482 [View Article] [PubMed]
    [Google Scholar]
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