Tuberculosis other imaging findings

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Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]; Associate Editor(s)-in-Chief: Mashal Awais, M.D.[2]; Sophia Saad, Associate Editor - WikiDoc [3] João André Alves Silva, M.D. [4]; Alejandro Lemor, M.D. [5]

Other Imaging Findings

This microchapter addresses imaging modalities outside dedicated chest radiograph, CT, MRI, and echocardiography/ultrasound sections: primarily 18F-FDG PET/CT for disease activity, extent, and treatment response; point-of-care ultrasound (POCUS) as a triage adjunct; and digital chest radiography with artificial-intelligence computer-aided detection (CAD) as the modern successor to mass miniature radiography (abreugraphy). Cross-sectional anatomic findings by organ system are covered in the CT, MRI, and ultrasound chapters and are referenced here only for context.[1][2]

18F-FDG PET/CT

FDG accumulates in activated macrophages and lymphocytes within granulomas. PET/CT is highly sensitive for active tuberculosis and can map the full anatomic burden of disease, including clinically occult extrapulmonary and disseminated foci, in a single examination.[1][3]

Principal clinical roles include:

  • Differentiating active from inactive/healed disease (more sensitive than radiography or CT).[2][3]
  • Detecting extrapulmonary and disseminated sites and guiding biopsy in nonspecific presentations.[3]
  • Monitoring treatment response, where metabolic change precedes morphologic change. However, FDG-PET/CT is not yet validated for clinical response assessment or defining cure: in the largest prospective sequential study, 64% of extrapulmonary-TB patients remained PET-positive at the end of WHO-recommended treatment (a group that included patients who had developed MDR-TB), and residual metabolic activity is present in ~45–55% of culture-cured pulmonary-TB patients. A negative end-of-treatment scan is protective against relapse, whereas residual activity—particularly with bilateral cavitary disease—is associated with relapse. Declining uptake and detection of latent-to-active transition remain research applications. Where FDG-PET/CT is unavailable, CT features of active disease (nodules, consolidation, tree-in-bud micronodules, FDG-avid nodes) correlate with residual metabolic activity and may serve as a surrogate for response assessment.[4][5][6]

Limitation: Low specificity. FDG-avid lesions cannot be reliably distinguished from malignancy (e.g., solitary pulmonary nodule) or other granulomatous/inflammatory processes such as sarcoidosis. A systematic review of 890 nodules found dual-time-point imaging no better than single-time-point (pooled sensitivity 85%, specificity 77%), and specificity is substantially worse in TB-endemic regions—a meta-analysis reported specificity of 61% (95% CI 49–72%) in endemic-infection areas versus 77% in non-endemic areas, with individual TB-endemic series as low as 25%.[7] Neither SUVmax nor nodule size reliably separates active tuberculosis from malignancy in these settings, although newer multi-time-point PET with dynamic-curve/machine-learning modeling is under investigation.[3][8][9][10][11] PET/CT remains primarily a research and problem-solving tool rather than a routine diagnostic test.

Point-of-Care Ultrasound (POCUS)

POCUS protocols such as FASH (Focused Assessment with Sonography for HIV-associated TB: pericardial, pleural, or ascitic effusion; abdominal lymphadenopathy; splenic or hepatic microabscesses) and lung ultrasound (subpleural nodules, consolidations) are portable and radiation-free but lack sufficient stand-alone accuracy.[12][13]

In a large prospective cohort (n=541, predominantly HIV-negative, India), FASH sensitivity was 51% and specificity 70%. Small (<1 cm) subpleural consolidations were sensitive (93%) but highly nonspecific (16%); larger consolidations performed comparably to chest radiography but did not meet WHO target product profile thresholds for a facility-based triage test.[12] A systematic review reached the same conclusion, noting absence of consensus protocols and high risk of bias.[13] On pooled meta-analysis, individual abdominal FASH features are specific but insensitive (enlarged lymph nodes 39% sensitivity/89% specificity; hypoechoic splenic lesions 30%/93%), and specificity falls in people living with HIV because target features overlap with other opportunistic infections and malignancies. Conversely, the combination of no FASH signs plus a normal chest radiograph and temperature <37.5°C has been reported to have ~99% sensitivity and 95% negative predictive value, defining the main rule-out use case.[14][15]

POCUS is best used as one component within a diagnostic algorithm and to guide sampling or effusion drainage, not as a screening or rule-out test.

Digital Chest Radiography with AI/CAD

Mass miniature radiography (abreugraphy/MMR) is obsolete. Its conceptual role has been replaced by digital chest radiography interpreted by computer-aided detection (CAD) software. WHO conditionally recommends (low certainty of evidence) CAD in place of human readers for tuberculosis screening and triage in people ≥15 years of age; CAD is not validated in children.[16][17]

Performance approximates that of human readers:

  • In a 10-country deep-learning study, the algorithm was noninferior to radiologists (AUC 0.89); at a sensitivity-favoring threshold it achieved 88% sensitivity/79% specificity versus 75%/84% for radiologists.[18]
  • In a multinational cohort of symptomatic primary-care attendees, CAD4TB v7 met WHO target product profile thresholds and outperformed Xpert-HR and CRP as a triage test (70% specificity at 90% sensitivity), with two-test algorithms further improving accuracy.[19] Generalizability is setting-dependent: in a pooled South African community-based active-case-finding analysis, CAD4TB v7 did not meet the target product profile (≈45% specificity at 90% sensitivity), performing worse in people living with HIV and asymptomatic individuals.[20] Independent multi-country head-to-head evaluation confirms that only a subset of commercial algorithms (e.g., CAD4TB, qXR, INSIGHT CXR) reach the 90% sensitivity/70% specificity triage target, with persistent underperformance in people living with HIV, those aged ≥50 years, and those with prior tuberculosis, only partly corrected by population-specific thresholds.[21]
  • A 2025 meta-analysis of five commercial products reported sensitivities of 86–91% and specificities of 59–80%, with an inherent sensitivity–specificity trade-off.[22]

Implementation caveats: CAD score thresholds must be locally calibrated. Stratifying CAD score thresholds by client characteristics such as age and sex can improve accuracy over a single universal threshold. Notably, a substantial proportion of individuals scoring above threshold with a negative routine single-sputum test have true TB detectable only with enhanced sampling or follow-up, suggesting current confirmation strategies may underestimate CAD yield.[23][24] Accuracy declines in people living with HIV, smear-negative disease, and those with prior tuberculosis (retained scarring increases false positives).[25][26] CAD identifies radiographic abnormality suggestive of tuberculosis; it is a triage/screening step that requires microbiologic confirmation (sputum Xpert Ultra or culture) and is not a diagnostic endpoint.[24][19] CAD accuracy for non-TB radiographic abnormalities is unestablished.

Clinical Recommendations

  • Prefer digital chest radiography plus WHO-endorsed CAD for community or high-risk group screening over legacy mass miniature radiography; confirm all CAD-positive results with a molecular WHO-recommended rapid diagnostic (Xpert Ultra) or culture.[24][19] Cost-effectiveness supports this sequencing: microsimulation modeling in South Africa found that digital CXR/CAD followed by confirmatory sputum Xpert Ultra identified ~13% fewer TB cases than universal Xpert Ultra but reduced screening costs by ~45% and was cost-effective (ICER $610 vs $3460 per year-of-life saved).[27]
  • Calibrate CAD score thresholds to the local population and stratify interpretation by HIV status and prior tuberculosis history.[25][26]
  • Reserve FDG-PET/CT for defining extent of extrapulmonary or disseminated disease, guiding biopsy, or assessing treatment response in complex cases—not for routine diagnosis and not to distinguish tuberculosis from malignancy.[3]
  • Use POCUS/FASH as an algorithm adjunct and procedural guide, not as a rule-out screening test.[12][13]

High-Yield Clinical Pearls

  • FDG-PET/CT is sensitive but not specific: an FDG-avid nodule may represent tuberculosis or cancer.[3]
  • CAD-read digital chest radiography performs on par with radiologists and can substantially reduce radiologist workload while preserving sensitivity in high-burden primary-care settings.[18]
  • Any positive screening image (CAD or human interpretation) requires microbiologic confirmation before treatment.[19]
  • See the MRI findings microchapter for radiation-free pulmonary imaging options (children, pregnancy, serial follow-up).

Common Pitfalls

  • Using uncalibrated CAD thresholds or legacy mass miniature radiography, generating excess false positives—particularly in patients with healed prior tuberculosis.[25][26]
  • Interpreting a positive CAD score or FDG-avid lesion as diagnostic of active tuberculosis without bacteriologic confirmation.[3][19]
  • Over-relying on POCUS as a rule-out test when its performance falls short of WHO triage targets.[12]
  • Assuming CAD or chest radiograph sensitivity is preserved in people living with HIV and smear-negative disease, where it declines.[25]

References

  1. ↑ 1.0 1.1 Bomanji JB, Alorfi F, Algodayan S; et al. (2026). "Imaging Modalities in Tuberculosis". Cold Spring Harbor Perspectives in Medicine. 16 (7): a041827. doi:10.1101/cshperspect.a041827.
  2. ↑ 2.0 2.1 Skoura E, Zumla A, Bomanji J (2015). "Imaging in Tuberculosis". International Journal of Infectious Diseases. 32: 87–93. doi:10.1016/j.ijid.2014.12.007.
  3. ↑ 3.0 3.1 3.2 3.3 3.4 3.5 3.6 Priftakis D, Riaz S, Zumla A, Bomanji J (2020). "Towards More Accurate F-Fluorodeoxyglucose Positron Emission Tomography (F-FDG PET) Imaging in Active and Latent Tuberculosis". International Journal of Infectious Diseases. 92S: S85–S90. doi:10.1016/j.ijid.2020.02.017.
  4. ↑ Bomanji J, Sharma R, Mittal BR; et al. (2020). "Sequential 18F-FDG PET Scan Findings in Patients With Extrapulmonary Tuberculosis During the Course of Treatment—a Prospective Observational Study". European Journal of Nuclear Medicine and Molecular Imaging. 47 (13): 3118–3129. PMID 32483653 Check |pmid= value (help).
  5. ↑ Lawal IO, Fourie BP, Mathebula M; et al. (2020). "18F-FDG PET/CT as a Noninvasive Biomarker for Assessing Adequacy of Treatment and Predicting Relapse in Patients Treated for Pulmonary Tuberculosis". Journal of Nuclear Medicine. 61 (3): 412–417. PMID 31451489.
  6. ↑ Lawal IO, Mokoala KMG, Mathebula M; et al. (2022). "Correlation Between CT Features of Active Tuberculosis and Residual Metabolic Activity on End-of-Treatment FDG PET/CT in Patients Treated for Pulmonary Tuberculosis". Frontiers in Medicine. 9: 791653. PMID 35295606 Check |pmid= value (help).
  7. ↑ Deppen SA, Blume JD, Kensinger CD; et al. (2014). "Accuracy of FDG-PET to Diagnose Lung Cancer in Areas With Infectious Lung Disease: A Meta-Analysis". JAMA. 312 (12): 1227–1236. PMID 25226478.
  8. ↑ Gould MK, Donington J, Lynch WR; et al. (2013). "Evaluation of Individuals With Pulmonary Nodules: When Is It Lung Cancer? Diagnosis and Management of Lung Cancer, 3rd ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines". Chest. 143 (5 Suppl): e93S–e120S. PMID 23649456.
  9. ↑ Sathekge M, Maes A, D'Asseler Y, Vorster M, Van de Wiele C (2012). "Nuclear Medicine Imaging in Tuberculosis Using Commercially Available Radiopharmaceuticals". Nuclear Medicine Communications. 33 (6): 581–590. PMID 22422098.
  10. ↑ Niyonkuru A, Chen X, Bakari KH; et al. (2020). "Evaluation of the Diagnostic Efficacy of 18F-Fluorine-2-Deoxy-D-Glucose PET/CT for Lung Cancer and Pulmonary Tuberculosis in a Tuberculosis-Endemic Country". Cancer Medicine. 9 (3): 1123–1129. PMID 31856410.
  11. ↑ Luo Y, Li J, Ma W; et al. (2023). "Differential Diagnosis of Lung Cancer and Tuberculosis Based on 18F-Fluorodeoxyglucose PET/CT Multi-Time Points Imaging". Nuclear Medicine Communications. 44 (8): 659–667. PMID 37272287 Check |pmid= value (help).
  12. ↑ 12.0 12.1 12.2 12.3 Weber SF, Wolf R, Manten K; et al. (2025). "Lung and Abdominal Ultrasound Accuracy for Tuberculosis: An Indian Prospective Cohort Study". PloS One. 20 (9): e0329670. doi:10.1371/journal.pone.0329670.
  13. ↑ 13.0 13.1 13.2 Bigio J, Kohli M, Klinton JS; et al. (2021). "Diagnostic Accuracy of Point-of-Care Ultrasound for Pulmonary Tuberculosis: A Systematic Review". PloS One. 16 (5): e0251236. doi:10.1371/journal.pone.0251236.
  14. ↑ Belard S, Taccari F, Kumwenda T; et al. (2024). "Point-of-Care Ultrasound for Tuberculosis and HIV—Revisiting the FASH Protocol and Its Differential Diagnoses". Clinical Microbiology and Infection. 30 (3): 320–327. PMID 37923216 Check |pmid= value (help).
  15. ↑ Ndege R, Weisser M, Elzi L; et al. (2019). "Sonography to Rule Out Tuberculosis in Sub-Saharan Africa: A Prospective Observational Study". Open Forum Infectious Diseases. 6 (4): ofz154. PMID 31041350.
  16. ↑ Dheda K, Perumal T, Moultrie H; et al. (2022). "The Intersecting Pandemics of Tuberculosis and COVID-19: Population-Level and Patient-Level Impact, Clinical Presentation, and Corrective Interventions". The Lancet Respiratory Medicine. 10 (6): 603–622.
  17. ↑ Geric C, Qin ZZ, Denkinger CM; et al. (2023). "The Rise of Artificial Intelligence Reading of Chest X-Rays for Enhanced TB Diagnosis and Elimination". The International Journal of Tuberculosis and Lung Disease. 27 (5): 367–372. PMID 37143227 Check |pmid= value (help).
  18. ↑ 18.0 18.1 Kazemzadeh S, Yu J, Jamshy S; et al. (2023). "Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists". Radiology. 306 (1): 124–137. doi:10.1148/radiol.212213.
  19. ↑ 19.0 19.1 19.2 19.3 19.4 Crowder R, Thangakunam B, Andama A; et al. (2026). "Diagnostic Accuracy of Tuberculosis Screening Tests in a Prospective Multinational Cohort: Chest Radiography With Computer-Aided Detection, Xpert Tuberculosis Host Response, and C-Reactive Protein". Clinical Infectious Diseases. 82 (2): e239–e247. doi:10.1093/cid/ciae549.
  20. ↑ Scott AJ, Perumal T, Pooran A; et al. (2025). "Clinical Evaluation of Computer-Aided Digital X-Ray Detection of Pulmonary Tuberculosis During Community-Based Screening or Active Case-Finding: A Case-Control Study". Lancet Global Health. doi:10.1016/S2214-109X(24)00517-6.
  21. ↑ Worodria W, Castro R, Kik SV; et al. (2026). "A Multi-Country Head-to-Head Accuracy Comparison of Automated Chest X-Ray Algorithms for Tuberculosis". Annals of the American Thoracic Society. PMID 41973983 Check |pmid= value (help).
  22. ↑ Han ZL, Zhang YY, Li J; et al. (2025). "A Systematic Review and Meta-Analysis of Artificial Intelligence Software for Tuberculosis Diagnosis Using Chest X-Ray Imaging". Journal of Thoracic Disease. 17 (5): 3223–3237. doi:10.21037/jtd-2025-604.
  23. ↑ Sung J, Kitonsa PJ, Nalutaaya A; et al. (2025). "Performance of Universal and Stratified Computer-Aided Detection Thresholds for Chest X-Ray-Based Tuberculosis Screening: A Cross-Sectional, Diagnostic Accuracy Study". The Lancet Digital Health. PMID 41309428 Check |pmid= value (help).
  24. ↑ 24.0 24.1 24.2 Macpherson L, Kik SV, Quartagno M; et al. (2025). "Diagnostic Accuracy of Chest X-Ray Computer-Aided Detection Software for Detection of Prevalent and Incident Tuberculosis in Household Contacts". Clinical Infectious Diseases. 80 (3): 626–636. doi:10.1093/cid/ciae528.
  25. ↑ 25.0 25.1 25.2 25.3 Tavaziva G, Harris M, Abidi SK; et al. (2022). "Chest X-Ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy". Clinical Infectious Diseases. 74 (8): 1390–1400. doi:10.1093/cid/ciab639.
  26. ↑ 26.0 26.1 26.2 Codlin AJ, Dao TP, Vo LNQ; et al. (2021). "Independent Evaluation of 12 Artificial Intelligence Solutions for the Detection of Tuberculosis". Scientific Reports. 11 (1): 23895. doi:10.1038/s41598-021-03265-0.
  27. ↑ Deleger JN, Khatami SN, Jones M; et al. (2026). "Cost-Effectiveness of Community Tuberculosis Screening in South Africa". American Journal of Respiratory and Critical Care Medicine. PMID 42085271 Check |pmid= value (help).

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