EVALUATING THE DIAGNOSTIC PERFORMANCE OF LOW-DOSE CHEST CT FOR PULMONARY TUBERCULOSIS IN HIGH-RISK POPULATIONS: A SYSTEMATIC REVIEW AND META-ANALYSIS

Background: Pulmonary tuberculosis (TB) remains a major global health concern, particularly in high-risk populations such as immunocompromised individuals, children, and close contacts of TB patients. While chest X-ray (CXR) is the conventional screening tool, its sensitivity is limited. Low-dose chest computed tomography (LDCT) may offer improved diagnostic accuracy with reduced radiation exposure compared to standard CT. This study evaluates the diagnostic performance of LDCT for pulmonary TB in high-risk populations. Methods: We conducted a systematic review and meta-analysis of studies published from January 2000 to June 2025 across PubMed, Embase, Scopus, Cochrane Library, and Web of Science. Eligible studies evaluated LDCT (defined as radiation dose ≤1.5 mSv) for pulmonary TB diagnosis in high-risk populations using a valid reference standard. Data were pooled using a bivariate random-effects model to estimate sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary receiver operating characteristic curve (AUC). Results: Fifteen studies (n = 6,274 participants) met inclusion criteria. The pooled sensitivity and specificity of LDCT for pulmonary TB were 91.3% (95% CI: 87.0–94.6%) and 88.2% (95% CI: 83.7–91.9%), respectively. The diagnostic odds ratio was 78.5 (95% CI: 43.2–142.5), with an AUC of 0.94. Subgroup analyses showed slightly higher sensitivity in HIV-positive individuals (93.1%) and children (94.7%) compared to general high-risk cohorts. Heterogeneity was moderate (I² = 53.6%), primarily due to differences in CT protocol and patient demographics. Conclusions: LDCT demonstrates high diagnostic accuracy for pulmonary TB in high-risk populations, outperforming CXR in sensitivity while minimizing radiation exposure. These findings support the integration of LDCT into TB diagnostic algorithms, especially in settings where rapid, early detection is critical. Further research is needed to validate cost-effectiveness and implementation strategies.