Development and preliminary validation of a CD161 immunoscore-based screening method for latent tuberculosis infection in a Chinese training cohort

ABSTRACT Current latent tuberculosis infection (LTBI) screening is limited by tuberculin skin test (TST) cross-reactivity, return-visit requirements, and interferon-gamma release assay (IGRA) costs. We developed a flow cytometry and deep learning CD161 immunoscore screening method, evaluating it across two phases ( N = 269) in Jiangxi, China. Phase 1 ( n = 110; 92 males, 18 females) used healthy controls ( n = 88) and clinically cured, post-treatment tuberculosis (TB) patients ( n = 22) for model development. This post-treatment cohort served as a proxy and was not equivalent to untreated LTBI. Phase 2 ( n = 159, all males) provided an independent, three-site proof-of-concept evaluation against TST, using IGRA as the reference standard. In phase 1, the optimal model achieved 100% sensitivity and 92% specificity. In phase 2, using a non-independently validated, post hoc Youden-selected cutoff (60.57), the immunoscore demonstrated 81.8% sensitivity, 80.4% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.833. This performance was comparable to TST (90.9% sensitivity, 72.3% specificity, AUC = 0.816, and DeLong P = 0.701). The immunoscore showed significantly higher specificity than TST ( P = 0.049) but similar sensitivity ( P = 0.521), with an IDI of 0.077 ( P = 0.021) and good calibration ( P = 0.553). These exploratory, all-male results do not establish discrimination between untreated LTBI, post-treatment sensitization, or active TB, necessitating diverse validation. IMPORTANCE A machine-learning-driven CD161 immunoscore using flow cytometry provides rapid latent tuberculosis infection (LTBI) screening with diagnostic performance comparable to established methods. Across this two-phase study of 269 participants, the independent all-male phase 2 cohort of 159 participants yielded a sensitivity of 81.8% and a specificity of 80.4% for the CD161 immunoscore. Performance remained consistent across colleges with sevenfold variation in LTBI prevalence (1.8%–12.2%), supporting robustness. For congregate settings such as colleges, this laboratory-based approach may offer operational advantages by providing same-day initial screening without requiring a return visit for result interpretation. A possible future application would be a staged pathway in which immunoscore-positive individuals undergo confirmatory interferon-gamma release assay testing. This implementation strategy remains conceptual and requires prospective evaluation of referral rates, laboratory capacity, acceptability, clinical outcomes, and cost-effectiveness. These proof-of-concept findings require confirmation in larger external cohorts with adequate female representation and more diverse demographic, occupational, and geographic backgrounds.

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Publication Details

Journal
Microbiology Spectrum
Published
2026-10-22
DOI
https://doi.org/10.1128/spectrum.01118-26
Primary Topic
Tuberculosis Research and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00
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article

Development and preliminary validation of a CD161 immunoscore-based screening method for latent tuberculosis infection in a Chinese training cohort

Shuang Feng, Ying Zhong, Tao Jiang, Jingyi Huang et al.
Microbiology Spectrum
Tuberculosis Research and Epidemiology
article

Development and preliminary validation of a CD161 immunoscore-based screening method for latent tuberculosis infection in a Chinese training cohort

Shuang Feng, Ying Zhong, Tao Jiang, Jingyi Huang, Xiaobin Huang, Yuanyuan Xiong, Ying Shen, Yu Wu, Liping Ning, Jin Wang
article en

Abstract

ABSTRACT Current latent tuberculosis infection (LTBI) screening is limited by tuberculin skin test (TST) cross-reactivity, return-visit requirements, and interferon-gamma release assay (IGRA) costs. We developed a flow cytometry and deep learning CD161 immunoscore screening method, evaluating it across two phases ( N = 269) in Jiangxi, China. Phase 1 ( n = 110; 92 males, 18 females) used healthy controls ( n = 88) and clinically cured, post-treatment tuberculosis (TB) patients ( n = 22) for model development. This post-treatment cohort served as a proxy and was not equivalent to untreated LTBI. Phase 2 ( n = 159, all males) provided an independent, three-site proof-of-concept evaluation against TST, using IGRA as the reference standard. In phase 1, the optimal model achieved 100% sensitivity and 92% specificity. In phase 2, using a non-independently validated, post hoc Youden-selected cutoff (60.57), the immunoscore demonstrated 81.8% sensitivity, 80.4% specificity, and an area under the receiver operating characteristic curve (AUC) of 0.833. This performance was comparable to TST (90.9% sensitivity, 72.3% specificity, AUC = 0.816, and DeLong P = 0.701). The immunoscore showed significantly higher specificity than TST ( P = 0.049) but similar sensitivity ( P = 0.521), with an IDI of 0.077 ( P = 0.021) and good calibration ( P = 0.553). These exploratory, all-male results do not establish discrimination between untreated LTBI, post-treatment sensitization, or active TB, necessitating diverse validation. IMPORTANCE A machine-learning-driven CD161 immunoscore using flow cytometry provides rapid latent tuberculosis infection (LTBI) screening with diagnostic performance comparable to established methods. Across this two-phase study of 269 participants, the independent all-male phase 2 cohort of 159 participants yielded a sensitivity of 81.8% and a specificity of 80.4% for the CD161 immunoscore. Performance remained consistent across colleges with sevenfold variation in LTBI prevalence (1.8%–12.2%), supporting robustness. For congregate settings such as colleges, this laboratory-based approach may offer operational advantages by providing same-day initial screening without requiring a return visit for result interpretation. A possible future application would be a staged pathway in which immunoscore-positive individuals undergo confirmatory interferon-gamma release assay testing. This implementation strategy remains conceptual and requires prospective evaluation of referral rates, laboratory capacity, acceptability, clinical outcomes, and cost-effectiveness. These proof-of-concept findings require confirmation in larger external cohorts with adequate female representation and more diverse demographic, occupational, and geographic backgrounds.

Microbiology Spectrum
The First Affiliated Hospital, Sun Yat-sen University (CN)
Openalex Percentile: Top 12%
Tuberculosis Research and Epidemiology
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