SynthesisBMC cancer2025
Diagnostic accuracy of CT-based radiomics models in differentiating lung cancer from tuberculosis in pulmonary lesions: a systematic review and meta-analysis.
Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Radiomics in Lung Cancer Imaging: A Narrative Review of Current Evidence.Journal of imaging · 2026Review
- Tuberculosis Preceding Lung Cancer: A Contemporary Meta-Analysis Revealing a Critical Gap in Post-2020 Evidence.Cancers · 2026Review
- Intranodular and perinodular radiomics features based on non-contrast CT to distinguish pulmonary cryptococcosis from lung adenocarcinoma: a two-center study.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDistinguishing lung cancer (LC) from pulmonary tuberculosis (TB) on CT is difficult. We synthesized evidence on radiomics, clinical, and combined models for LC–TB discrimination.
methodsPubMed, Web of Science, Embase, and Scopus were searched to August 2025 following PRISMA. Metrics were harmonized to LC-positive/TB-negative and pooled with a bivariate random-effects model. Study quality was assessed with QUADAS-2 and METRICS.
resultsFourteen retrospective studies (4281 participants) were included. Radiomics models (11 validations cohorts) achieved pooled sensitivity 0.80 (95% CI 0.74–0.86) and specificity 0.83 (0.75–0.88); SROC AUC 0.88 (0.85–0.91). At a 25% pre-test probability, radiomics models corresponded to post-test probabilities of 61% and 7%. Deeks’ funnel asymmetry test showed no small-study effects (p = 0.88). Clinical-only models performed more modestly (sensitivity 0.60, specificity 0.80, AUC 0.77). Combined radiomics + clinical models performed best (sensitivity 0.82, specificity 0.93, AUC 0.90). Head-to-head comparison showed higher specificity for radiomics versus clinical models (p = 0.02), and higher sensitivity for combined models versus radiomics (p < 0.001) without a clear specificity difference (p = 0.41). Prespecified subgroup analyses indicated that models retaining > 10 radiomic features, developed in cohorts restricted to nodules < 3 cm, and using non-contrast CT tended to perform better, whereas externally validated cohorts showed lower accuracy than internal test sets, and both nodule-size spectrum and CT acquisition phase emerged as major contributors to between-study heterogeneity.
conclusionsCT-based radiomics adds discriminative value beyond clinical variables, and integrating clinical information with radiomics yields the most favorable overall accuracy while highlighting the importance of broader external validation.
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