ReviewJournal of pathology informatics2026
Artificial intelligence for lung cancer classification in cytology specimens: A systematic review and diagnostic test accuracy meta-analysis of benign-malignant diagnosis and ADC/SCC/SCLC subtyping.
Review in Journal of pathology informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Cytology specimens are essential for lung cancer diagnosis, yet distinguishing benign from malignant cells and subtyping adenocarcinoma, squamous cell carcinoma, and small-cell lung cancer remains challenging due to limited cellular material. Methods: We systematically searched seven databases for studies evaluating artificial intelligence-based lung cancer classification in cytology. After screening, 32 studies were included, of which 20 contributed 33 analyzable two-by-two data rows for diagnostic test accuracy meta-analysis. Outcomes were pooled using bivariate logit random-effects models. Summary receiver operating characteristic curves, Fagan nomograms, Deeks' test for publication bias, and quality assessments using QUADAS-2 and artificial intelligence-specific tools were performed. Results: For benign versus malignant diagnosis, 18 data rows encompassing 3464 observations yielded a pooled sensitivity of 89.3% (95% confidence interval, 84.1-92.9), a specificity of 88.5% (95% confidence interval, 83.2-92.3), and an area under the curve of 0.952. At a median prevalence of 58.2%, the post-test probability of malignancy after a positive artificial intelligence result was 91.2%, whereas a negative result yielded a post-test probability of 14.6%. For one-versus-rest subtyping among adenocarcinoma, squamous cell carcinoma, and small-cell lung cancer, 14 data rows comprising 2687 observations demonstrated a pooled sensitivity of 77.5%, specificity of 89.5%, and area under the curve of 0.901. Small-cell lung cancer showed the strongest performance (specificity 93.2%, area under the curve 0.949), whereas squamous cell carcinoma had lower sensitivity (67.0%). Substantial heterogeneity was observed across studies. Deeks' test suggested potential small-study effects, and the risk of bias was high in all included studies. Conclusion: Artificial intelligence-based cytology classification achieves high diagnostic accuracy for benign-malignant discrimination and shows promising but less mature performance for histological subtyping. Prospective external validation, standardized reporting, and clinically integrated human-artificial intelligence studies are required before routine clinical deployment.
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