ArticleScientific reports2025
Integration of ROSE cytology and serum tumor markers for rapid subtyping of lung cancer.
Article in Scientific reports, 2025. 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
Diagnostic delays in lung cancer compromise patient survival. This study aims to develop a deep learning-based framework that integrates rapid on-site evaluation (ROSE) cytomorphology and serum tumor markers to enable accurate pre-pathological subtyping, thereby facilitating earlier treatment initiation. A dataset of 156 matched cases with both ROSE cytology images and five serum biomarkers (squamous cell carcinoma antigen (SCCA), pro-gastrin-releasing peptide (ProGRP), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin-19 fragment (CYFRA21-1)) was retrospectively analyzed. Two deep learning models were developed: (1) a ROSE Image-Only Model (RIOM) using ResNet-50 with spatial attention, and (2) a ROSE-Serum Marker Model (RSMM) incorporating cross-modal feature alignment between ROSE images and serum biomarkers. A consensus strategy was implemented to stratify patients based on prediction agreement between the two models. Manual ROSE assessment achieved 84.0% overall diagnostic accuracy. The RIOM matched this performance (84.5% accuracy), while the multimodal RSMM significantly surpassed both, achieving 91.6% accuracy in five-class classification (Benign, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell lung carcinoma (LCLC), small cell lung cancer (SCLC)). The consensus strategy identified 80.6% of cases as prediction-consistent, for which the accuracy for malignancy determination, non-small cell lung cancer (NSCLC) discrimination, and final subtype classification reached 98.4%. The proposed multimodal decision-support framework provides a rapid and reliable tool for pre-pathological lung cancer subtyping. By enabling high-confidence diagnoses immediately after biopsy, this approach could rationalize clinical workflows through risk-stratified management and significantly accelerate reflex molecular testing and therapy initiation, particularly for the majority of patients with prediction-consistent results.
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