DeepPMD: A Comprehensive Deep Learning Framework for Primary-Metastatic Classification and Origin Prediction in Lung Adenocarcinoma – Multi-Center Whole Slide Image Validation

Background Among solid malignancies, metastatic spread accounts for ~ 90% of cancer-related deaths. For unknown pulmonary lesions, accurate diagnosis is crucial for treatment. In diagnostically challenging cases like second primary tumor and cancer of unknown primary (CUP), traditional morphological analysis and immunohistochemistry (IHC) often remain inconclusive, delaying critical therapeutic intervention. Methods We developed a deep learning model (DeepPMD) that makes predictions using H&E stained whole slide images and basic clinical data. The model was trained and validated on a single-center cohort of 793 patients, then externally validated on three additional cohorts totaling 1187 patients from independent centers. Results DeepPMD achieved accurate prediction of tumor nature and site of origin, outperforming benchmarks in all tests. In external validation, the model achieved a macro-AUC of 0.974 (95% CI 0.949, 0.991) for origin prediction on excisional biopsies and 0.935 (95% CI 0.913, 0.953) on aspiration biopsies. Its diagnostic logic aligned with pathological criteria, and its CUP predictions showed high concordance with clinical predictions (consistency score 0.86). Conclusion DeepPMD is an accurate, generalizable, and interpretable AI tool for identifying pulmonary tumor origins from WSIs. It can provide probability-ranked potential primary sites to guide targeted IHC testing, optimizing diagnostic workflow, shortening turnaround times, and conserving tissue samples.