LungPanelNet: a machine learning-based approach for the early prediction and differentiation of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) represents a major global health challenge, primarily due to its frequent diagnosis at advanced stages, which significantly limits therapeutic efficacy and results in poor survival outcomes. A critical unmet need exists for non-invasive, accurate diagnostic tools for early detection. OBJECTIVE: This study aimed to develop and validate a robust machine learning model based on a panel of serum tumor markers for the early prediction of NSCLC and its differentiation from benign pulmonary conditions. METHODS: In this retrospective cohort study, we recruited 2,283 participants, including 1,339 with NSCLC, 313 with pneumonia, 260 with biopsy-confirmed benign lesions, and 371 with other benign lung masses. Serum levels of six key tumor markers-Squamous Cell Carcinoma Antigen (SCCA), Carcinoembryonic Antigen (CEA), Cancer Antigen 125 (CA-125), Cytokeratin 19 Fragment (CYFRA21-1), Neuron-Specific Enolase (NSE), and Pro-Gastrin-Releasing Peptide (ProGRP)-were quantified, and a custom deep neural network, LungPanelNet, was constructed for the classification task. RESULTS: The model demonstrated superior predictive performance on an independent testing set, achieving an area under the receiver operating characteristic curve (AUC-ROC) of 0.92 (95% CI: 0.88-0.96), with an accuracy of 89.3%, a sensitivity of 91.5%, and a specificity of 87.8%. Feature importance analysis identified SCCA and CYFRA21-1 as the most significant predictors. CONCLUSION: Our findings demonstrate that a machine learning model integrating a panel of serum tumor markers can effectively distinguish NSCLC from a spectrum of benign pulmonary conditions with high accuracy. This approach shows promise as a clinical decision-support tool, though further validation in larger, prospective, multi-center cohorts is warranted. This was a retrospective cohort study without clinical trial registration.