Abstract
BACKGROUND: Some lung cancer patients are pathologically confirmed to have lung adenocarcinoma with neuroendocrine differentiation (LUAD-ND). However, research on this subtype remains limited. This study aimed to systematically investigate the metastatic patterns and prognosis-related factors of LUAD-ND, and construct neural network-based prediction models for survival outcomes. METHODS: By analyzing the Surveillance, Epidemiology, and End Results (SEER) database, we employed the Cox proportional hazards model to investigate prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in patients with LUAD-ND. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) and detailed the median survival time and specific time survival probabilities for different features in the LUAD-ND population. Finally, using a neural network algorithm, we developed a predictive model for forecasting LUAD-ND's OS and CSS, evaluating its performance using the area under the receiver operating characteristic curve (AUC). RESULTS: Most patients with LUAD-ND were diagnosed at an advanced stage. The OS time of patients with LUAD-ND was 12 (95% CI: 10-14) months, and the CSS time was 14 (95% CI: 12-16) months. The most common distant metastatic sites were bone, followed by liver, brain, and lung. Surgery (HR =0.51; 95% CI: 0.31-0.82; P=0.006) and chemotherapy (HR =0.33; 95% CI: 0.21-0.50; P<0.001) were associated with improved OS. Similarly, surgery (HR =0.49; 95% CI: 0.28-0.84; P=0.01) and chemotherapy (HR =0.31; 95% CI: 0.19-0.49; P<0.001) were linked to better CSS. The neural network-based tool can effectively predict the prognosis of LUAD-ND, achieving an AUC of 0.852-0.864 for 6-month OS and 0.835-0.883 for 6-month CSS. CONCLUSIONS: Patients with LUAD-ND face a dismal prognosis, yet chemotherapy and surgical interventions can ameliorate their outcomes. The neural network tool developed in this study yields precise prognostic estimations.