The NET Score: An Interpretable AI-Assisted Prognostic Score for Mortality Risk in Lung Neuroendocrine Tumors

Background: Lung neuroendocrine tumors (NETs), including typical and atypical carcinoids, show heterogeneous outcomes. Existing nomograms are often complex and insufficiently validated, limiting their bedside use. A simple, reproducible model based on routine pathology is therefore needed. This study aimed to develop and internally validate the NET Score, an interpretable, AI-assisted tool to estimate individualized mortality risk. Methods: In a retrospective cohort of resected pulmonary carcinoids, candidate predictors were screened using LASSO regression to reduce overfitting and identify key variables: nodal status, mitotic index (>2 per 2 mm2), necrosis, and Ki-67 (>5%). Selected variables were incorporated into a logistic regression model to generate a point-based score (0-8). The model estimates cumulative mortality risk across follow-up, not fixed-time survival. Internal validation included bootstrap and cross-validation to assess discrimination and calibration. Results: The final model included LODDS > -0.5 (3 points), mitotic index >2 (2 points), necrosis (2 points), and Ki-67 (1 point). Discrimination was moderate (bootstrap-corrected AUC 0.70). Risk groups were defined as low (0-2 points, ≤5%), intermediate (3-4 points, 8-12%), and high (≥5 points, ≥18%). Kaplan-Meier curves demonstrated progressive survival stratification across risk groups. Conclusion: The NET Score is a practical and interpretable prognostic tool for lung NETs. It supports risk communication and clinical decision-making while maintaining transparency. External validation is required. Combining AI-based variable selection with a simple scoring system represents a pragmatic approach to prognostic modeling in rare thoracic malignancies.