A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment.

Background:  Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy.

Methods:  The study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules. AI technology was employed to assess CT features, such as nodule size, solidity, and malignancy probability. Additionally, lung cancer blood biomarkers were measured. Statistical analysis involved univariate analysis to identify significant differences among factors, followed by multivariate logistic regression to establish independent risk factors. The model performance was validated using receiver operating characteristic curves and decision curve analysis (DCA) for internal validation. Furthermore, an external dataset comprising 51 cases of lung nodules was utilized for independent validation to assess robustness and generalizability.

Results:  A total of 176 patients were included, divided into benign/preinvasive (n = 76) and invasive cancer groups (n = 100). Multivariate analysis identified eight independent predictors of malignancy: lobulation sign, bronchial inflation sign, AI-predicted malignancy probability, nodule nature, diameter, solidity proportion, vascular endothelial growth factor, and lung cancer autoantibodies. The combined predictive model demonstrated high accuracy (area under the curve [AUC] = 0.946). DCA showed that the combined model significantly outperformed the traditional model, and also proved superior to models using AI-predicted malignancy probability or the seven lung cancer autoantibodies plus traditional model. External validation confirmed its robustness (AUC = 0.856), achieving a sensitivity of 0.80 and specificity of 0.86, effectively distinguishing between invasive and noninvasive nodules.

Conclusion:  This combined approach of AI-based CT features analysis with lung cancer biomarkers provides a more accurate and clinically useful tool for guiding treatment decisions in pulmonary nodule patients. Further studies with larger cohorts are warranted to validate these findings across diverse patient populations.