Objective To explore the value of an artificial intelligence (AI)-based imaging model combined with morphological features of computed tomography (CT) in differentiating the benign pulmonary nodules from the malignant ones and predicting the postoperative pathological outcomes. Methods Based on the postoperative pathological results, the selected patients were divided into a benign group and a malignant group. The clinical data and CT imaging features of the patients were collected, and AI was used to analyze the CT images. Univariate and multivariate logistic regression analyses were employed to identify factors independently associated with pathological outcomes, as well as factors associated with AI model misdiagnosis (i.e., false-positive classification of benign nodules). These analyses served to evaluate the diagnostic performance of the AI model. Results Univariate analysis indicated that patient gender, multiple CT features (including morphological appearance, mean CT value, vascular sign, spiculation, cavitation, pleural retraction, calcification and CT imaging features), and AI analysis results were significantly correlated with the pathological outcomes (all P<0.05). Separately, morphological appearance, nodule diameter, calcification, and mean CT value showed significant association with AI model misclassification (all P<0.05). The AI-based clinical prediction model (AUC=0.8837) had better diagnostic efficacy than the clinical prediction model constructed from multivariate analysis results (AUC=0.839 8). Conclusions The analysis of AI-based model and key CT morphological features are independent influencing factors in differentiating benign and malignant pulmonary nodules. Its predictive efficacy is better than a single traditional clinical indicator, providing a powerful tool for clinical screening of high-risk pulmonary nodules and achieving early precise intervention for lung cancer.
Key words
AI /
lung cancer /
CT /
pulmonary nodule /
artificial intelligence
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