Differentiation of Early-Stage Tumors from Benign Lesions Manifesting as Pure Ground-Glass nodule: A Clinical Prediction Study Based on AI-Derived Quantitative Parameters

ShuXiang Chen^1,2*HuiJuan Zhang²WenFu Cao²YongXiu Tong²

• ¹Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China
• ²Shengli Clinical Medical College of Fujian Medical University,Fujian Provincial Hospital,Fuzhou University Affiliated Provincial Hospital, Fuzhou, China

Objectives: Differentiating between benign and malignant pure ground-glass nodule (pGGN) is of great clinical significance. The aim of our study was to evaluate whether AI-derived quantitative parameters could predict benignity versus early-stage tumors manifesting as pGGN. Methods: A total of 1,538 patients with pGGN detected by chest CT at different campuses of our hospital from May 2013 to December 2023 were retrospectively analyzed. This included CT and clinical data, as well as AI-derived quantitative parameters. All patients were randomly divided into a training group (n=893), an internal validation group (n=382), and an external validation group (n=263). Hazard factors for earlystage tumors were identified using univariate analysis and multivariate logistic regression analysis. Independent risk factors were then screened, and a prediction nomogram was constructed to maximize predictive efficacy and clinical application value. The performance of the nomogram was evaluated using ROC curves and calibration curves, while decision curve analysis (DCA) was used to assess the net benefit prediction threshold. Results:The final logistic model included nine independent predictors (age, location, minimum CT value, standard deviation, kurtosis, compactness, energy, costopleural distance, and volume) and was developed into a user-friendly nomogram. The AUCs of the ROC curves in the training, internal validation, and external validation cohorts were 0.696 (95% CI: 0.638-0.754), 0.627 (95% CI: 0.533-0.722), and 0.672 (95% CI: 0.543-0.801), respectively. The calibration plot demonstrated a good correlation between observed and predicted values, and the nomogram remained valid in the validation cohort. Decision curve analysis (DCA) showed that the model's predictive performance was acceptable, providing substantial net benefit for clinical application.Conclusions:The clinical prediction nomogram, based on AI-derived quantitative parameters, visually displays an overall score to differentiate benign lesions from early-stage tumors manifesting as pGGN. This nomogram may serve as a convenient screening tool for clinical use and provides a reference for formulating individualized follow-up and treatment plans for patients with pGGN.