Abstract: Objective: To develop and validate a nomogram for predicting lung injury severity in patients with CTD-ILD by integrating quantitative computed tomography (CT) indicators (standard deviation, normal lung percentage, and fibrosis percentage) and clinical characteristics (sex and disease duration), providing a reference for clinical risk stratification. Methods: This retrospective study included 162 patients with connective tissue disease-associated interstitial lung disease (CTD-ILD) who were divided into mild (Warrick score <8) and moderate-severe (Warrick score ≥8) groups. Quantitative CT images were obtained using a 3D-Slicer. Key parameters were identified using logistic regression analysis and model performance was evaluated using 10-fold cross-validation, bootstrap resampling, and decision curve analysis (DCA). Results: The model identified sex (OR=0.293), SD (OR=1.043), F% (OR=1.708), MLD（P=0.003, OR=0.969）and NSIP subtype (OR=0.175) as independent predictors. The nomogram showed excellent discrimination (The 10-fold cross-validation yielded a mean area under the curve of 0.848, and the concordance index was 0.833 following 1,000 Bootstrap resampling iterations), good calibration, and the highest net benefit at a 15% high-risk threshold via DCA, making it suitable for clinical decisions. Conclusion: This study successfully developed a multiparametric prediction model for assessing CTD-ILD severity, demonstrating good discrimination and calibration, thereby offering a valuable reference for individualized treatment and rational resource allocation.