Background/purpose : Interstitial lung disease (ILD) is an important factor determining the course of systemic sclerosis (SSc). Quantitative high-resolution computed tomography (HRCT) radiomics can aid in the detection of ILD, particularly when pulmonary function tests cannot be performed. The aim of this study is to develop machine learning (ML)-based models using only radiomic, only clinical, and radiomic+clinical combinations for the detection of ILD in SSc and to compare their performance.Materials and Methods This retrospective, single-center study included 67 SSc patients(38 ILD positive, 29 ILD negative; mean age 58 ± 13.9; 5 males). A total of 852 radiomic features were extracted from HRCT using whole-lung segmentation, and after feature selection, the performance of logistic regression(LR) models was evaluated using 5-fold cross-validation and out-of-fold(OOF) probabilities to calculate AUC, accuracy, and F1 score (radiomic only, clinical only, radiomic+clinical); thresholds were determined using the Youden index. Calibration and decision curve analysis(DCA) were performed, and binary AUC differences were tested using the De Long method.Results The radiomic model achieved an OOF AUC of 0.819, an accuracy of 0.821, and an F1 score of 0.838(sensitivity 0.816, specificity 0.828). The clinical model showed lower performance(AUC 0.760; accuracy 0.761; F1 score 0.771). The combined model achieved an AUC value of 0.825 with higher sensitivity (0.868) but lower specificity(0.655), resulting in 0.776 accuracy and 0.815 F1 score. The DeLong test showed no significant AUC differences between the models(all p > 0.05). Decision curve analysis results showed positive net benefit for all strategies at low to moderate thresholds, with largely overlapping curves.Conclusion HRCT radiomic features provided good discrimination for detecting ILD in SSc and performed better than clinical variables alone; adding clinical data slightly increased sensitivity without significantly increasing AUC. Radiomics-based approaches may provide a useful imaging biomarker for ILD detection, particularly in settings where pulmonary function testing is unavailable.