Objective <p>Calcific tendinopathy, predominantly affecting rotator cuff tendons, leads to significant pain and tendon degeneration. Although US-guided percutaneous irrigation (US-PICT) is an effective treatment for this condition, prediction of patient’ s response and long-term outcomes remains a challenge. This study introduces a novel radiomics-based model to forecast patient outcomes, addressing a gap in the current predictive methodologies.</p> Materials and methods <p>The study involved 84 patients who underwent US-PICT, with data collected on clinical and demographic factors, alongside radiomic features extracted from ultrasound images. Key radiomic features predictive of the outcome were discerned through Least Absolute Shrinkage and Selection Operator (LASSO) method. Machine Learning models, including Random Forest, XGBoost, and Support Vector Machines, were employed to analyze the radiomics, the clinical and the combined dataset, focusing on calcium removal extent. An external testing was conducted using an independent cohort from a different institution to assess the model’s generalizability.&#xa0;Metrics were calculated for the best-performing models, namely area under the curve (AUC) score, sensitivity, specificity, precision or positive predictive value, and negative predictive value.</p> Results <p>The selected features were merged with clinical data, notably the calcification’s maximum diameter. This enriched dataset was fed into classification models. The superior model achieved an AUC of 0.88 (95% CI 0.73–0.99), with a positive predictive value of 0.92 and a sensitivity of 0.90. In external testing, the combined model achieved an AUC of 0.78. SHAP analysis was employed to highlight the impact of the selected features on the optimal model’s effectiveness.</p> Conclusion <p>The developed radiomics model offers a promising tool for predicting outcomes of US-PICT, potentially guiding clinical decision-making.</p>