Quantitative Super-Resolution Ultrasound Microvascular Features for Machine Learning-Based Classification of Thyroid Nodules

This study compared five machine learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Gradient Boosting (GB)-for classifying thyroid nodules using quantitative features derived from conventional ultrasound, contrast-enhanced ultrasound (CEUS), and super-resolution ultrasound (SRUS). The retrospective study included 68 thyroid nodules from 63 patients (30 benign and 38 confirmed as papillary thyroid carcinoma [PTC]) and was analyzed using 25 quantitative features via five-fold patient-grouped cross-validation, ensuring that all nodules from the same patient were assigned to the same fold. Model performance was summarized across folds and from pooled out-of-fold (OOF) predictions, and a focused OOF SHapley Additive exPlanations (SHAP) analysis using a permutation-based explainer was performed for the best-performing SVM. SVM achieved the highest mean accuracy (0.686 ± 0.120) and mean Receiver Operating Characteristic-Area Under Curve (ROC-AUC) (0.690 ± 0.164), with a pooled OOF sensitivity of 0.842 and specificity of 0.500, while microcalcification and post-contrast enlargement were identified as the most significant model-specific SHAP contributors. RF offered balanced performance (accuracy 55.9%, sensitivity 55.3%, specificity 56.7%), whereas DT performed poorly (accuracy 0.429, ROC-AUC 0.447), approaching random guessing. The study demonstrated that quantitative SRUS measurements can be incorporated into conventional classifiers; however, the findings remain exploratory and require prospective, multicenter validation with larger cohorts before clinical implementation.