Interpretable machine learning distinguishes skip from continuous metastasis in N1b papillary thyroid carcinoma.

Skip metastasis-defined as lateral lymph node metastasis(N1b) in the absence of central lymph node involvement-represents a distinct yet underrecognized metastatic pattern in papillary thyroid carcinoma (PTC). Accurate identification of skip metastasis is clinically important. However, reliable tools to distinguish skip from continuous metastasis in N1b PTC remain limited. We developed and validated prediction models to discriminate skip from continuous metastasis using a multi-cohort design. A retrospective cohort of 739 patients with N1b PTC who underwent routine bilateral CND with lateral neck dissection was used for model development. Ten machine learning algorithms were trained using clinically available variables and evaluated through internal validation and two independent external validation cohorts. Model performance was assessed in terms of discrimination, calibration, clinical utility, and risk reclassification. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP). Skip metastasis was identified in 14.2% of N1b PTC patients. Among all candidate models, the XGBoost algorithm consistently demonstrated superior and stable discrimination across development and validation cohorts, achieving the highest area under the receiver operating characteristic and precision-recall curves. Calibration analyses showed good agreement between predicted and observed risks, and decision curve analysis demonstrated a favorable net clinical benefit across clinically relevant threshold probabilities. Compared with alternative models, XGBoost provided significant improvements in patient-level risk stratification, as reflected by positive net reclassification improvement and integrated discrimination improvement in both internal and external validations. SHAP analyses revealed that the number of central lymph nodes dissected, number of metastatic lateral lymph nodes, tumor location, age, and tumor size were the most influential predictors, capturing clinically coherent and non-linear risk patterns. We developed and externally validated an interpretable and clinically deployable machine learning model to distinguish skip from continuous metastasis in N1b PTC. This model enables individualized risk assessment and may support tailored surgical decision-making. Larger and more prospective studies are warranted to evaluate its impact on clinical outcomes.