Refining Prognostic Predictions and Risk Assessment in Head and Neck Rhabdomyosarcoma

This study aims to develop and validate prognostic models for predicting overall survival (OS) and disease-specific survival (DSS) in patients with head and neck rhabdomyosarcoma (HNRMS), providing a practical tool for clinical decision-making.

A retrospective cohort of patients diagnosed with HNRMS between 2000 and 2021 was analyzed. Prognostic factors were identified through Cox regression, and nomograms were constructed for survival. Model performance was rigorously assessed using the concordance index (C-index), time-dependent ROC curves, calibration curve, and decision curve analysis (DCA).

A total of 1271 patients with HNRMS were included, with 889 patients in the training set and 382 in the validation set. The median OS was 16.9 months, and the median DSS was not reached. Advanced age, alveolar histology, para-meningeal tumor location, distant metastasis, and lack of radiotherapy emerged as key adverse prognostic factors. The constructed nomograms demonstrated strong predictive performance, with C-index values of 0.759 for OS and 0.743 for DSS in the training cohort, and 0.743 for OS and 0.730 for DSS in the validation cohort. Time-dependent ROC analysis further confirmed the superior accuracy of the nomograms compared to traditional staging systems. Risk stratification classified patients into low-, medium-, and high-risk groups, with 5-year OS rates of 81.7%, 40.2%, and 12.0%, respectively, highlighting the model's clinical utility.

Our nomograms provide a highly accurate and clinically actionable tool for individualized prognostication in HNRMS, addressing the limitations of conventional staging systems. This advancement holds significant promise for personalized treatment strategies and improved patient outcomes.