Establishment and validation of a recurrence risk model in early-stage tongue squamous cell carcinoma patients incorporating immune-inflammatory biomarkers and clinicopathological parameters.

Abstract

Objective: To develop and validate a machine learning-based predictive model incorporating immuno-inflammatory biomarkers and clinicopathological parameters to predict recurrence risk in early-stage tongue squamous cell carcinoma (TSCC) patients.

Methods: This retrospective study included 515 early-stage TSCC patients treatment at Xinyu People's Hospital between May 2014 and May 2019. Medical records and laboratory data were reviewed. Patients were randomly divided into a training cohort (n=339) and a validation cohort (n=176). Feature selection was performed using LASSO, Xgboost, and Support Vector Machine (SVM) algorithms to identify key features associated with recurrence. A predictive nomogram was then built based on multivariate Cox regression analysis. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).

Results: Recurrence was observed in 160 cases (31.07%), with 111 (32.74%) in training cohort (n=339) and 49 (27.84%) in the validation cohort (n=176). Machine learning algorithms identified several key risk factors for recurrence, including immuno-inflammatory markers (e.g., white blood cell count [WBC], platelet count [PLT], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], systemic inflammation response index [SIRI], C-reactive protein-to-albumin ratio [CAR]) and clinicopathological characteristics (e.g., pathological classification, chemotherapy status, tumor location). The nomogram achieved areas under the ROC curve (AUCs) of 0.902 (95% CI: 0.866-0.937) in the training set and 0.819 (95% CI: 0.759-0.876) in the validation set. Calibration curves demonstrated good predictive consistency (P=0.621). DCA showed a clear net clinical benefit across a wide range of thresholds probabilities (P<0.001).

Conclusion: This predictive model, integrating immuno-inflammatory markers and clinicopathological features, exhibits excellent predictive performance for recurrence risk in early-stage STCC and offers substantial clinical utility.