Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma.

Background There have been few recent advances in the identification of occult lymph node metastases (OLNM) in oral squamous cell carcinoma (OSCC). This study aimed to develop, compare, and validate several machine learning models to predict OLNM in clinically N0 (cN0) OSCC. Methods The biomarkers CD31 and PROX1 were combined with relevant histological parameters and evaluated on a training cohort (n = 56) using four different state-of-the-art machine learning models. Next, the optimized models were tested on an external validation cohort (n = 112) of early-stage (T1-2 N0) OSCC. Results The random forest (RF) model gave the best overall performance (area under the curve = 0.89 [95% CI = 0.8, 0.98]) and accuracy (0.88 [95% CI = 0.8, 0.93]) while maintaining a negative predictive value >95%. Conclusions We provide a new clinical decision algorithm incorporating risk stratification by an RF model that could significantly improve the management of patients with early-stage OSCC.