Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer

Abstract

Objective: We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography.

Study design: This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE).

Results: For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98.

Conclusions: Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.