Morphology-based Machine-Learning for Predicting Lymph Node Status in Oral Tongue Squamous Cell Carcinoma

Abstract

Purpose To develop machine-learning models based on morphological features extracted from preoperative magnetic resonance imaging (MRI) to predict lymph node status in oral tongue squamous cell carcinoma (OTSCC). Method This study retrospectively enrolled 90 OTSCC patients, of whom 45 and 13 patients, respectively, had confirmed lymph node metastasis (LNM) and extranodal extension (ENE). Fourteen morphological features and two customized metrics were derived from T2-weighted (T2W) images. Tumor maximum diameter and MRI-derived depth of invasion (DOI) were measured on contrast-enhanced T1-weighted (ceT1W) images. Information gain algorithm was applied to select the top five attributes. Models were created using six machine-learning methods, including neural network (NN), random forest (RF), logistic regression (LR), support vector machine (SVM), naïve bayes (NB), and AdaBoost. An internal stratified 10-fold cross-validation was performed to assess their performance. Results For predicting LNM, the NN classifier, which included Situation, Elongation, Top Bottom Area, Least Axis Length, and Minor Axis Length, yielded the best model, with an AUC of 0.746 and accuracy of 72.2%. The performance of the NN model was slightly superior to that of MRI-derived DOI (0.746 vs. 0.655), although the difference was not significant (P = 0.122). For predicting ENE, the SVM classifier, which included situation, Elongation, Top Bottom Area, Least Axis Length, and Minor Axis Length, performed the best, with an AUC of 0.750 and accuracy of 85.6%. Conclusions Machine-learning models using MRI morphological features have potential in preoperative evaluation of cervical lymph node status in OTSCC.