Non-invasive prediction of lymph node risk in oral cavity cancer patients using a combination of supervised and unsupervised machine learning algorithms

In oral cavity (OC) squamous cell cancer, the incidence of occult nodal metastases varies from 20% to 50% depending and tumor size and thickness. Besides clinical and histopathological factors, image-derived biomarkers may help estimate the probability of LN (lymph nodes) metastasis using a non-invasive approach to further stratify patients' need for neck dissection. We investigated the role of MR-based radiomics in predicting positive lymph nodes in OC patients, prior to surgery. We also investigated different supervised and unsupervised dimensionality reduction techniques, as well as different classifiers. Results showed that the combination of radiomics+clinical factors outperform radiomics and clinical predictors alone. Overall, a combination of supervised and supervised machine learning algorithms seems more suitable for better performances in radiomic studies