Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool.

ABSTRACT: Estimation of risk of recurrence in early-stage oral tongue squamous cell carcinoma (OTSCC) remains a challenge in the field of head and neck oncology. We examined the use of artificial neural networks (ANNs) to predict recurrences in early-stage OTSCC. A Web-based tool available for public use was also developed. A feedforward neural network was trained for prediction of locoregional recurrences in early OTSCC. The trained network was used to evaluate several prognostic parameters (age, gender, T stage, WHO histologic grade, depth of invasion, tumor budding, worst pattern of invasion, perineural invasion, and lymphocytic host response). Our neural network model identified tumor budding and depth of invasion as the most important prognosticators to predict locoregional recurrence. The accu

SUBMITTER: Alabi RO 

PROVIDER: S-EPMC6828835 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature