Machine Learning Prediction of Extracapsular Extension in Human Papillomavirus–Associated Oropharyngeal Squamous Cell Carcinoma

Abstract

Objective To determine whether machine learning (ML) can predict the presence of extracapsular extension (ECE) prior to treatment, using common oncologic variables, in patients with human papillomavirus (HPV)–associated oropharyngeal squamous cell carcinoma (OPSCC). Study Design Retrospective database review. Setting National Cancer Database study. Methods All patients with HPV-associated OPSCC treated surgically between January 1, 2010, and December 31, 2015, were selected from the National Cancer Database. Patients were excluded if surgical pathology reports did not include information regarding primary tumor stage, number of metastatic regional lymph nodes, size of largest metastatic regional lymph node, and tumor grade. The data were split into a random distribution of 80% for training and 20% for testing with ML methods. Results A total of 3753 adults with surgically treated HPV-associated OPSCC met criteria for inclusion in the study. Approximately 38% of these patients treated with surgical management demonstrated ECE. ML models demonstrated modest accuracy in predicting ECE, with the areas under the receiver operating characteristic curves ranging from 0.58 to 0.68. The conditional inference tree model (0.66) predicted the metastatic lymph node number to be the most important predictor of ECE. Conclusion Despite a large cohort and the use of ML algorithms, the power of clinical and oncologic variables to predict ECE in HPV-associated OPSCC remains limited.