Enhancing prediction of primary site recurrence in head and neck cancer using radiomics and uncertainty estimation.

Yu Hu¹, Kimberly Taing¹, Jing Wang¹, David Sher¹, Michael Dohopolski¹

¹Medical Artificial Intelligence and Automation Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States.

PubMed PMC DOI PDF

Abstract

INTRODUCTION: Head and neck squamous cell carcinomas (HNSCC) present a significant clinical challenge due to high recurrence rates despite advances in radiation and chemotherapy. Early detection of recurrence is critical for optimizing treatment outcomes and improving patient survival. METHODS: We developed two artificial intelligence (AI) pipelines-(1) machine learning models trained on radiomic…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.