ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1623393
This article is part of the Research TopicArtificial Intelligence-based Multimodal Imaging and Multi-omics in Medical ResearchView all 7 articles
Enhancing Prediction of Primary Site Recurrence in Head and Neck Cancer Using Radiomics and Uncertainty Estimation
Provisionally accepted
- University of Texas Southwestern Medical Center, Dallas, United States
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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. In this study, we developed two artificial intelligence (AI) pipelines-(1) machine learning models trained on radiomic and clinical data and (2) a Vision Transformer-based model directly applied to imaging data-to predict HNSCC recurrence using pre-and post-treatment PET/CT scans from a cohort of 249 patients. We incorporated Test-Time Augmentation (TTA) and Conformal Prediction to quantify prediction uncertainty and enhance model reliability. The machine learning models demonstrated strong predictive performance, with an average AUC of 0.820. The vision transformer model showed moderate performance (AUC = 0.658). Uncertainty analysis improved model accuracy by excluding ambiguous cases, highlighting the potential of combining radiomics with uncertainty estimation for more precise HNSCC recurrence prediction.
Keywords: head and neck cancer, Medical Image Analysis, machine learning, feature and model selection, SAM-Med3D, Test-time augmentation, Conformal Prediction
Received: 05 May 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Hu, Taing, Wang, Sher and Dohopolski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Michael Dohopolski, University of Texas Southwestern Medical Center, Dallas, United States
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