Radiomics in Otolaryngology–Head and Neck Surgery

The results of this Review suggest that radiomics is a promising tool that can be integrated with clinical and pathologic data to enhance diagnosis, optimize prognostication, and personalize treatment in otolaryngology. Standardization of imaging protocols, rigorous validation in multi-institutional cohorts, and integration with clinical workflows remain critical prerequisites for clinical application. With continued refinement and integration, radiomics applications may help streamline clinical workflows and guide treatment planning.

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Publication Details

Journal
JAMA Otolaryngology–Head & Neck Surgery
Published
2026-02-12
DOI
https://doi.org/10.1001/jamaoto.2025.5462
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Radiomics in Otolaryngology–Head and Neck Surgery

Alexandra T. Bourdillon, Andrés M. Bur, Alexandria Yao
JAMA Otolaryngology–Head & Neck Surgery
Radiomics and Machine Learning in Medical Imaging
article

Radiomics in Otolaryngology–Head and Neck Surgery

Alexandra T. Bourdillon, Andrés M. Bur, Alexandria Yao
article en

Abstract

The results of this Review suggest that radiomics is a promising tool that can be integrated with clinical and pathologic data to enhance diagnosis, optimize prognostication, and personalize treatment in otolaryngology. Standardization of imaging protocols, rigorous validation in multi-institutional cohorts, and integration with clinical workflows remain critical prerequisites for clinical application. With continued refinement and integration, radiomics applications may help streamline clinical workflows and guide treatment planning.

JAMA Otolaryngology–Head & Neck Surgery
University of Kansas (US), University of California, San Francisco (US)
Openalex Percentile: Top 58%
Radiomics and Machine Learning in Medical Imaging
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