NI-RADS in posttreatment head and neck cancer surveillance: a framework for standardized imaging with clinical impact

Read full text
Authors: David A. Zander, Ashley H. Aiken, Yuh-Shin Chang, Fabian Henry Jürgen Elsholtz, Ryan T. Hughes, Amy F. Tsang Juliano, Kim O. Learned, Ashok Srinivasan, Sara Strauß, Jame Wicks, Paul M. Bunch
Cited by: 1
References: 38

Abstract

The Neck Imaging Reporting and Data System (NI-RADS), developed through the American College of Radiology (ACR), provides a standardized framework for interpreting and managing posttreatment imaging in head and neck cancer. Building upon the success of NI-RADS PET/CT, the recently released NI-RADS MRI version 2025 represents a major advancement, introducing modality-specific descriptors and management recommendations tailored to MRI. This review summarizes the history and development of NI-RADS, highlighting both the validated PET/CT framework and the subsequent MRI update. At its core, NI-RADS offers a standardized lexicon for post-treatment findings, a structured reporting format that stratifies risk of disease recurrence, and linked management recommendations. Surveillance imaging is an essential component of post-treatment head and neck cancer care. Evidence strongly supports early imaging surveillance within six months of definitive therapy, whereas the benefits of long-term imaging surveillance remain under-investigated. With the goal of improving patient outcomes, NI-RADS provides a consistent, risk-adaptable framework that supports both clinical decision-making and standardized data collection for future outcomes research. NI-RADS will continue to evolve with advances in oncologic management and imaging technology. Future updates may incorporate ultrasound, advanced imaging techniques, and circulating tumor biomarkers. As surveillance strategies in head and neck cancer advance, NI-RADS is well positioned to serve as the foundation for personalized, risk-based imaging surveillance.

Fields of Study & Subjects

Head and Neck Cancer Studies Esophageal Cancer Research and Treatment Radiomics and Machine Learning in Medical Imaging

Publication details

Journal / source
Cancer Imaging
Publisher
Springer Science and Business Media LLC
Volume
26
Issue
1
Pages
8–8
Publication date
10 January 2026
Language
EN