{"title":"Radiological extranodal extension in head and neck cancers: current evidence and challenges in imaging detection and prognostic impact.","authors":"Nivedita Chakrabarty, Abhishek Mahajan","doi":"10.1093/bjro/tzaf021","DOIUrl":null,"url":null,"abstract":"<p><p>Extranodal extension (ENE) is an established adverse prognostic indicator for head and neck cancers (HNC), and its presence entails adjuvant chemoradiotherapy, hence, it had been incorporated for the first time as the advanced regional node N3b category in the 8th edition of the Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) Tumour Node Metastasis (TNM) classification for cancers of the oral cavity, human papillomavirus-negative oropharynx, hypopharynx, larynx and major salivary gland carcinomas. Pathological ENE is available for cases which are operated on, but cases which are managed non-surgically or unfit for surgery rely on imaging for providing the information on ENE, and this has prompted researchers across the globe to devise radiological grading for ENE. Radiological ENE has finally been given due credit and incorporated in the 9th version of AJCC TNM staging for nasopharyngeal carcinoma, which came into effect from January 2025. Knowledge of ENE status on baseline imaging prior to operation also helps in counselling patients regarding prognosis and planning adjuvant treatment. In this article, we have comprehensively reviewed the radiological/imaging ENE (rENE/iENE) grading proposed by researchers worldwide, extensively reviewed the existing evidence and challenges of using rENE/iENE for staging, grading, prognosticating and treating HNC, and also discussed the future scope of using rENE/iENE for managing patients with HNC of all the subsites, including thyroid cancers. Artificial intelligence-based studies for predicting rENE/iENE have also been discussed briefly.</p>","PeriodicalId":72419,"journal":{"name":"BJR open","volume":"7 1","pages":"tzaf021"},"PeriodicalIF":2.1000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12449263/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BJR open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bjro/tzaf021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Extranodal extension (ENE) is an established adverse prognostic indicator for head and neck cancers (HNC), and its presence entails adjuvant chemoradiotherapy, hence, it had been incorporated for the first time as the advanced regional node N3b category in the 8th edition of the Union for International Cancer Control (UICC)/American Joint Committee on Cancer (AJCC) Tumour Node Metastasis (TNM) classification for cancers of the oral cavity, human papillomavirus-negative oropharynx, hypopharynx, larynx and major salivary gland carcinomas. Pathological ENE is available for cases which are operated on, but cases which are managed non-surgically or unfit for surgery rely on imaging for providing the information on ENE, and this has prompted researchers across the globe to devise radiological grading for ENE. Radiological ENE has finally been given due credit and incorporated in the 9th version of AJCC TNM staging for nasopharyngeal carcinoma, which came into effect from January 2025. Knowledge of ENE status on baseline imaging prior to operation also helps in counselling patients regarding prognosis and planning adjuvant treatment. In this article, we have comprehensively reviewed the radiological/imaging ENE (rENE/iENE) grading proposed by researchers worldwide, extensively reviewed the existing evidence and challenges of using rENE/iENE for staging, grading, prognosticating and treating HNC, and also discussed the future scope of using rENE/iENE for managing patients with HNC of all the subsites, including thyroid cancers. Artificial intelligence-based studies for predicting rENE/iENE have also been discussed briefly.