Correlation between head and neck radiologists reporting on extranodal extension detected on radiological imaging: A head and neck cancer international group multinational study

INTRODUCTION: Extranodal extension detected on imaging (iENE) is an increasingly recognised risk factor for poor prognosis in patients with head and neck squamous cell carcinoma. However, its clinical usefulness depends on accurate and reliable radiological assessment. A major limitation to the use of iENE as a prognostic marker remains variability in its interpretation, attributable in part to the lack of standardised diagnostic criteria. METHODS: This international, multicentre reliability study included expert head and neck radiologists nominated to represent member groups of the Head and Neck Cancer International Group (HNCIG). Participants underwent standardised training using the HNCIG consensus classification and educational atlas, and then independently evaluated anonymised CT and MRI scans using both binary (iENE present or absent) and ordinal (grades 0-3) classifications over two rounds. Inter- and intra-rater reliability were evaluated using free-marginal multi-rater kappa statistics and intraclass correlation coefficients. Subgroup analyses were performed by imaging modality and radiologist experience. RESULTS: Fourteen radiologists representing 12 research groups, spanning 25 countries, completed both assessment rounds. Overall inter-rater agreement was moderate for binary classification (κ = 0.75, 95% CI 0.66-0.84) and weak for ordinal classification (κ = 0.49, 95% CI 0.42-0.56). Intra-rater agreement was moderate for binary classification and moderate to strong for ordinal classification. Agreement levels did not differ significantly by imaging modality or years of experience. CONCLUSION: Reliable radiological assessment is necessary for the use of iENE in clinical trials, staging, and routine decision making. Using the HNCIG consensus classification in a binary format produced consistent results across international expert radiologists and imaging modalities, supporting its wider use. However, substantial variability remains in grading the extent of iENE, indicating the need for further training support.

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Journal
Oral Oncology
Published
2026-04-26
DOI
https://doi.org/10.1016/j.oraloncology.2026.107980
Primary Topic
Dental Radiography and Imaging
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article
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article

Correlation between head and neck radiologists reporting on extranodal extension detected on radiological imaging: A head and neck cancer international group multinational study

Ahmad K. Abou‐Foul, Hisham Mehanna, Barton F. Branstetter, Caroline Kristunas et al.
Oral Oncology
Dental Radiography and Imaging
article

Correlation between head and neck radiologists reporting on extranodal extension detected on radiological imaging: A head and neck cancer international group multinational study

Ahmad K. Abou‐Foul, Hisham Mehanna, Barton F. Branstetter, Caroline Kristunas, E Santos-Armentia, Eloísa Maria Mello Santiago Gebrim, C. Henson, Xin Wu, Santiago Medrano-Martorell, Hirofumi Kuno, J.W. Dankbaar, William M. Lydiatt, Shao Hui Huang, Y. Xiao, A.D. King, D. Andrew, F. Bidault, J.P.N. Goh, E. Yu, P. de Graaf, J. Kim, D.G. Schafigh
article en

Abstract

INTRODUCTION: Extranodal extension detected on imaging (iENE) is an increasingly recognised risk factor for poor prognosis in patients with head and neck squamous cell carcinoma. However, its clinical usefulness depends on accurate and reliable radiological assessment. A major limitation to the use of iENE as a prognostic marker remains variability in its interpretation, attributable in part to the lack of standardised diagnostic criteria. METHODS: This international, multicentre reliability study included expert head and neck radiologists nominated to represent member groups of the Head and Neck Cancer International Group (HNCIG). Participants underwent standardised training using the HNCIG consensus classification and educational atlas, and then independently evaluated anonymised CT and MRI scans using both binary (iENE present or absent) and ordinal (grades 0-3) classifications over two rounds. Inter- and intra-rater reliability were evaluated using free-marginal multi-rater kappa statistics and intraclass correlation coefficients. Subgroup analyses were performed by imaging modality and radiologist experience. RESULTS: Fourteen radiologists representing 12 research groups, spanning 25 countries, completed both assessment rounds. Overall inter-rater agreement was moderate for binary classification (κ = 0.75, 95% CI 0.66-0.84) and weak for ordinal classification (κ = 0.49, 95% CI 0.42-0.56). Intra-rater agreement was moderate for binary classification and moderate to strong for ordinal classification. Agreement levels did not differ significantly by imaging modality or years of experience. CONCLUSION: Reliable radiological assessment is necessary for the use of iENE in clinical trials, staging, and routine decision making. Using the HNCIG consensus classification in a binary format produced consistent results across international expert radiologists and imaging modalities, supporting its wider use. However, substantial variability remains in grading the extent of iENE, indicating the need for further training support.

Oral OncologyVol. 178
Fujian Medical University (CN), University of Pittsburgh (US), Chinese University of Hong Kong (HK), University of California, San Francisco (US), University of Cologne (DE), Université Paris-Saclay (FR), Nebraska Methodist College (US), Institut Gustave Roussy (FR), Tan Tock Seng Hospital (SG), Princess Margaret Cancer Centre (CA), University Medical Center Utrecht (NL), Amsterdam Neuroscience (NL), Prince of Wales Hospital (CN), Hospital Clínic de Barcelona (ES), Povisa Hospital (ES), National Cancer Center Hospital East (JP), Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (BR), Fujian Provincial Cancer Hospital (CN), Amsterdam University Medical Centers (NL), University of Oklahoma Health Sciences Center (US), University of Birmingham (GB), Vrije Universiteit Amsterdam (NL), University of Hong Kong (HK), University of Sheffield (GB)
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Openalex Percentile: Top 17%
Dental Radiography and Imaging
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