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Extranodal extension of tumour on histopathology is known to be a negative prognostic factor in head and neck cancer. Compelling evidence suggests that extranodal extension detected on radiological imaging is also a negative prognostic factor. Furthermore, if imaging detected extranodal extension could be identified reliably before the start of treatment, it could be used to guide treatment selection, as patients might be better managed with non-surgical approaches to avoid the toxicity and cost of trimodality therapy (surgery, chemotherapy, and radiotherapy together). There are many aspects of imaging detected extranodal extension that remain unresolved or are without consensus, such as the criteria to best diagnose them and the associated terminology. The Head and Neck Cancer International Group conducted a five-round modified Delphi process with a group of 18 international radiology experts, representing 14 national clinical research groups. We generated consensus recommendations on the terminology and diagnostic criteria for imaging detected extranodal extension to harmonise clinical practice and research. These recommendations have been endorsed by 19 national and international organisations, representing 34 countries. We propose a new classification system to aid diagnosis, which was supported by most of the participating experts over existing systems, and which will require validation in the future. Additionally, we have created an online educational resource for grading imaging detected extranodal extensions., Competing Interests: Declaration of interests HM is the director and a shareholder of Warwickshire Head and Neck Clinic; Chair of the Head and Neck Cancer International Group; past President of the British Association of Head and Neck Oncologists; has received honoraria from AstraZeneca; served on speakers bureau for Merck Sharpe & Dohme (MSD) and Sanofi Pasteur; has received research funding from GSK Biologicals, MSD, Sanofi Pasteur, GSK, and AstraZeneca; and has received travel and accommodation expenses from Sanofi, Pasteur, and MSD. CG received royalties from Elsevier for authoring and editing books and chapters for Elsevier. WML is the Chair of the American Joint Committee on Cancer (ninth version) Head and Neck Task Force. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Objectives/hypothesis: To assess the ability of specific positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) features to detect extracapsular extension (ECE) in head and neck squamous cell carcinoma (HNSCC) patients., Study Design: Retrospective study in a tertiary certified university cancer institute., Methods: We performed a review of patients with advanced HNSCC at Bern University Hospital between 2014 and 2018. Patients with pretherapeutic PET/CT and/or MRI who underwent neck dissection were included, with 212 patients fulfilling inclusion criteria. Blinded evaluation of specific PET/CT and MRI features with respect to presence of ECE was performed. Histopathological examination of neck dissection specimens was used as the gold standard to determine ECE status., Results: Out of the 212 included patients, 184 had PET/CT, 186 MRI, and 158 both modalities. Overall clinical stage IV (odds ratio [OR]: 2.26, 95% confidence interval [CI]: 2.25-11.74), ill-defined margins in both PET/CT and MRI (OR: 3.48, 95% CI: 1.21-9.98 and OR: 2.14, 95% CI: 0.94-4.89, respectively), and a maximum standardized uptake value ≥ 10 (OR: 5.44, 95% CI: 1.21-9.98) were all significant independent predictors of ECE. When combined, these four features led to a cumulative score able to predict ECE status with an accuracy of 91.43%., Conclusions: The current findings indicate specific features in PET/CT and MRI are potential predictors of ECE status and may help in pretherapeutic stratification in HNSCC., Level of Evidence: 4 Laryngoscope, 131:E163-E169, 2021., (© 2020 The American Laryngological, Rhinological and Otological Society, Inc.)

Purpose: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians., Methods: We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists., Results: A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 ( P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 ( P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance., Conclusion: Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.

Objective: To determine whether radiologic extranodal extension (ENE) appearing on pretreatment CT and MRI could predict the prognosis in patients with human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC)., Materials and Methods: The study population was obtained from a historical cohort diagnosed with HPV-related OPSCC. A total of 134 OPSCC patients who had a metastatic lymph node on pretreatment CT or MRI were included, and radiologic ENE was evaluated by two experienced head and neck radiologists. Kaplan-Meier and multivariate Cox regression analyses were performed to evaluate the impact of radiologic ENE on progression-free survival (PFS). The diagnostic performance of CT and MRI for the diagnosis of ENE was also evaluated in patients who underwent neck dissection., Results: Seventy patients (52.2%) showed radiologic ENE-positive findings. Although patients showing radiologic ENE had a worse 3-year PFS (83.7% vs. 95.3%, p = 0.023), the association between radiologic ENE and PFS was not statistically significant on multivariate analysis ( p = 0.141; hazard ratio, 2.68; 95% confidence interval, 0.72-9.97). CT or MRI had a sensitivity of 62%, specificity of 77.8%, and accuracy of 71.9% for predicting pathologic ENE., Conclusion: Radiologic ENE on CT or MRI did not predict poor PFS in patients with HPV-related OPSCC, although there was a trend towards worse PFS. Further studies are warranted to determine whether radiologic ENE is a useful imaging biomarker to risk-stratify patients with HPV-related OPSCC., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2019 The Korean Society of Radiology.)