Search

Objective: The American Joint Committee on Cancer (AJCC) 8th edition introduced distinct clinical and pathological staging paradigms for human papilloma virus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC). Treatment planning for OPSCC often utilizes positron emission tomography/computed tomography (PET/CT) to assess clinical stage. We hypothesize that PET/CT will accurately predict final pathologic AJCC 8th edition staging in patients with HPV+ OPSCC., Methods: All patients with primary HPV+ OPSCC with preoperative PET/CT who underwent transoral robotic surgery and neck dissection between 2011 and 2017 were identified. Data were collected via chart review. Two neuroradiologists performed blinded re-evaluation of all scans. Primary tumor size and cervical nodal disease characteristics were recorded and TNM staging was extrapolated. Cohen's kappa statistic was used to assess interrater reliability. Test for symmetry was performed to analyze discordance between radiologic and pathologic staging., Results: Forty-nine patients met inclusion criteria. Interrater reliability was substantial between radiologists for nodal (N) and overall staging (OS) (κ = 0.715 and 0.715). Radiologist A review resulted in identical OS for 67% of patients, overstaging for 31%, and understaging for 2%. Radiologist B review resulted in 61% identical OS, 39% overstaging, and 0% understaging. In misclassified cases, the test of symmetry shows strong bias toward overstaging N stage and OS (P < .001). Radiologic interpretation of extracapsular extension showed poor interrater reliability (κ = 0.403) and poor accuracy., Conclusion: PET/CT predicts a higher nodal and overall stage than pathologic staging. PET/CT should not be relied upon for initial tumor staging, as increased FDG uptake is not specific for nodal metastases. PET/CT is shown to be a poor predictor of ECE., Level of Evidence: 4 Laryngoscope, 131:1535-1541, 2021., (© 2021 The American Laryngological, Rhinological and Otological Society, Inc.)

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.

Background/aim: We evaluated the diagnostic value of functional imaging with [ 18 F]-fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography/computed tomography (PET/CT) for the identification of extranodal extension (ENE) in patients with head and neck squamous cell carcinoma (HNSCC)., Patients and Methods: In this study, 94 patients with HNSCC who underwent FDG-PET/CT were enrolled. We recorded the maximum standardized uptake value (SUV max ), compared the results with pathologic findings, and evaluated the diagnostic performance of using a SUV max cut-off value for ENE., Results: Of the 566 dissected levels examined, 53 (9.4%) exhibited ENE. The mean SUV max of LN with and without ENE were 6.67 and 1.64, respectively (p<0.001). A receiver operating characteristics (ROC) curve analysis for SUV max showed an area under the ROC curve of 0.913. A SUV max cut-off of 3.0 achieved diagnostic performance for identifying ENE with sensitivity, specificity, and accuracy of 81.1%, 94.3% and 93.1%, respectively., Conclusion: FDG-PET/CT findings using a SUV max cut-off of 3.0 provides appropriate diagnostic value in identifying ENE., (Copyright© 2020, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

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.)