Oral oncology · 2025
Identification of clinical and pathologic features associated with extranodal extension in patients with HPV-mediated oropharyngeal squamous cell carcinoma.
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Olson Brennan, Bogan Aaron, Abdel-Halim Chadi N, Rolle-McFarland Danelle, Graves Jeffrey, Vallecillo Tissiana, Ma Daniel J, Neben-Wittich Michelle A, Lester Scott C, Gamez Mauricio, Holtzmann Adam, Mohammadi Homan, Garcia Joaquin, Chintakuntlawar Ashish, Price Katharine A, Fuentes Bayne Harry E, Rwigema Jean Claude M, Patel Samir H, McGee Lisa A, Patel Samip, Janus Jeffrey R, Chang Brent, Hinni Michael L, Tasche Kendall T, Yin Linda X, Moore Eric J, Price Daniel L, Van Abel Kathryn M, Routman David M
Аннотация
BACKGROUND: Extranodal extension (ENE) is an important prognostic factor that is not ascertainable prior to surgical resection of involved lymph node(s) or may not be available in patients with prior excisional biopsies.
METHODS: This study involved a retrospective analysis of 900 patients diagnosed with HPV(+)OPSCC and treated with primary surgery at a single tertiary care center. Patient demographics, comorbidities, and pathologic characteristics of disease were collected and analyzed. Pathology reports were reviewed to identify ENE. Univariate and multivariate logistic regression models were used to identify factors associated with odds of ENE.
RESULTS: ENE was identified in 506/900 (56.2 %) patients. On univariate analysis, increased Adult Comorbidity Evaluation - 27 (p = 0.013), pathologic T-stage (p = 0.012), pathologic N-stage (p < 0.001), count of involved nodes (p < 0.001), size of largest involved node (p < 0.001), and perineural invasion (p = 0.001) were found to be positively associated with increased odds of ENE. On multivariate analysis, count of involved nodes (p < 0.001), size of largest involved node (p < 0.001), and perineural invasion (p = 0.021) were positively associated with increased odds of ENE. Although PNI improved the model's predictive performance statistically, its exclusion produced similar results. Both predictive models have been made publicly available (https://rtools.mayo.edu/prime_ene_prediction/).
CONCLUSION: Increased count of involved lymph nodes, size of largest involved lymph node, and presence of perineural invasion in patients with HPV(+)OPSCC are strongly associated with increased odds of ENE. These data suggest that ENE presence can be reliably predicted using the number and size of involved nodes. Such a predictive model could prove a useful tool in informing treatment strategies for HPV(+)OPSCC patients prior to surgical resection of involved lymph node(s) and those without sufficient pathologic ENE data from prior excisional biopsies.
METHODS: This study involved a retrospective analysis of 900 patients diagnosed with HPV(+)OPSCC and treated with primary surgery at a single tertiary care center. Patient demographics, comorbidities, and pathologic characteristics of disease were collected and analyzed. Pathology reports were reviewed to identify ENE. Univariate and multivariate logistic regression models were used to identify factors associated with odds of ENE.
RESULTS: ENE was identified in 506/900 (56.2 %) patients. On univariate analysis, increased Adult Comorbidity Evaluation - 27 (p = 0.013), pathologic T-stage (p = 0.012), pathologic N-stage (p < 0.001), count of involved nodes (p < 0.001), size of largest involved node (p < 0.001), and perineural invasion (p = 0.001) were found to be positively associated with increased odds of ENE. On multivariate analysis, count of involved nodes (p < 0.001), size of largest involved node (p < 0.001), and perineural invasion (p = 0.021) were positively associated with increased odds of ENE. Although PNI improved the model's predictive performance statistically, its exclusion produced similar results. Both predictive models have been made publicly available (https://rtools.mayo.edu/prime_ene_prediction/).
CONCLUSION: Increased count of involved lymph nodes, size of largest involved lymph node, and presence of perineural invasion in patients with HPV(+)OPSCC are strongly associated with increased odds of ENE. These data suggest that ENE presence can be reliably predicted using the number and size of involved nodes. Such a predictive model could prove a useful tool in informing treatment strategies for HPV(+)OPSCC patients prior to surgical resection of involved lymph node(s) and those without sufficient pathologic ENE data from prior excisional biopsies.