Early-stage human papilloma virus (HPV)-driven oropharyngeal cancer has excellent survival outcomes. The current therapeutic strategies aim to reduce treatment-related morbidity: transoral robotic surgery (TORS) +/- adjuvant (chemo)radiotherapy, or upfront (chemo)radiotherapy +/- salvage surgery. Greatest morbidity is associated with tri-modality therapy, and identification of patients destined to tri-modality therapy is critical to negate unnecessary morbidity. Extranodal extension (ENE) remains a primary indication for adjuvant chemoradiotherapy. Therefore, pre-treatment identification of occult ENE would allow patients to be directed towards optimal primary oncological management.
To identify CT, PET CT, and clinical parameters that predict ENE in early-stage HPV-driven oropharyngeal cancer.
Methods: Retrospective collation of clinical, radiological and pathological data was conducted on all patients with early-stage HPV-driven oropharyngeal cancer (cT1-3N0-2), without clinical or radiological evidence of ENE, undergoing TORS between January 2016 and September 2021. Pre-operative PET CTs were re-evaluated to establish standard uptake value (SUVmax), metabolic tumour volume, total lesion glycolysis, and uptake patterns in primary and nodal disease. Risk of ENE was evaluated with respect to clinical and PET CT parameters.
Results: Of the 75 patients fitting inclusion criteria, pathological nodal disease was identified in 59 patients and ENE was identified in 16 individuals. Mean age was 61 years, with a male predominance (M:F ratio = 4.77:1). Univariate analysis of all factors highlighted that indistinct nodal margins on CT imaging was associated with ENE (OR=12.3, CI 95% = 1.25-121.3, p=0.0312). Multivariate analysis performed on all factors identified increasing age (p=0.046), combined tongue base and tonsil tumours (p=0.023), indistinct nodal margins (p=0.022) and higher nodal SUVmax (p=0.030) were all associated with increased risk of ENE. A high volume of variance was accounted for by the multivariate analysis model, with an AUC of 0.89.
Conclusions: This study indicates that clinical and PET CT parameters are able to predict the presence of ENE, having implications for optimising patient outcomes. A nomogram of significant factors has been compiled in Figure 1.