Predictors of Outcome after (Chemo)Radiotherapy for Node-Positive Oropharyngeal Cancer: The Role of Functional MRI

Simple Summary We have conducted a prospective study on patients with locally advanced oropharyngeal cancer who are candidates for concomitant radio-chemotherapy; we considered their anamnestic findings, tumor characteristics and evaluated the role of innovative radiological features, particularly the magnetic resonance imaging (MRI) biomarkers. Our aim was to identify those elements correlated with worse tumor control. Diffusion-weighted (DWI) imaging and dynamic contrast-enhanced (DCE) can help identify hypoxic regions in head and neck cancer which are known to be more resistant to the effects of radiation. A better understanding of these factors may help us improve our knowledge on tumor behavior and thus provide a more tailored treatment in patients that respond poorly. The prognosis of a subset of patients with locally advanced oropharyngeal cancer (LA-OPC) is still poor despite improvements in patient selection and treatment. Identifying specific patient- and tumor-related factors can help to select those patients who need intensified treatment. We aimed to assess the role of historical risk factors and novel magnetic resonance imaging (MRI) biomarkers in predicting outcomes in these patients. Patients diagnosed with LA-OPC were studied with diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline and at the 10th radiotherapy (RT) fraction. Clinical information was collected as well. The endpoint of the study was the development of disease progression, locally or distantly. Of the 97 patients enrolled, 68 were eligible for analysis. Disease progression was recorded in 21 patients (11 had loco-regional progression, 10 developed distant metastases). We found a correlation between N diameter and disease control (p = 0.02); features such as p16 status and extranodal extension only showed a trend towards statistical significance. Among perfusion MRI features, higher median values of K-ep both in primary tumor (T, p = 0.016) and lymph node (N, p = 0.003) and lower median values of v(e) (p = 0.018 in T, p = 0.004 in N) correlated with better disease control. K-ep P90 and N diameter were identified by MRMR algorithm as the best predictors of outcome. In conclusion, the association of non-invasive MRI biomarkers and patients and tumor characteristics may help in predicting disease behavior and patient outcomes in order to ensure a more customized treatment.