The purpose of this research study is to investigate the relationship between vascular habitats and treatment response AND to investigate the potential utility of dynamic contrast enhanced (DCE)-MRI derived parameters in predicting treatment response.
This data can be used to assist in the development of a clinical decision support (CDS) tool for risk categorization and individualized decision-making. While "low-risk" patients could benefit from de-escalated radiation therapy, "high-risk" patients could receive early initiation of targeted therapies, or immunotherapies.
Adult population (≥18 years) of both genders
Biopsy confirmed diagnosis of OPSCC
Known HPV status as determined by p16 immunohistochemistry (p16 positivity defined as >70% of carcinoma cells showing nuclear reactivity) or polymerase chain reaction (PCR) sequencing
Prior to any treatment except for incisional or excisional biopsies
HPV- negative OPSCC
Presence of distant metastatic (M1) disease
Pregnancy
Existence of significant co-morbidities at baseline which would prevent standard CRT treatment
Single group
MRI
Diagnostic testMagnetic Resonance Imaging (MRI) is a type of scan that uses radio waves to take detailed pictures of the body. You will be asked to lie on an MRI table where the technologist will place a receiver on the part of your body to be studied. You will be provided a blanket for comfort and earplugs since the MRI makes noises while it is scanning. You will still be able to hear some sound to ensure you can communicate with the technologist and can follow any direction given throughout the MRI scan. The technologist will slowly slide you into the MRI magnet where radio waves will be transmitted into you.
Time to Progression
Determination of the clinical utility of pretreatment Diffusion Weighted Imaging (DWI) and Dynamic Contrast Enhanced-MRI (DCE-MRI) derived parameters in predicting response to standard Chemo-radiotherapy (CRT) in patients with HPV-positive Oropharyngeal Squamous Cell Carcinomas (OPSCC).
Secondary Outcome
Development of a Bayesian Network-based prediction model in identifying good responders to standard Chemo-Radiotherapy (CRT).
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