AUTHOR=Cao Yue , Aryal Madhava , Li Pin , Lee Choonik , Schipper Matthew , Hawkins Peter G. , Chapman Christina , Owen Dawn , Dragovic Aleksandar F. , Swiecicki Paul , Casper Keith , Worden Francis , Lawrence Theodore S. , Eisbruch Avraham , Mierzwa Michelle 

TITLE=Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in Both p16+ and p16– High Risk Head and Neck Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 9 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.01118

DOI=10.3389/fonc.2019.01118

ISSN=2234-943X

ABSTRACT=Purpose: To investigate p16 effects on quantitative imaging (QI) metrics of MRI and FDG-PET and their response rates during radiation therapy as well as predictive values for local, regional and distant failure (LF, RF and DF) in poor prognosis locally advanced head and neck cancers (HNC). We hypothesized that MRI-based QI metrics could add to clinical predictors of treatment failure more significantly than FDG-PET metrics.
Materials and methods: 54 patients with poor prognosis HNCs who were enrolled in an IRB approved prospective adaptive chemoradiotherapy trial were analyzed.  MRI-derived gross tumor volume (GTV), blood volume (BV) and apparent diffusion coefficient (ADC) pre-treatment and mid-treatment (fraction 10) as well as pre-treatment FDG PET metrics were analyzed in primary and nodal tumors.  Cox proportional hazards models were used to test QI metrics for prediction of LRF and DF free survival in addition to dominant clinical predictors (p16 and T4/N3).
Results: The mean ADC pre-RT and its change rate mid-treatment were significant higher and lower in p16- than p16+ primary tumors, respectively.  A Cox model identified that high mean ADC pre-RT had a high hazard for LF and RF in p16- tumors (p=0.015) but not in p16+ ones.  Most interesting, persisting subvolumes of low BV (TVbv) in primary and nodal tumors mid-treatment had high-risk for DF (p<0.05).  Also, total nodal GTV mid-treatment, mean/max SUV of FDG in all nodal tumors, and total nodal TLG were predictive for DF (p<0.05).  When including T4/N3 and total nodal GTV in the model, all nodal PET parameters had a p-value of >0.3, and only TVbv of primary tumors had a p-value of 0.06.
Conclusion: MRI-defined biomarkers are predictive and compare favorably with FDG-PET imaging markers.  MRI could be well integrated into the radiation therapy workflow for treatment planning, response assessment and adaptive therapy.