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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Oncol. | doi: 10.3389/fonc.2019.01118

Predictive Values of MRI and PET Derived Quantitative Parameters for Patterns of Failure in both p16+ and p16- High Risk Head and Neck Cancer

 Yue Cao1*, Madhava Aryal1, Pin Li2, Matthew Schipper2,  Peter Hawkins1, Christina Chapman1, Dawn Owen1,  Aleksandar Dragovic1*,  Paul Swiecicki3, Keith Casper4, Francis Worden3, Theodore S. Lawrence5,  Avraham Eisbruch1 and MIchelle Mierzwa1*
  • 1Department of Radiation Oncology, University of Michigan, United States
  • 2Department of Biostatistics, School of Public Health, University of Michigan, United States
  • 3Department of Internal medicine, University of Michigan, United States
  • 4Department of Otolaryngology, University of Michigan, United States
  • 5Department of Radiology,University of Cambridge, United Kingdom

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.

Keywords: MRI, head and neck cancer, radiation therapy, imaging biomarker, adaptive therapy

Received: 01 Aug 2019; Accepted: 08 Oct 2019.

Copyright: © 2019 Cao, Aryal, Li, Schipper, Hawkins, Chapman, Owen, Dragovic, Swiecicki, Casper, Worden, Lawrence, Eisbruch and Mierzwa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Yue Cao, Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, United States, yuecao@med.umich.edu
Dr. Aleksandar Dragovic, Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, United States, adragovi@med.umich.edu
MD. MIchelle Mierzwa, Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, United States, mmierzwa@med.umich.edu