Daniela Thorwarth1,2, Stefan Welz3, David Mönnich4, Christina Pfannenberg5, Konstantin Nikolaou5, Matthias Reimold6, Christian La Fougère6, Gerald Reischl7, Paul-Stefan Mauz8, Frank Paulsen3, Markus Alber4,9, Claus Belka2,10, Daniel Zips2,3. 1. Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany daniela.thorwarth@med.uni-tuebingen.de. 2. German Cancer Consortium, Tübingen, Germany, and German Cancer Research Center, Heidelberg, Germany. 3. Department of Radiation Oncology, University of Tübingen, Tübingen, Germany. 4. Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Tübingen, Germany. 5. Diagnostic and Interventional Radiology, Department of Radiology, University of Tübingen, Tübingen, Germany. 6. Department of Nuclear Medicine, University of Tübingen, Tübingen, Germany. 7. Department of Preclinical Imaging and Radiopharmacy, University of Tübingen, Tübingen, Germany. 8. Department of Otorhinolaryngology, University of Tübingen, Tübingen, Germany. 9. Department of Radiation Oncology, University of Heidelberg, Heidelberg, Germany; and. 10. Department of Radiation Oncology, LMU Munich, München, Germany.
Abstract
Our purpose was to evaluate an imaging parameter-response relationship between the extent of tumor hypoxia quantified by dynamic 18F-fluoromisonidazole (18F-FMISO) PET/CT and the risk of relapse after radiotherapy in patients with head and neck cancer. Methods: Before a prospective cohort of 25 head and neck cancer patients started radiotherapy, they were examined with dynamic 18F-FMISO PET/CT 0-240 min after tracer injection. 18F-FMISO image parameters, including a hypoxia metric, M FMISO , derived from pharmacokinetic modeling of dynamic 18F-FMISO and maximum tumor-to-muscle ratio (TMRmax) at 4 h after injection, gross tumor volume (GTV), relative hypoxic volume based on M FMISO , and a logistic regression model combining GTV and TMRmax, were assessed and compared with a previous training cohort (n = 15). Dynamic 18F-FMISO was used to validate a tumor control probability model based on M FMISO The prognostic potential with respect to local control of all potential parameters was validated using the concordance index for univariate Cox regression models determined from the training cohort, in addition to Kaplan-Meier analysis including the log-rank test. Results: The tumor control probability model was confirmed, indicating that dynamic 18F-FMISO allows stratification of patients into different risk groups according to radiotherapy outcome. In this study, M FMISO was the only parameter that was confirmed as prognostic in the independent validation cohort (concordance index, 0.71; P = 0.004). All other investigated parameters, such as TMRmax, GTV, relative hypoxic volume, and the combination of GTV and TMRmax, were not able to stratify patient groups according to outcome in this validation cohort (P = not statistically significant). Conclusion: In this study, the relationship between M FMISO and the risk of relapse was prospectively validated. The data support further evaluation and external validation of dynamic 18F-FMISO PET/CT as a promising method for patient stratification and hypoxia-based radiotherapy personalization, including dose painting.
Our purpose was to evaluate an imaging parameter-response relationship between the extent of tumor hypoxia quantified by dynamic 18F-fluoromisonidazole (18F-FMISO) PET/CT and the risk of relapse after radiotherapy in patients with head and neck cancer. Methods: Before a prospective cohort of 25 head and neck cancerpatients started radiotherapy, they were examined with dynamic 18F-FMISO PET/CT 0-240 min after tracer injection. 18F-FMISO image parameters, including a hypoxia metric, M FMISO , derived from pharmacokinetic modeling of dynamic 18F-FMISO and maximum tumor-to-muscle ratio (TMRmax) at 4 h after injection, gross tumor volume (GTV), relative hypoxic volume based on M FMISO , and a logistic regression model combining GTV and TMRmax, were assessed and compared with a previous training cohort (n = 15). Dynamic 18F-FMISO was used to validate a tumor control probability model based on M FMISO The prognostic potential with respect to local control of all potential parameters was validated using the concordance index for univariate Cox regression models determined from the training cohort, in addition to Kaplan-Meier analysis including the log-rank test. Results: The tumor control probability model was confirmed, indicating that dynamic 18F-FMISO allows stratification of patients into different risk groups according to radiotherapy outcome. In this study, M FMISO was the only parameter that was confirmed as prognostic in the independent validation cohort (concordance index, 0.71; P = 0.004). All other investigated parameters, such as TMRmax, GTV, relative hypoxic volume, and the combination of GTV and TMRmax, were not able to stratify patient groups according to outcome in this validation cohort (P = not statistically significant). Conclusion: In this study, the relationship between M FMISO and the risk of relapse was prospectively validated. The data support further evaluation and external validation of dynamic 18F-FMISO PET/CT as a promising method for patient stratification and hypoxia-based radiotherapy personalization, including dose painting.
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