WE-G-BRD-06: Variation in Dynamic Positron Emission Tomography Imaging of Tumor Hypoxia in Early Stage Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy




Tumor hypoxia is correlated with treatment failure. To date, there are no published studies investigating hypoxia in non-small cell lung cancer (NSCLC) patients undergoing SBRT. We aim to use 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) imaging to non-invasively quantify the tumor hypoxic volume (HV), to elucidate potential roles of reoxygenation and tumor vascular response at high doses, and to identify an optimal prognostic imaging time-point.


SBRT-eligible patients with NSCLC tumors >1cm were prospectively enrolled in an IRB-approved study. Computed Tomography and dynamic PET images (0–120min, 150–180min, and 210–240min post-injection) were acquired using a Siemens BiographmCT PET/CT scanner. 18F-FMISO PET was performed on a single patient at 3 different time points around a single SBRT delivery of 18 Gy and HVs were compared using a tumor-to-blood ratio (TBR)>1.2 and rate of influx (Ki)>0.0015 (Patlak).


Results from our first patient showed substantial temporal changes in HV following SBRT. Using a TBR threshold >1.2 and summed images 210–240min, the HVs were 19%, 31% and 13% of total tumor volume on day 0, 2 (48 hours post-SBRT), and 4 (96 hours post-SBRT). The absolute volume of hypoxia increased by nearly a factor of 2 after 18 Gy and then decreased almost to baseline 96 hours later. Selected imaging timepoints resulted in temporal changes in HV quantification obtained with TBR. Ki, calculated using 4-hour dynamic data, evaluated HVs as 22%, 75% and 21%, respectively.


ith the results of only one patient, this novel pilot study highlights the potential benefit of 18F-FMISO PET imaging as results indicate substantial temporal changes in tumor HV post-SBRT. Analysis suggests that TBR is not a robust parameter for accurate HV quantification and heavily influenced by imaging timepoint selection. Kinetic modeling parameters are more sensitive and may aid in future treatment individualization based on patient-specific biological information.