ESTRO 36 Abstract Book

S489 ESTRO 36 _______________________________________________________________________________________________

Material and Methods The mechanistic prediction is based on a radiobiological TCP model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013). The study presented here (see Sup. Figure 1) focuses on a cohort of 35 head and neck cancer patients treated with chemoradiotherapy which received baseline FDG PET and FMISO dynamic PET, and intra-treatment FMISO dynamic PET scans, and which excluded subjects having a significant increase in hypoxia during treatment. The model is used to predict the radiobiological evolution of each tumor voxel of the baseline image up until the intra- treatment scan (9.2±3.4 days). The main inputs to the model are the initial fractions of proliferative and hypoxic tumor cells in each voxel, obtained from an approximate solution to a system of linear equations relating cell fractions to voxel-level FDG uptake, perfusion (FMISO K 1 ) and hypoxia (FMISO k 3 ). For each lesion, the predicted levels of intra-treatment hypoxia are compared to the measured k 3 from the intra-treatment scan. A single global parameter (the average fraction of extremely hypoxic cells that take up FMISO) is determined from a training subset of 29 lesions by minimizing the average discrepancy between each lesion’s measured and predicted intra- treatment k 3 histograms (Cramér-von Mises criterion). A validation subset of 10 lesions is held out to test the resulting model.

decreasing pre- or mid-therapy K 1 spatial heterogeneity, higher but decreasing pre- or mid-therapy overall V b parameter value, and lower pre-therapy V b spatial heterogeneity.

Figure shows selected results of Kaplan-Meier analyses that illustrates prognostic power of some imaging biomarkers based on FLT PET parametric images.

Conclusion Worse outcome after radiotherapy was significantly associated with higher pre- or mid-therapy overall K i . Additionally, we found that various imaging biomarkers derived from vascular parameters or their change through the therapy, contains even stronger prognostic information than the FLT transport parameter, which justify use of kinetic analysis. PO-0890 PET-based radiobiological modeling of changes in tumor hypoxia during chemoradiotherapy M. Crispin Ortuzar 1 , M. Grkovski 1 , B.J. Beattie 1 , N.Y. Lee 2 , N. Riaz 2 , J.L. Humm 1 , J. Jeong 1 , A. Fontanella 1 , J.O. Deasy 1 1 Memorial Sloan Kettering Cancer Center, Medical Physics, New York, USA 2 Memorial Sloan Kettering Cancer Center, Radiation Oncology, New York, USA Purpose or Objective To develop a mechanistic radiobiological model of tumor control probability (TCP) for predicting changes in tumor hypoxia during chemoradiotherapy, based on pre- treatment imaging of perfusion and hypoxia with 18 F- Fluoromisonidazole (FMISO) dynamic PET and of glucose metabolism with 18 F-Fluorodeoxyglucose (FDG) PET.

Results The average fraction of extremely hypoxic cells that take up FMISO is 0.15 (95% CI 0.05 – 0.30 on bootstrap). In the training subset, the model predicts the mean, median and standard deviation of each lesion’s intra-treatment k 3 histograms (Pearson’s linear correlation coefficients between predicted and measured values of ρ=0.62, 0.60 and 0.69 respectively, all with positive 95% CI on bootstrap – see Sup. Table 1). In the validation subset, only the predictions of the intra-treatment mean and median k 3 of each lesion are significant (ρ=0.59 and 0.60 respectively).