ESTRO 37 Abstract book

S1151

ESTRO 37

Electronic Poster: Physics track: (Quantitative) functional and biological imaging

EP-2091 Complementarity of PET-texture features with respect to information given by conventional indexes. M. Carles 1 , T. Bach 2 , I. Torres 3 , D. Baltas 1 , U. Nestle 4 , L. Martí 3 1 Medical Center- Faculty of Medicine- University of Freiburg, Division of Medical Physics- Department of Radiation Oncology, Freiburg, Germany 2 Medical Center- Faculty of Medicine- University of Freiburg, Administrative and Clinical Informatics- Department of Radiation Oncology, Freiburg, Germany 3 Hospital Universitario y Politécnico La Fe, Clinical Area of Medical Imaging, Valencia, Spain 4 Medical Center- Faculty of Medicine- University of Freiburg, Department of Radiation Oncology, Freiburg, Germany Purpose or Objective The interest in the quantification of tumor heterogeneity based on texture features (TF) with PET has recently increased. However the complementary information that PET-TF could provide with respect to the conventional indexes (SUV max , metabolic volume V and TLG) has been shown to be dependent of the method employed to resample the tumor voxel intensities, which is a preprocessing required for TF computation. The aim of our work was to study the complementarity of TF and conventional indexes information when two resampling methods were applied for TF computation. Material and Methods Thirty-one lung patients (36 lesions) were retrospectively analyzed. The first method (RN) resampled the tumor intensities with a constant number of bins (N) along all the 36 lesions: N=16, N=32 and N=64. For the second method (RW), the width of the resampling bin (SUV resolution, W) was constant along the lesions: W= 0.05, W=0.1 and W=0.5. Complementarity was evaluated in terms of Spearman's correlations (p<0.05). We considered TF with added value the ones that did not show strong correlation with any of the 3 conventional indexes, i. e. p>0.05 or (p<0.05, r<0.8) for all indexes. Results Results showed that TF complementarity with respect to conventional indexes was dependent on the resampling methods evaluated. For RW stronger correlations with SUV max were found, figure 1.a. TF with RN were more correlated with volume, figure 1.b. RW gave rise to a larger number of TF with added value, table 1.

Conclusion For this challenging patient cohort, sequentially acquired 4D DECT scans showed similar patient anatomy and stable breathing pattern allowing a consistent generation of DECT-based 79keV MonoCT datasets applicable for proton dose calculation. Patient-specific DECT-based SPR prediction on average CT datasets and breathing phases performed appropriately and can potentially reduce current range uncertainty in proton therapy. Even if large motion differences occur during the 2 sequential 4D DECT scans, dose distributions can still be reliably calculated using only the 140kVp dataset and beyond that important information on motion variability and robustness is gathered.