ESTRO 2020 Abstract Book

S1016 ESTRO 2020

lesions anywhere from C1 to L5, had less than 5 cms paraspinal mass, >3mm gap between edge of the lesion, 2 contiguous spine levels with <50% body involvement & No spinal instability. All patients underwent S board rigid fixation / body fix immobilization, advanced image guidance using 6 DOF corrections on C-arm Linac. Fractionation used for treatment was either 16 / 1 Frc. or 24gy/3 Frc based on the clinical scenario, goals of treatment & projected life expectancy. All patients underwent limited metabolic imaging Pre- treatment and Post-treatment PET-CT scans up to 3 months. Post SBRT first PET-CT scan was scheduled at 48 hours and serial PET CT scans were done at 10, 30, 60 and 90 days post treatment. PET-CT images were reviewed in order to determine the pre- and post- treatment maximum standardized uptake value (m-SUV) of the lesion, including “complete resolution” of FDG- avidity. Corresponding morphologic changes in the target lesions and surrounding normal bony were studied on the PET-CT images during the 3 moth evaluation period. Results All the 5 patients showed nearly consistent serial regression patterns in metabolic activity post treatment. The observed metabolic regression was between 60%-65% (median 50%) 48 hrs after treatment, 70% metabolic regression ( median – 70%) was observed 10 days post treatment. A median SUV increase of 1.7 was observed in all 5 pts in the first and second month scan when compared with 10th day post Treatment scan. At 3rd month, 4 out of 5 patients had FDG non-Avid disease. During the evaluation period (first 3 months) there was no significant change in bony architecture but the paraspinal mass lesions morphologically decreased at 3rd month scan. Conclusion Early metabolic response post Spine SBRT was seen at 48 hours post treatment & maximum metabolic response was seen at 3 monrths post SBRT. This is the first study reported in literature which looked into the serial metabolic trending in first 3 months post Spinal SBRT for spinal oligometastatic disease. PO-1823 New insights into cellular dose-response in vitro from high-throughput time-lapse AI assays R. Koch 1,2,3 , I. Dokic 2,3,4 , M. Alber 1,2,3 , E. Bahn 1,2,3,5 1 Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany ; 2 Heidelberger Institut für Radioonkologie HIRO, Quantitative klinische Strahlenbiologie, Heidelberg, Germany ; 3 National Center for Tumor Diseases NCT, Integrative Radiation Oncology, Heidelberg, Germany ; 4 German Cancer Research Center DKFZ, Clinical Cooperation Unit Translational Radiation Oncology, Heidelberg, Germany ; 5 German Cancer Research Center DKFZ, Clinical Cooperation Unit Radiation Oncology, Heidelberg, Germany Purpose or Objective Assaying the response of cells to radiation in vitro is a widely used radiobiological tool in radiotherapy. Despite occasional criticism, cell colony growth (CG) is commonly used as a surrogate to quantify the deleterious effect of radiation. Today, automation allows the acquisition and analysis of image data as a time series on a large scale. We took this as an opportunity to investigate the potential benefit of extracting statistical and time-dependent dynamical information. Material and Methods Cells of three different strains (ACHN, RENCA, H3122) were seeded onto multiwell plates, irradiated with single doses Poster: Radiobiology track: DNA damage response

of 0-10 Gy X-rays and placed into an incubator with automated phase-contrast image acquisition in 3 h intervals for 9 days. We developed an automated image segmentation routine that identifies individual colonies and records morphological parameters. We tracked colonies over time by pairwise matching on subsequent images. We then identified exponentially growing colonies and extracted CG parameters by using wavelet decomposition to identify periods of exponential CG and by training a tree ensemble classifier on a combination of morphological, wavelet and curve-fitting parameters. Based on the extracted CG data, we developed a biomathematical stochastic model that explicitly calculates the effects of radiation damage, colony growth, -stagnation, and -fusion to predict colony size distributions as a function of dose and time. Results The image segmentation algorithm achieves an average accuracy of 99 % (97-99 %). Colony classification displays an average accuracy of 85 % (78-91 %). Between 57 % and 95 % of the colonies exhibit phases of exponential CG, depending on radiation dose and cell line. CG rates are normally distributed with large standard deviations of up to 80 % and decrease with increasing dose. We observed a large variability of these parameters between the cell lines (see Fig. 1).

Comparing predictions from our model to the experimental data confirmed the hypothesis that stochastic CG explains the observations (see Fig. 2). Note that our model uses no adjustable parameters but only the extracted parameters on CG rate distributions.

Conclusion The observed decrease in CG rate with dose presents a potentially serious source of systematic error in