ESTRO 2022 - Abstract Book

S1455

Abstract book

ESTRO 2022

Results ADC maps with radiotherapy and 16ch diagnostic coil setups are shown in Fig1. C-D. Compared to the 16ch coil, SNR with radiotherapy setup was lower by a factor of 2.5 (b0 image, near isocenter) but still met the QIBA criterion (SNR>50). Regarding ADC accuracy with radiotherapy setup in center 1 and 2 (Fig. 2), the bias was -0.06 (±0.07) and -0.18 (±0.06) x10 -4 mm 2 /s; precision was 5.4% and 4.9%; short term repeatability error was 0.3% for both centers. The intercenter reproducibility error was 0.6%. Thus, all QIBA criterions for ADC were met near isocenter except precision. ADC %bias and repeatability were worse in the very low ADC range. ADC precision could be improved by increasing number of averages. Similar results were obtained with the clinical RESOLVE protocol (bias, -0.04 (±0.04) x10 -4 mm 2 /s; precision, 3.2%; repeatability, 0.2%).

Conclusion Measurement of consistent ADC values on a PET/MR system with radiotherapy setup is feasible at different centers, meeting most QIBA criterions in phantoms. Stable results were obtained with the clinical RESOLVE protocol in a multicenter imaging study.

PO-1657 Evaluation of a Lexicographic Optimization based Algorithm for Automated Planning of Prostate Cancer

M.V. Gutierrez 1 , N. Maffei 1 , E. Cenacchi 1 , L. Manco 1 , A. Bernabei 1 , L. Boni 1 , A. Bruni 2 , M. Vernaleone 2 , E. Mazzeo 2 , G. Guidi 1

1 University Hospital of Modena, Medical Physics, Modena, Italy; 2 University Hospital of Modena, Radiotherapy Unit - Oncology and Hematology, Modena, Italy Purpose or Objective To evaluate and quantify the planning performance of a commercially available automated planning module with a lexicographic optimization-based algorithm in order to increase treatment planning efficiency, achieve high quality radiation therapy treatment plans and reduce inter-planner variability for a random cohort of prostate cancer patients. Materials and Methods An automated planning routine was applied to a sample of 12 previously treated patients. For the first two patients 540 automated plans were generated to refine the optimization goals and determine a planning strategy. For the remaining 10