ESTRO 2020 Abstract Book

S272 ESTRO 2020

PH-0483 Acuros XB Dose Calculation algorithm: Dosimetric evaluation for computed tomography metal artifacts

In a clinical workflow, marker delineations should be checked by a second RTT or physicist, to prevent misidentification of makers. Based on the results gathered from burning in five voxels, this method was found suitable for clinical use.

Abstract withdrawn

PH-0484 Evaluation of AI based contouring tools in prostate cancer RT A. Borkvel 1 , E. Gershkevitsh 1 , M. Adamson 1 , K. Kolk 1 , D. Zolotuhhin 1 1 North Estonia Medical Centre, Radiotherapy Centre, Tallinn, Estonia Purpose or Objective OAR and target contouring is a labour intensive step in an RT treatment planning process. Recently multiple vendors have introduced a segmentation algorithms based on artificial intelligence (AI) to reduce the amount of manual work. In this study, the accuracy and efficiency gain for prostate cancer patient contouring when using AI based contouring tools from two vendors was assessed. Material and Methods Contouring accuracy was evaluated for 37 patients by comparing manually contoured structures to automatically segmented structures using MVison contouring tool. Standard Imaging StructSure software was used to evaluate the difference in structure volumes and Dice similarity coefficient. The following structures were segmented and evaluated for all patients: bladder, rectum, prostate, seminal vesicles, femoral heads and penile bulb. To assess the efficiency gain, two experienced RTTs produced manual contours for 18 patients and recorded the time required for outlining. Different set of 15 patients was automatically segmented, then manually edited by the same RTTs and time required recorded. Use of TPS semi-automatic segmentation tools was allowed. Average time per patient was calculated for manual contouring and editing task. To compare 2 different vendors the same 4 prostate patients were segmented using MVision and Mirada Medical contouring tools. Results Table 1 shows results of the automatic segmentation. Difference in volume for bladder and rectum is less than 10% and Dice coefficient is higher than 0.8. For the rest of the structures the difference in volumes is greater (27.0%- 69.5%) and reflected in a poorer Dice coefficient results (0.44-0.78). For prostate, MVision contouring tool systematically added extra contour on one CT slice above and below those outlined manually. The larger difference for femoral heads are due to the different definition of these structures in the AI training set.