ESTRO 2022 - Abstract Book

S1672

Abstract book

ESTRO 2022

(distance-to-agreement) and dose-difference criteria was setup as <3% for each measured point. The number of measured points in one verification plan was 1386.The threshold was determined as 5%. The number of points that meet assumption was determined as 95% of total measured points. The analyse was performed in global shift. Results Coverage of target by isodose 95% of prescribed dose were achieved for all 10 prepared treatment plans. At 8 from 10 treatment plans isodose 107% of prescribed dose cover less than 2% of irradiated volume. The initial criteria of mean dose in lungs less than 9 Gy were archived in all 10 treatment plans. Results of dosimetric verification was acceptable in six of nine treatment plans. During the verification with measurement array ArcCHECK® - Sun Nuclear only for 1 patient plan failed. Conclusion The method of dosimetric verification with measurement array ArcCHECK® - Sun Nuclear for total body irradiation plans at helical Tomotherapy (TTBI) can be use as reference’s method in in vitro dosimetry at helical Tomotherapy. D. Piro 1 , M. Marras 1 , A. D'Aviero 1 , A. Boschetti 1 , C. Votta 1 , A. Re 1 , F. Catucci 1 , D. Cusumano 2 , C. Di Dio 1 , S. Menna 2 , M. Iezzi 3 , F. Quaranta 1 , C. Flore 1 , E. Sanna 1 , D. Piccari 1 , G.C. Mattiucci 2 , V. Valentini 2 1 Mater Olbia Hospital, Radiation Oncology Unit, Olbia (SS), Italy; 2 Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Roma, Italy; 3 Istituto di Radiologia, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Roma, Italy Purpose or Objective Organs at risk (OARs) delineation is a crucial step of treatment planning workflow.Time consuming and inter-observer variability are main issues in manual OARs delineation. Deep-learning based auto-segmentation is a promising strategy to improve OARs contouring in radiotherapy departments. A comparison of deep-learning-generated auto-contours (AC) with manual contours (MC) was performed by expert Radiation Oncologists from a single center. Materials and Methods Planning computed tomography (CT) scans of patients undergoing RT treatments in pelvic region were considered. CT scans were processed by a commercial deep learning auto-segmentation based software to generate AC. Pelvic protocol was used to perform AC, structure set include ano-rectum, bladder, bowel bag and femoral heads. Manual Contours of OARs were delineated by expert radiation oncologists following the same contouring guidelines used by AC software. The AC and MC were compared using the Dice Similarity Coefficient (DSC) and 95% Hausdorff distance transform (DT). Results Twenty-three CT scan were included in the analysis; ano-rectum and femoral heads contours were not compared when considered for Clinical target Volumes in 5 and 2 cases respectively. DSC and DT results are showed in Fig. 1 and 2. Minimal differences were showed if we consider DSC of femoral heads (0.93 for both left and right femoral heads and DT 12.42 and 11.68 mm respectively). The comparison of hollow organs was slightly worse; probably regardless to differences in analysing density of hollow organs content. Mean DSC and 95% DT for bladder were 0.88 and 14.31 mm respectively. The comparison of ano-rectum and bowel- bag delineations resulted in DST of 0.83 and 0.86 respectively, and a DT of 29 mm for ano-rectum and 36.9 for bowel-bag. PO-1887 Validation of deep learning auto-segmentation in pelvic organs at risk: a preliminary analysis