ESTRO 36 Abstract Book

S491 ESTRO 36 2017 _______________________________________________________________________________________________

Physics, Odense, Denmark 16 Hospital of Næstved, Oncology, Næstved, Denmark

available imaging software (MIM Vista, Cleveland OH). On the test dataset the agreement between the manually drawn gold standard contours and atlas-based auto- segmented contours was measured with a Dice- coefficient. To determine the impact of auto-segmented contours on dosimetry calculations we determined the mean radiation dose for the manual contours and the auto- segmented contours for left-sided breast cancer patients. Differences in dose between the two contours were expressed with mean absolute errors. Results Within the test dataset the atlas-based auto-segmentation approach accurately delineated the heart with a Dice- coefficient of 0.87 ± 0.06 (mean ± standard deviation). Auto-segmentation was much less accurate for the LAD with a Dice-coefficient of 0.05 ± 0.06. Among left-sided breast cancer patients the mean heart dose was 1.2 ± 0.9 Gy for the manually contoured heart, and 2.7 ± 0.9 Gy for the manually contoured LAD. The auto-segmented mean heart dose was similar to the manually contoured mean heart dose, with a mean absolute error of 0.1 ± 0.2 Gy (range 0.0 - 0.7 Gy). The auto-segmented mean LAD dose differed moderately from the manual contoured mean LAD dose, with a mean absolute error of 1.0 ± 1.2 Gy (range 0.0 – 1.7 Gy). There were no statistically significant differences between the manual contours and the automated-contours for either the whole heart (p=0.78 by Wilcoxon-rank sum test), or the LAD (p=0.85). Conclusion This study demonstrates that atlas-based auto- segmentation accurately delineates the whole heart, though less accurately captures the LAD. The high concordance in mean heart dose between the manual contours and automated contours suggests that atlas- based auto-segmented contours could play a role in radiation treatment planning. PO-0898 Automated segmentation for breast cancer radiation therapy based on the ESTRO delineation guideline. A.R. Eldesoky 1,2 , E.S. Yates 3 , T.B. Nyeng 3 , M.S. Thomsen 3 , H.M. Nielsen 1 , P. Poortmans 4 , C. Kirkove 5 , M. Krause 6,7 , C. Kamby 8 , I. Mjaaland 9 , E.S. Blix 10,11 , I. Jensen 12 , M. Berg 13 , E.L. Lorenzen 14,15 , Z. Taheri-Kadkhoda 16 , B.V. Offersen 1 1 Aarhus University Hospital, oncology, Aarhus, Denmark 2 Mansoura University, Clinical Oncology and Nuclear Medicine, Mansoura, Egypt 3 Aarhus University Hospital, Medical Physics, Aarhus, Denmark 4 Radboud University Medical Center, Radiation Oncology, Nijmegen, The Netherlands 5 Catholic University of Louvain, Radiation Oncology, Louvain, Belgium 6 OncoRay- University Hospital Carl Gustav Carus- Technische Universität Dresden- and Helmholtz-Zentrum Dresden-Rossendorf, Radiation Oncology, Dresden, Germany 7 German Cancer Consortium DKTK Dresden and German Cancer Research Center DKFZ Heidelberg, Radiation Oncology, Dresden, Germany 8 Rigshospitalet, Oncology, Copenhagen, Denmark 9 Stavanger University Hospital, Oncology, Stavanger, Norway 10 University Hospital of North Norway, Oncology, Tromsø, Norway 11 Institute of Medical Biology- UiT The Arctic University of Norway, Immunology Research group, Tromsø, Norway 12 Aalborg University Hospital, Medical Physics, Aalborg, Denmark 13 Hospital of Vejle, Medical Physics, Vejle, Denmark 14 University of Southern Denmark, Institute of Clinical Research, Odense, Denmark 15 Odense University Hospital, Laboratory of Radiation

Purpose or Objective To internally and externally validate an atlas based automated segmentation (ABAS) tool for loco-regional radiation therapy of early breast cancer based on the ESTRO consensus guideline for target volume delineation. Material and Methods Structures of 60 patients manually delineated according to the ESTRO consensus guideline were included in four categorized multi-atlas libraries (based on laterality and surgery) using MIM Maestro ™ software. These libraries were used for automated segmentation of primary and nodal target volumes and organs at risk. Internal Validation of ABAS was done by comparing ABAS before and after correction against a gold standard manual segmentation (MS) in 50 patients from the local institution using Dice Similarity Coefficient (DSC), Average Hausdorff Distance (AHD), difference in volume (∆V) and time. External validation was done by comparing ABAS without correction against MS in 40 patients from other institutions using DSC and AHD. In the internal validation phase MS and correction of ABAS was performed by only one observer, while in the external validation phase MS was performed by multiple observers from 10 different institutions. Results ABAS reduced the time of MS before and after correction by 93% and 32%, respectively. In the internal validation phase, ABAS showed high agreement with MS for lung, heart, breast and humeral head, moderate agreement for chest wall and axillary nodal levels and poor agreement for inter-pectoral, internal mammary nodal regions and left anterior descending coronary artery (Figure 1). Correcting ABAS significantly improved all the parameters defined as increased DSC and decreased AHD and ∆V. Applying ABAS in an external group of patients with different arm positions, immobilization techniques and respiratory gating status showed comparable results (Table 1). Table 1.