Abstract Book

S1168

ESTRO 37

Results DIRLAB data-based extreme phase registration (inspiration to expiration phase) shows lowest TRE values for ANTS [(2.4 ± 1.3] mm], VarReg [(2.5 ± 1.3) mm] and Elastix [(2.6 ± 1.2) mm]. However, high(est) DIR accuracy not necessarily translated into high(est) correspondence modeling accuracy as ANTS registration yields highest mean TRE (3.0 mm) in model-based motion field estimation. Correspondence model formation and model- based 4D dose simulation for patient 4D CT data shows at least for lung metastases high accordance for the different DIR algorithm, irrespective of DIR accuracy differences. In contrast, results of 4D dose simulation for the investigated liver metastases are more diverging and vary for the different DIR approaches. However, large negative ΔD 95% values for 4 out of 5 algorithms were successfully connected to positive local recurrence for 2 liver metastases. Conclusion Especially the diverging results of 4D dose simulation in the liver raise doubts regarding reliability of DIR application in low contrast areas. Although, 4D dose accumulation for lung metastases shows promising results, current open source DIR frameworks should not be considered ready for "plug-and-play" use for 4D dose accumulation. EP-2123 Clinical evaluation of an auto-segmentation toolbox for breast CTV R. Simões 1 , R. Rozendaal 1 , J. Trinks 1 , R. Kalisvaart 1 , U. Van der Heide 1 , P. Remeijer 1 1 Netherlands Cancer Institute, Radiation Oncology, Amsterdam, The Netherlands Purpose or Objective Mirada Medical’s atlas-based auto-segmentation toolbox (Workflow Box™) is used to obtain breast CTV contours, which are manually corrected (if necessary) by the clinicians before being sent to the Treatment Planning System. In this work, our aim is to build an evaluation framework to assess the quality of the automatic contours with respect to the manual adjustments made on them, by analyzing both geometrical and dosimetric differences. Material and Methods 20 automatically generated contours of the left breast were available, as well as the manual adjustments made by the clinicians. The automatic contours were obtained with Mirada’s Workflow Box™, using 10 left breast atlases that had previously been selected and verified by a clinician. Within this toolbox, Deformable Image Registration is performed using an adaptation of Lucas- Kanade optic flow and the labels are fused by majority voting. Standard performance evaluation metrics were used to geometrically compare the contours: Dice coefficient, 95 th percentile Hausdorff distance (95%HD) and centroid distance (CD). A visual assessment of the errors was performed and displayed by means of projections onto a mean breast shape. The treatment plans were automatically generated using an in-house built framework (FAST), which has been clinically validated. Two treatment plans were generated per patient based on the auto-contour and the adjusted contour and the resulting dose distributions were compared. The dosimetric differences were quantified using indicators from γ analysis (3%/3mm): γ pass rate (γPR) and γ 95% percentile (γ95). Results The results for the geometric metrics are the following (mean ± standard deviation): Dice score 0.95±0.04, 95% Hausdorff distance (10.85±7.65)mm, centroid distance (4.50±4.35)mm. The differences are most often found in the most posterior and cranial parts of the breast (Fig. 1). We observe that larger breasts tend to be under- segmented and smaller breasts are often over-

Conclusion Textural kinetic trajectories from consequential intra- treatment CT scans can predict for subsequent radiation- induced toxicities. Combining clinical and radiomics input can synergistically add up to the predictive capacity of post-RT xerostomia probability computation. on correspondence model-based 4D dose accumulation for lung/liver SBRT T. Sothmann 1,2 , N. Mogadas 1 , T. Gauer 2 , R. Werner 1 1 University Medical Center Hamburg - Eppendorf UKE, Department of Computational Neuroscience, Hamburg, Germany 2 University Medical Center Hamburg - Eppendorf UKE, Department of Radiotherapy and Radio-Oncology, Hamburg, Germany Purpose or Objective 4D dose accumulation approaches rely on accurate DIR- based internal motion field estimation. This study, therefore, investigates and compares open source deformable image registration (DIR) frameworks regarding their influence on correspondence model-based 4D dose simulation in stereotactic radiotherapy of liver and lung lesions. Material and Methods Five wide-spread open source DIR frameworks (ANTS, VarReg, DIRART, NiftyReg, Elastix, Plastimatch) were considered, motivated by high ranks in the EMPIRE10 registration challenge. Registration accuracy was evaluated by means of DIRLAB 4D CT data sets and is quantified by landmark-based target registration errors (TRE). Further, regression-based correspondence models that correlate patient-specific internal motion and external breathing signals were built on the basis of each DIR framework (internal motion field estimation) and mentioned 4D CT DIRLAB data. This allows for model accuracy evaluation. Eventually, correspondence model- based 4D dose simulation was conducted employing 4D CT treatment planning data of 10 patients with 6 lung and 9 liver lesions with known 2-years local control of each lesion. For each DIR and patient, an individual correspondence model was built to simulate the delivered 4D dose distribution of the corresponding VMAT treatment plans. During treatment acquired breathing curves were used as external motion information to account for patient-specific motion effects. Deviations between planned dose and retrospectively simulated dose distributions were analyzed by ΔD 95% , the difference of D 95%,4D-Sim and D 95%,Plan with D 95% as the dose received by 95% of the GTV. A potential linkage between ΔD 95% and clinical local recurrence was investigated. EP-2122 Influence of DIR algorithms