ESTRO 37 Abstract book

S1178

ESTRO 37

Purpose or Objective To explore the possibility of texture features from daily megavoltage computed tomography (MVCT) images on predicting radiotherapy outcome of non-small cell lung cancer (NSCLC). Material and Methods Daily MVCT images from 18 patients with peripheral primary non-small cell lung cancer (histo-pathological stage T1N0M0) were prospectively analyzed. Craniocaudal respiratory movement observed under 2D simulator should be less than 1cm as enrollment criteria. MVCT images were obtained covering the whole gross tumor volume (GTV) plus 2cm extension in superior-inferior direction. Ten patients were treated with radiotherapy prescription dose of 4.0~7.0Gy/fraction, 7 patients with 10.0~11.0Gy/fraction and 1 patient with 15.0Gy/fraction. MVCT image scanning parameters: acquisition pitch 4mm/r and reconstruction interval 1mm. Image resolution is 512x512 pixel. Gross tumor volumes (GTV) were delineated by 3DSlicer with lung window setting (window width, 1500 HU; window level, -700 HU). Texture features of GTVs were extracted by the Imaging Biomarker Explorer (IBEX) software. To test whether the texture features changed with stable and consistent variation trends during the treatment course, each texture feature is observed and their values were The average GTV volume of 18 patients was 12.0cc. Artifacts on MVCT images from the respiratory movement did not seriously affect the delineations. Fifty-two texture features from 5 categories were extracted from each set of MVCT images, which features were selected to cover the diverse range of features that have been used in previous texture feature studies using CT images of NSCLC. Forty (77%) texture features presented stable and consistent variation trends during the treatment course. Two texture features: Meanabsolutedeviation, Graylevelnonuniformity presented stable and consistent variation trends on MVCT images from 5 and 4 patients respectively. Texture features InverseVariance, LongRunEmphasis, RunPercentage, Coarseness, GlobalMean, GlobalStd and Variance presented stable and consistent variation trends on MVCT images from 3 patients. Conclusion Some of the texture features extracted from the daily MVCT images obtained by TomoTerapy have a steady and consistent variation trend during the radiotherapy course, which may be used to predict radiotherapy outcomes in patients with NSCLC. Further investigations are expected to relate the texture features from TomoTherapy MVCT and radiotherapy outcomes. EP-2135 Statistical motion masks to identify sliding surfaces for motion models used on an MR-Linac B. Eiben 1 , E.H. Tran 1 , M.J. Menten 2 , A. Wetscherek 2 , D.J. Hawkes 1 , U. Oelfke 2 , J.R. McClelland 1 1 University College London, Medical Physics and Biomedical Engineering, London, United Kingdom 2 The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Joint Department of Physics, London, United Kingdom Purpose or Objective Respiratory motion is a challenge for precise radiotherapy delivery for lung cancer patients. MR-Linac technology facilitates real-time monitoring of lung tumour motion using 2D cine-MR acquisitions. However, motion of the full 3D patient anatomy including the tumour and organs at risk remains, due to relatively slow 3D MR acquisition and reconstruction techniques, an unsolved problem. In this regard motion models are a promising tool to estimate the full 3D motion of the patient during treatment based on a surrogate signal. Building such plotted. Results

best to interpret multi-modality image information from computerised tomography (CT) and magnetic resonance (MR) image data. The aim of this work was to assist with this task and construct a prostate shape model, or atlas, that would automatically segment the prostate on MR images. Material and Methods T2-weighted MR prostate scans from 40 patients (30 training cases and 10 test cases) were obtained from the publicly available MICCAI PROMISE12 data set. Following registration of all prostate cases in the training set to a reference case an atlas was created using Principal Component Analysis (PCA) to extract the mean-shape and shape variations from the data. A covariance matrix was computed by subtracting the average shape and decomposing the matrix to find the first few large eigenvalues covering 95%~98% of the information within the shape. The shape model was then structured as: prior model = (mean shape + b x Ø), where b is the weight vector corresponding to the eigenvectors and Ø is the corresponding eigenvector matrix. Any new case can be generated by deforming the prior atlas. Results Table 1 shows the simulation performance using three atlas shape model methods incorporating different variations of the algorithm. These include rigid and non- rigid registration for the initial registration to a reference case and Iterative Closest Point (ICP) and Coherent Point Drift (CPD) to search for corresponding pairs in the registered data. The pilot results show that the CPD method works better than ICP registration, particularly for non-rigid data points. Furthermore, both the Dice Similarity Criterion (DSC) and the Jaccard Index (JI) measures for the CPD method indicate an acceptable performance with mean values of 0.71 and 0.55 for the combined approach. The Hausdorff Distance (HD), with a mean of 4.04 mm, also represents a favourable result for this method. Table 1 : Performance comparison of three different models used to build the atlas. Conclusion The proposed atlas-based segmentation algorithms were evaluated on T2-weighted MR prostate images from the MICCAI PROMISE12 challenge. The simulation results demonstrate that the proposed approach has generally acceptable performance for automatically segmenting a new prostate case and has the potential to be used as part of a clinical decision support system. However, further validation on a much larger data set is required. EP-2134 Texture features from Tomo daily MVCT may predict radiotherapy outcome of NSCLC: a pilot study J. Zhang 1 , J. Zhu 2 , Y. Zhang 1 , Y. Yin 2 , B. Li 3 1 University of Jinan, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, China 2 Shandong Cancer Hospital affiliated to Shandong University, Department of Radiation Oncology Physic and Technology, Jinan, China 3 Shandong Cancer Hospital affiliated to Shandong University, Department of Radiation Oncology, Jinan, China Metric Rigid-ICP Rigid- CPD Rigid- & Non- rigid-CPD Dice Similarity Criterion 0.625 0.659 0.706 Jaccard Index 0.456 0.491 0.546 Haussdorf Distance 18.040 4.518 4.041