ESTRO 36 Abstract Book

S498 ESTRO 36 _______________________________________________________________________________________________

Material and Methods Ten patients who were referred for brain metastasis radiosurgery were analysed in this study. A planning CT (1 mm slice thickness), a contrast-enhanced T1 3D MRI scan (1.5T, 1 mm isotropic voxel size, surface coils) with patient immobilized in a 3-point thermoplastic shell (mask-MR) and a contrast-enhanced T1 3D MRI scan (1.5T, 1 mm isotropic voxel size, multi-channel head coil) without immobilization mask (no mask-MR) were acquired. First, a clinician stated which of the MRI scans had superior quality, to assure that the no-mask MR had at least the same image quality compared to the clinically used mask- MR. Then, the two MRIs were registered independently to the planning CT by a normalized mutual information algorithm which was restricted to rigid registration. The GTV was delineated by 3 clinicians on 1) mask-MR and 2) no mask-MR. The brain stem, chiasm and right eye were delineated by one clinician. Furthermore, 8 well-defined landmarks were marked by an observer in both scans. Residual registration errors were estimated for both MRIs by measuring the absolute coordinate differences in the three orthogonal directions between the set of landmarks on both imaging series after registration. Moreover, the absolute differences in the centres-of-gravity coordinates of GTV (median of 3 observers), brain stem, chiasm and right eye on mask-MR and no mask-MR were compared. Results The no mask-MR image quality was found to be superior in 9 of the 10 patients. The average coordinate difference between mask-MR and no mask-MR for all landmarks along the three orthogonal directions were within 0.5 mm (table 1). Similar results were found for the coordinates of the centre-of-gravity of all delineated OARs and GTV. Deviations in OAR registration > 1mm could be attributed to variations in delineation (figure 1). Only in one case, a registration error was observed. All GTV deviations were within 1mm. Conclusion The registration of MRIs obtained with or without immobilization mask to a planning-CT generally differs less than the MRI resolution (1 mm isotropic). Therefore, immobilization of the head during MRI for patients undergoing radiotherapy of brain metastasis is not necessary. However, to guarantee high accuracy of image registration when omitting an immobilization device during MRI, more attention should be paid to the quality of MR-CT fusion. Furthermore, consecutive MR images should be matched separately to CT, to correct for intra-scan motion. We foresee two benefits of scanning without mask. Firstly, the patient comfort during the MRI scan sessions will be improved. Secondly, omission of the immobilization mask permits the use of a multi-channel head coil which results in higher image quality. Moreover, using a head coil allows for introduction of MRI techniques which require high signal-to-noise ratios or acceleration (e.g. DWI and FLAIR).

PO-0902 Identifying the dominant prostate cancer focal lesion using 3D image texture analysis D. Montgomery 1 , K. Cheng 1 , Y. Feng 1 , D.B. McLaren 2 , S. McLaughlin 3 , W. Nailon 1 1 Edinburgh Cancer Centre Western General Hospital, Department of Oncology Physics, Edinburgh, United Kingdom 2 Edinburgh Cancer Centre Western General Hospital, Department of Clinical Oncology, Edinburgh, United Kingdom 3 Heriot Watt University, School of Engineering and Physical Sciences, Edinburgh, United Kingdom Purpose or Objective Prostate cancer is one of the few solid organs where radiotherapy is applied to the whole organ. This is because accurately identifying the dominant cancer foci on magnetic resonance (MR) images, which can then be mapped onto computerised tomography (CT) images for radiotherapy planning, is difficult. The aim of this study was to investigate the use of three-dimensional (3D) texture analysis for automatically identifying the dominant cancer foci on MR images acquired for diagnosis and prior to the administration of androgen deprivation therapy, which may shrink the tumour foci. Material and Methods On 14 patients with confirmed prostate cancer, 3D image texture analysis was carried out on T2-weighted MR images acquired for diagnosis on a Symphony 1.5T scanner (Siemens, Erlangen, Germany). The prostate, bladder, rectum and the location of the main cancer foci were outlined on all images. In 5x5x5 pixel 3 volumes within the prostate 446 3D texture analysis features were calculated. These features were used to train an AdaBoost model, which was used to predict the class of each 5x5x5 region as either 'prostate” or 'focal lesion.” Morphological filtering was applied to each region to remove invalid elements and to clean the final outline. The Dice similarity coefficient was used to assess the agreement between the clinical and predicted contours. Results Figure 1 shows an example of a contour produced by the algorithm where the Dice similarity coefficient was 0.98. Table 1 shows the Dice coefficients calculated between the clinical contours and the contours predicted by 3D image analysis. 11 of the 14 cases had a Dice score greater than 0.65 and 8 of the 14 cases had a score greater than 0.9, indicating good agreement between the clinical and predicted contours. In 3 cases the image analysis technique failed to identify the focal lesion.

Figure 1 : Clinical contour in blue and predicted contour generated by 3D texture analysis shown in red on three T2-weighted MR images from the same patient (Patient 6).

Table 1 : Dice coefficient between the clinical contours and the contours predicted by image analysis. Conclusion The 3D image analysis results presented are encouraging and demonstrate the potential of this approach for automatically identifying focal disease on T2-weighted MR images. However, further investigation is required to establish why the approach fails in certain circumstances