ESTRO 36 Abstract Book

S150 ESTRO 36 _______________________________________________________________________________________________

Material and Methods CT data and heart delineations from 386 lung cancer patients were used to quantify random registration uncertainties. Inter-patient registration inclu ded an affine and a non-rigid registration (NRR) using the first patient in our database as reference. The affine re gistration was initialized by scaling the clip-box that encompassed both lungs to match the reference patient’s clip-box and then an automatic intensity-based affine registration was run. Subsequently a non-rigid registration was performed using NiftyReg on the same region. Both registrations ignored bony anatomy. Global random registration uncertainty was estimated by assessing standard deviation of all centres of mass of the transformed organ of interest contours, here the heart. Local random uncertainties on the heart surface were estimated by calculating the standard deviation of the distances of individual transformed delineations to the median heart. To determine the impact of the random registration uncertainty in our study, we compared the results of the data mining analysis between the original dose distributions and the Gaussian blurred dose distributions using the global registration uncertainty found, excluding outliers. Results Figure 1 summarizes the global and local random uncertainties. The smaller local uncertainties were seen on the lateral aspects of the heart close to the heart-lung interface; conversely, the largest local uncertainties were observed on the caudal regions of the heart close to the lung-diaphragm-liver interface.

Conclusion We did show significant deviations between positioning using the surface scanner and CBCT with the chosen ROI. In this small patient cohort, 68% of the fractions would have been out of tolerance using a threshold of 5mm and 3 degrees if positioned solely based on the surface scanner. Therefore a surface scanner does not replace the usual X-ray image guidance procedure. Furthermore, for pelvic patients it does not seem possible to use the surface scanner for reliable estimations of rotational deviations which could have limited repeated x-ray imaging. PV-0286 Quantifying registration uncertainties in image-based data mining E.M. Vasquez Osorio 1 , A. McWilliam 1,2 , J. Kennedy 3 , C. Faivre-Finn 1,4 , M. Van Herk 1,2 1 The University of Manchester, Division of Molecular & Clinical Cancer Studies- School of Medical Sciences- Faculty of Biology- Medicine and Health, Manchester, United Kingdom 2 The Christie NHS Foundation Trust, Christie Medical Physics and Engineering, Manchester, United Kingdom 3 The Christie NHS Foundation Trust, Informatics, Manchester, United Kingdom 4 The Christie NHS Foundation Trust, Clinical Oncology, Manchester, United Kingdom Purpose or Objective Image based data mining relies on non-rigid registration to bring image data on a common frame of reference. Registration uncertainties will affect the analysis and must be quantified and incorporated. We have developed a method to quantify global and local random registration uncertainties. Additionally, we evaluated the impact of accounting for global random registration uncertainties on the results of a recent lung data mining study that identified the base of the heart as a dose sensitive region affecting survival in lung cancer patients [1].

Including the random registration uncertainties in the data mining analysis did not change the conclusions of the study, mainly because significant regions exceeded the registration accuracy in size (figure 2).