SU-E-J-186: CT Textures Can be Predictive for Tumor Shrinkage

Medical Physics
Luke A HunterLaurence E Court

Abstract

To determine if NSCLC GTV CT textures can be predictive for tumor shrinkage after proton therapy with concurrent chemotherapy. Simulation and weekly 4DCTs were obtained from 25 patients with locally advanced NSCLC treated by proton therapy at a 74 Gy dose level with concurrent chemotherapy. In-house deformable image registration software based on the demons algorithm propagated the physician-generated planning GTV onto each weekly image set. GTV soft tissue volumes were then extracted from CT images by in-house software which pruned out voxels below a cutoff HU threshold. Patients whose normalized end-treatment pruned GTV was less than the group median were classified as 'strong responders.'The remaining patients were classified as 'weak responders.' For each pruned GTV, the MaZda texture analysis software generated quantitative 3D texture features based on the image histogram, absolute gradient, co-occurrence matrix, and run-length matrix. Using the Fisher coefficient, a subset of these texture features that were relevant to distinguishing the two response groups was obtained. In-house software and MaZda were then used to perform neural network data classification with leave-one-out cross-validation based on this texture featu...Continue Reading

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