Open AccessEstimation of changing gross tumor volume from longitudinal CTs during radiation therapy delivery based on a texture analysis with classifier algorithms: a proof-of-concept study
Author(s) -
D. Schött,
Taly Gilat Schmidt,
William A. Hall,
Paul Knechtges,
G. Noid,
Slade Klawikowski,
Beth Erickson,
X. Allen Li
Publication year - 2019
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims.2019.06.24
Subject(s) - image guided radiation therapy , voxel , kurtosis , medicine , radiation therapy , cone beam computed tomography , computer science , nuclear medicine , classifier (uml) , contouring , artificial intelligence , radiology , computed tomography , mathematics , statistics , computer graphics (images)
Adaptive radiation therapy (ART) is moving into the clinic rapidly. Capability of delineating the tumor change as a result of treatment response during treatment delivery is essential for ART. During image-guided radiation therapy (IGRT), a CT or cone-beam CT is taken at the time of daily setup and the tumor is not visible by eye in regions of soft tissue due to low contrast. The scope of this paper is to develop a method using a classifier trained on non-contrast CT textures, to estimate the gross tumor volume (GTV) of the day (GTVd) from daily (longitudinal) CTs acquired during the course of IGRT when the tumor is not visible.