Estimation 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

Diane Schott, Taly Gilat Schmidt, William Hall, Paul Knechtges, George Noid, Slade Klawikowski, Beth Erickson, X. Allen Li

Research output: Contribution to journalArticlepeer-review


Background: 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 imageguided 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. Methods: CT textures from daily diagnostic-quality CTs routinely acquired during IGRT using an inroom CT were analyzed. Pretreatment GTV was delineated from pre-RT diagnostic images and populated to the first daily CT. Maps of first-order textures (mean, SD, entropy, skewness and kurtosis) and shortrange second-order textures were created from the first daily CT. The classifier was trained to sort voxels into GTV and surrounding tissue on subsequent daily CTs over the course of RT. Optimum combinations of textures was defined by repeating the training process with all possible texture combinations. The trained classifier was used to identify voxels belonging to the GTVd, based on the CT of the day. Posttreatment GTV delineated from the post-RT follow-up images was populated to the last daily CT and used to validate the last GTVd delineated by the classifier. To demonstrate the concept, the method was described using three representative treatment sites, e.g., lung, breast and pancreatic tumors. Results: Comparing the classifier map generated from a new CT to the initial training CT, the dice coefficient (DC) for GTV in lung is 83% on the eighth treatment and 84% on the last. The DC for the breast GTV is 56% mid-treatment and 65% at the last treatment. In the case of the pancreas with the least in organ tissue contrast, the DC for 4 cases ranges from 21% to 77% for the last treatment compared with the post-RT diagnostic CT. The Housdorff distance (HD) ranged from 2.9 to 5.9 mm with the mean GTV RECIST dimension of 22.75 mm long by 14.7 mm short. Conclusions: It is feasible to estimate the general region of the GTV of the day from the daily CT acquired during RT, based on CT textures, using a trained voxel classifier algorithm. The obtained GTV may be used as a starting point for an accurate GTV delineation in online adaptive replanning. Further study with larger patient datasets is required to improve the robustness of the algorithms.

Original languageEnglish (US)
Pages (from-to)1189-1200
Number of pages12
JournalQuantitative Imaging in Medicine and Surgery
Issue number7
StatePublished - 2019
Externally publishedYes


  • Computer-assisted imaging processing
  • Image-guided radiotherapy
  • X-ray computed tomography scanner

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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