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Title: SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

Abstract

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

Authors:
; ; ; ; ; ;  [1]
  1. The Cleveland Clinic Foundation, Cleveland, OH (United States)
Publication Date:
OSTI Identifier:
22626744
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BIOMEDICAL RADIOGRAPHY; COMPUTERIZED TOMOGRAPHY; CORRELATIONS; ENTROPY; IMAGE PROCESSING; IMAGES; LUNGS; NEOPLASMS; PATIENTS; PLANNING; RADIOTHERAPY

Citation Formats

Andrews, M, Abazeed, M, Woody, N, Stephans, K, Videtic, G, Xia, P, and Zhuang, T. SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients. United States: N. p., 2016. Web. doi:10.1118/1.4955792.
Andrews, M, Abazeed, M, Woody, N, Stephans, K, Videtic, G, Xia, P, & Zhuang, T. SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients. United States. doi:10.1118/1.4955792.
Andrews, M, Abazeed, M, Woody, N, Stephans, K, Videtic, G, Xia, P, and Zhuang, T. Wed . "SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients". United States. doi:10.1118/1.4955792.
@article{osti_22626744,
title = {SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients},
author = {Andrews, M and Abazeed, M and Woody, N and Stephans, K and Videtic, G and Xia, P and Zhuang, T},
abstractNote = {Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.},
doi = {10.1118/1.4955792},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}