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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. 2016. "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 = 2016,
month = 6
}
  • Purpose: Local failure after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) is uncommon. We report the safety and efficacy of SBRT for salvage of local failure after previous SBRT with a biologically effective dose (BED) of ≥100 Gy{sub 10}. Methods and Materials: Using an institutional review board–approved lung SBRT registry, we identified all patients initially treated for early-stage NSCLC between August 2004 and January 2012 who received salvage SBRT for isolated local failure. Failure was defined radiographically and confirmed histologically unless contraindicated. All patients were treated on a Novalis/BrainLAB system using ExacTrac for imagemore » guidance, and received a BED of ≥100 Gy{sub 10} for each SBRT course. Tumor motion control involved a Bodyfix vacuum system for immobilization along with abdominal compression. Results: Of 436 patients treated from August 2004 through January 2012, we identified 22 patients with isolated local failure, 10 of whom received SBRT for salvage. The median length of follow-up was 13.8 months from salvage SBRT (range 5.3-43.5 months). Median tumor size was 3.4 cm (range 1.7-4.8 cm). Two of the 10 lesions were “central” by proximity to the mediastinum, but were outside the zone of the proximal bronchial tree. Since completing salvage, 3 patients are alive and without evidence of disease. A fourth patient died of medical comorbidities without recurrence 13.0 months after salvage SBRT. Two patients developed distant disease only. Four patients had local failure. Toxicity included grade 1-2 fatigue (3 patients) and grade 1-2 chest wall pain (5 patients). There was no grade 3-5 toxicity. Conclusions: Repeat SBRT with a BED of ≥100 Gy{sub 10} after local failure in patients with early-stage medically inoperable NSCLC was well tolerated in this series and may represent a viable salvage strategy in select patients with peripheral tumors ≤5 cm.« less
  • Purpose: There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lungmore » cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. Methods: FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. Results: All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66). Conclusion: Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).« less
  • Purpose: Tumor control probability (TCP) calculated with accumulated radiation doses may help design appropriate treatment margins. Image registration errors, however, may compromise the calculated TCP. The purpose of this study is to develop benchmark CT images to quantify registration-induced errors in the accumulated doses and their corresponding TCP. Methods: 4DCT images were registered from end-inhale (EI) to end-exhale (EE) using a “demons” algorithm. The demons DVFs were corrected by an FEM model to get realistic deformation fields. The FEM DVFs were used to warp the EI images to create the FEM-simulated images. The two images combined with the FEM DVFmore » formed a benchmark model. Maximum intensity projection (MIP) images, created from the EI and simulated images, were used to develop IMRT plans. Two plans with 3 and 5 mm margins were developed for each patient. With these plans, radiation doses were recalculated on the simulated images and warped back to the EI images using the FEM DVFs to get the accumulated doses. The Elastix software was used to register the FEM-simulated images to the EI images. TCPs calculated with the Elastix-accumulated doses were compared with those generated by the FEM to get the TCP error of the Elastix registrations. Results: For six lung patients, the mean Elastix registration error ranged from 0.93 to 1.98 mm. Their relative dose errors in PTV were between 0.28% and 6.8% for 3mm margin plans, and between 0.29% and 6.3% for 5mm-margin plans. As the PTV margin reduced from 5 to 3 mm, the mean TCP error of the Elastix-reconstructed doses increased from 2.0% to 2.9%, and the mean NTCP errors decreased from 1.2% to 1.1%. Conclusion: Patient-specific benchmark images can be used to evaluate the impact of registration errors on the computed TCPs, and may help select appropriate PTV margins for lung SBRT patients.« less
  • Purpose: To study the variability of patient-specific motion models derived from 4-dimensional CT (4DCT) images using different deformable image registration (DIR) algorithms for lung cancer stereotactic body radiotherapy (SBRT) patients. Methods: Motion models are derived by 1) applying DIR between each 4DCT image and a reference image, resulting in a set of displacement vector fields (DVFs), and 2) performing principal component analysis (PCA) on the DVFs, resulting in a motion model (a set of eigenvectors capturing the variations in the DVFs). Three DIR algorithms were used: 1) Demons, 2) Horn-Schunck, and 3) iterative optical flow. The motion models derived weremore » compared using patient 4DCT scans. Results: Motion models were derived and the variations were evaluated according to three criteria: 1) the average root mean square (RMS) difference which measures the absolute difference between the components of the eigenvectors, 2) the dot product between the eigenvectors which measures the angular difference between the eigenvectors in space, and 3) the Euclidean Model Norm (EMN), which is calculated by summing the dot products of an eigenvector with the first three eigenvectors from the reference motion model in quadrature. EMN measures how well an eigenvector can be reconstructed using another motion model derived using a different DIR algorithm. Results showed that comparing to a reference motion model (derived using the Demons algorithm), the eigenvectors of the motion model derived using the iterative optical flow algorithm has smaller RMS, larger dot product, and larger EMN values than those of the motion model derived using Horn-Schunck algorithm. Conclusion: The study showed that motion models vary depending on which DIR algorithms were used to derive them. The choice of a DIR algorithm may affect the accuracy of the resulting model, and it is important to assess the suitability of the algorithm chosen for a particular application. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.« less
  • Purpose: To investigate whether Monte Carlo (MC) recalculated dose distributions can predict the geometric location of the recurrence for nonsmall cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT). Methods: Thirty NSCLC patients with local recurrence were retrospectively selected for this study. The recurred gross target volumes (rGTV) were delineated on the follow-up CT/PET images and then rigidly transferred via imaging fusion to the original planning CTs. Failure pattern was defined according to the overlap between the rGTV and planning GTV (pGTV) as: (a) in-field failure (≥80%), (b) marginal failure (20%–80%), and (c) out-of-field failure (≤20%). All clinicalmore » plans were calculated initially with pencil beam (PB) with or without heterogeneity correction dependent of protocols. These plans were recalculated with MC with heterogeneity correction. Because of non-uniform dose distributions in the rGTVs, the rGTVs were further divided into four regions: inside the pGTV (GTVin), inside the PTV (PTVin), outside the pGTV (GTVout), and outside the PTV (PTVout). The mean doses to these regions were reported and analyzed separately. Results: Among 30 patients, 10 patients had infield recurrences, 15 marginal and 5 out-of-field failures. With MC calculations, D95 and D99 of the PTV were reduced by (10.6 ± 7.4)% and (11.7 ± 7.9)%. The average MC calculated mean doses of GTVin, GTVout, PTVin and PTVout were 48.2 ± 5.3 Gy, 48.2 ± 5.5 Gy, 46.3 ± 6.2 Gy and 46.6 ± 5.6 Gy, respectively. No significant dose differences between GTVin and GTVout (p=0.65), PTVin and PTVout (p=0.19) were observed, using the paired students t-test. Conclusion: Although the PB calculations underestimated the tumor target doses, the geometric location of the recurrence did not correlate with the mean doses of subsections of the recurrent GTV. Under dose regions recalculated by MC cannot predict the local failure for NSCLC patients treated with SBRT.« less