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Title: SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease

Abstract

Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. Methods: The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres placed at the upper lobe and lower lobe respectively in the normal lung (contralateral to tumor). 27 texture features (9 from intensity histogram, 8 from grey-level co-occurrence matrix [GLCM] and 10 from grey-level run-length matrix [GLRM]) were extracted from [normal lung-GTV]. To measure the variability of a feature F, the relative difference D=|Fref -Fsim|/Fref*100% was calculated, where Fref was for the entire normal lung and Fsim was for [normal lung-GTV]. A feature was considered as robust if the largest non-outlier (Q3+1.5*IQR) D was less than 5%, and considered as not correlated with normal lung volume when their Pearson correlation was lower than 0.50. Results: Only 11 features were robust. All first-order intensity-histogram features (mean, max, etc.) were robust, while most higher-order features (skewness, kurtosis, etc.) were unrobust. Only two of the GLCM and four of themore » GLRM features were robust. Larger GTV resulted greater feature variation, this was particularly true for unrobust features. All robust features were not correlated with normal lung volume while three unrobust features showed high correlation. Excessive variations were observed in two low grey-level run features and were later identified to be from one patient with local lung diseases (atelectasis) in the normal lung. There was no dependence on GTV location. Conclusion: We identified 11 robust normal lung CT texture features that can be further examined for the prediction of radiation-induced lung disease. Interestingly, low grey-level run features identified normal lung diseases. This work was supported in part by the National Cancer Institute Grants R01CA172638.« less

Authors:
; ;  [1]
  1. University of Maryland School of Medicine, Baltimore, MD (United States)
Publication Date:
OSTI Identifier:
22626752
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; ASYMMETRY; COMPUTERIZED TOMOGRAPHY; CORRELATIONS; FIBROSIS; FINANCING; LUNGS; NEOPLASMS; PATIENTS; PNEUMONITIS; RESPIRATION; SIMULATION

Citation Formats

Choi, W, Riyahi, S, and Lu, W. SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease. United States: N. p., 2016. Web. doi:10.1118/1.4955803.
Choi, W, Riyahi, S, & Lu, W. SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease. United States. doi:10.1118/1.4955803.
Choi, W, Riyahi, S, and Lu, W. 2016. "SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease". United States. doi:10.1118/1.4955803.
@article{osti_22626752,
title = {SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease},
author = {Choi, W and Riyahi, S and Lu, W},
abstractNote = {Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume. Methods: The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres placed at the upper lobe and lower lobe respectively in the normal lung (contralateral to tumor). 27 texture features (9 from intensity histogram, 8 from grey-level co-occurrence matrix [GLCM] and 10 from grey-level run-length matrix [GLRM]) were extracted from [normal lung-GTV]. To measure the variability of a feature F, the relative difference D=|Fref -Fsim|/Fref*100% was calculated, where Fref was for the entire normal lung and Fsim was for [normal lung-GTV]. A feature was considered as robust if the largest non-outlier (Q3+1.5*IQR) D was less than 5%, and considered as not correlated with normal lung volume when their Pearson correlation was lower than 0.50. Results: Only 11 features were robust. All first-order intensity-histogram features (mean, max, etc.) were robust, while most higher-order features (skewness, kurtosis, etc.) were unrobust. Only two of the GLCM and four of the GLRM features were robust. Larger GTV resulted greater feature variation, this was particularly true for unrobust features. All robust features were not correlated with normal lung volume while three unrobust features showed high correlation. Excessive variations were observed in two low grey-level run features and were later identified to be from one patient with local lung diseases (atelectasis) in the normal lung. There was no dependence on GTV location. Conclusion: We identified 11 robust normal lung CT texture features that can be further examined for the prediction of radiation-induced lung disease. Interestingly, low grey-level run features identified normal lung diseases. This work was supported in part by the National Cancer Institute Grants R01CA172638.},
doi = {10.1118/1.4955803},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: Radiation induced lung damage (RILD) is an important dose-limiting toxicity for patients treated with radiation therapy. Scoring systems for RILD are subjective and limit our ability to find robust predictors of toxicity. We investigate the dose and time-related response for texture-based lung CT image features that serve as potential quantitative measures of RILD. Methods: Pre- and post-RT diagnostic imaging studies were collected for retrospective analysis of 21 patients treated with photon or proton radiotherapy for NSCLC. Total lung and selected isodose contours (0–5, 5–15, 15–25Gy, etc.) were deformably registered from the treatment planning scan to the pre-RT and availablemore » follow-up CT studies for each patient. A CT image analysis framework was utilized to extract 3698 unique texture-based features (including co-occurrence and run length matrices) for each region of interest defined by the isodose contours and the total lung volume. Linear mixed models were fit to determine the relationship between feature change (relative to pre-RT), planned dose and time post-RT. Results: Seventy-three follow-up CT scans from 21 patients (median: 3 scans/patient) were analyzed to describe CT image feature change. At the p=0.05 level, dose affected feature change in 2706 (73.1%) of the available features. Similarly, time affected feature change in 408 (11.0%) of the available features. Both dose and time were significant predictors of feature change in a total of 231 (6.2%) of the extracted image features. Conclusion: Characterizing the dose and time-related response of a large number of texture-based CT image features is the first step toward identifying objective measures of lung toxicity necessary for assessment and prediction of RILD. There is evidence that numerous features are sensitive to both the radiation dose and time after RT. Beyond characterizing feature response, further investigation is warranted to determine the utility of these features as surrogates of clinically significant lung injury.« less
  • Purpose: Existing normal tissue complication probability (NTCP) models for radiation pneumonitis (RP) traditionally rely on dosimetric and clinical data but are limited in terms of performance and generalizability. Extraction of pre-treatment image features provides a potential new category of data that can improve NTCP models for RP. We consider quantitative measures of total lung CT intensity and texture in a framework for prediction of RP. Methods: Available clinical and dosimetric data was collected for 198 NSCLC patients treated with definitive radiotherapy. Intensity- and texture-based image features were extracted from the T50 phase of the 4D-CT acquired for treatment planning. Amore » total of 3888 features (15 clinical, 175 dosimetric, and 3698 image features) were gathered and considered candidate predictors for modeling of RP grade≥3. A baseline logistic regression model with mean lung dose (MLD) was first considered. Additionally, a least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the set of clinical and dosimetric features, and subsequently to the full set of clinical, dosimetric, and image features. Model performance was assessed by comparing area under the curve (AUC). Results: A simple logistic fit of MLD was an inadequate model of the data (AUC∼0.5). Including clinical and dosimetric parameters within the framework of the LASSO resulted in improved performance (AUC=0.648). Analysis of the full cohort of clinical, dosimetric, and image features provided further and significant improvement in model performance (AUC=0.727). Conclusions: To achieve significant gains in predictive modeling of RP, new categories of data should be considered in addition to clinical and dosimetric features. We have successfully incorporated CT image features into a framework for modeling RP and have demonstrated improved predictive performance. Validation and further investigation of CT image features in the context of RP NTCP modeling is warranted. This work was supported by the Rosalie B. Hite Fellowship in Cancer research awarded to SPK.« less
  • Purpose: To describe the probability of RILD by application of the Lyman-Kutcher-Burman normal-tissue complication (NTCP) model for primary liver carcinoma (PLC) treated with hypofractionated three-dimensional conformal radiotherapy (3D-CRT). Methods and Materials: A total of 109 PLC patients treated by 3D-CRT were followed for RILD. Of these patients, 93 were in liver cirrhosis of Child-Pugh Grade A, and 16 were in Child-Pugh Grade B. The Michigan NTCP model was used to predict the probability of RILD, and then the modified Lyman NTCP model was generated for Child-Pugh A and Child-Pugh B patients by maximum-likelihood analysis. Results: Of all patients, 17 developedmore » RILD in which 8 were of Child-Pugh Grade A, and 9 were of Child-Pugh Grade B. The prediction of RILD by the Michigan model was underestimated for PLC patients. The modified n, m, TD{sub 5} (1) were 1.1, 0.28, and 40.5 Gy and 0.7, 0.43, and 23 Gy for patients with Child-Pugh A and B, respectively, which yielded better estimations of RILD probability. The hepatic tolerable doses (TD{sub 5}) would be MDTNL of 21 Gy and 6 Gy, respectively, for Child-Pugh A and B patients. Conclusions: The Michigan model was probably not fit to predict RILD in PLC patients. A modified Lyman NTCP model for RILD was recommended.« less
  • Purpose: One major uncertainty in radiotherapy planning of non-small-cell lung cancer concerns the definition of the clinical target volume (CTV), meant to cover potential microscopic disease extension (MDE) around the macroscopically visible tumor. The primary aim of this study was to establish pretreatment risk factors for the presence of MDE. The secondary aim was to establish the impact of these factors on the accuracy of positron emission tomography (PET) and computed tomography (CT) to assess the total tumor-bearing region at pathologic examination (CTV{sub path}). Methods and Materials: 34 patients with non-small-cell lung cancer who underwent CT and PET before lobectomymore » were included. Specimens were examined microscopically for MDE. The gross tumor volume (GTV) on CT and PET (GTV{sub CT} and GTV{sub PET}, respectively) was compared with the GTV and the CTV at pathologic examination, tissue deformations being taken into account. Using multivariate logistic regression, image-based risk factors for the presence of MDE were identified, and a prediction model was developed based on these factors. Results: MDE was found in 17 of 34 patients (50%). The MDE did not exceed 26 mm in 90% of patients. In multivariate analysis, two parameters (mean CT tumor density and GTV{sub CT}) were significantly associated with MDE. The area under the curve of the two-parameter prediction model was 0.86. Thirteen tumors (38%, 95% CI: 24-55%) were identified as low risk for MDE, being potential candidates for reduced-intensity therapy around the GTV. In the low-risk group, the effective diameter of the GTV{sub CT/PET} accurately represented the CTV{sub path}. In the high-risk group, GTV{sub CT/PET} underestimated the CTV{sub path} with, on average, 19.2 and 26.7 mm, respectively. Conclusions: CT features have potential to predict the presence of MDE. Tumors identified as low risk of MDE show lower rates of disease around the GTV than do high-risk tumors. Both CT and PET accurately visualize the CTV{sub path} in low-risk tumors but underestimate it in high-risk tumors.« less
  • Purpose: To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). Methods and Materials: A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identifiedmore » in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. Results: For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84). Conclusions: A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.« less