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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Texture analysis of AGR program matrix materials

We report the fuel form for high-temperature gas-cooled reactors consists of tristructural isotropic (TRISO) particles embedded in a matrix of graphite flake and carbonized resin. The process of overcoating particles prior to compacting yields a circumferential orientation of the graphite flake surrounding the TRISO particles, which is modified to varied extents when overcoated particles are pressed into the final fuel form. As graphite is highly anisotropic, the texture may impact the properties and performance of the fuel. Ellipsometry was used to measure the texture of the matrix for fueled compacts and unfueled “matrix-only” samples. Results indicated local texture related to the spherical particles in compacts associated with overcoating versus a more linear layered structure in “matrix-only” samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Diffraction Methods for Qualitative and Quantitative Texture Analysis of Ferroelectric Ceramics

Crystallographic textures are pervasive in ferroelectrics and underpin the functional properties of devices utilizing these materials because many macroscopic properties (e.g., piezoelectricity) require a non-random distribution of dipoles. Inducing a preferred grain texture has become a viable route to improve these functional properties. X-ray and neutron diffraction have become valuable tools to probe crystallographic textures. This paper presents an overview of qualitative and quantitative methods for assessing crystallographic textures in electroceramics (domain and grain textures) and discusses their strengths and weaknesses.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Texture measurements on quartz single crystals to validate coordinate systems for neutron time-of-flight texture analysis

In crystallographic texture analysis, ensuring that sample directions are preserved from experiment to the resulting orientation distribution is crucial to obtain physical meaning from diffraction data. This work details a procedure to ensure instrument and sample coordinates are consistent when analyzing diffraction data with a Rietveld refinement using the texture analysis software MAUD . A quartz crystal is measured on the HIPPO diffractometer at Los Alamos National Laboratory for this purpose. The methods described here can be applied to any diffraction instrument measuring orientation distributions in polycrystalline materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Assessment of Therapy Response to Transarterial Radioembolization for Liver Metastases by Means of Post-treatment MRI-Based Texture Analysis

IntroductionTo determine whether post-treatment magnetic resonance imaging (MRI)-based texture analysis of liver metastases (LM) may be suited predicting therapy response to transarterial radioembolization (TARE) during follow-up.Materials and MethodsThirty-seven patients with LM treated by TARE (mean age 63.4 years) between January 2006 and December 2014 were identified in this retrospective feasibility study. They underwent dynamic contrast-enhanced and hepatocellular phase MRI after TARE (mean 2.2 days). Response was evaluated on follow-up imaging scheduled in intervals of 3 months (median follow-up, 7.3 months) based on response evaluation criteria in solid tumors 1.1 (RECIST 1.1). Results of texture analysis [mean, standard deviation, skewness (s), kurtosis (k), entropy and uniformity] were compared between patients with progressive disease (PD) and patients with stable disease (SD), partial or complete response (PR/CR). Receiver operating characteristics including the area under the curve (AUC) and cutoff values including the sensitivity and specificity were calculated.ResultsAccording to RECIST 1.1, 24 patients (64.9%) had PD, 8 SD (21.6%) and 5 PR (13.5%). MRI-based texture analysis showed an earlier differentiation between patients with and without PD when compared with RECIST 1.1. Median k (2.88 vs. 2.35) in arterial phase MRI and median s (0.48 vs. 0.25) and k (2.85 vs. 2.25) in venous phase MRI were significantly different (p < 0.05). The AUC for k derived from arterial phase MRI was 0.73 (cutoff = 2.55, sensitivity = 0.83, specificity = 0.62) (p < 0.05). The AUC for s and k in venous phase MRI was 0.76 (cutoff = 0.35, sensitivity = 0.71, specificity = 0.85) (p > 0.05) and 0.83 (cutoff = 2.50, sensitivity = 0.75, specificity = 0.85) (p < 0.05).ConclusionThis study indicates the potential of MRI-based texture analysis at arterial and venous phase MRI for the early prediction of PD after TARE. Level of Evidence: IV.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome

Purpose: To assess the association between T2-weighted imaging (T2WI) texture-analysis parameters and the pathological aggressiveness or long-term outcomes in pancreatic ductal adenocarcinoma (PDAC) patients. Methods: A total of 66 patients (mean age 65.3 ± 9.0 years) who underwent preoperative MRI followed by pancreatectomy for PDAC between 2013 and 2015 were included in this study. A radiologist performed a texture analysis twice on one axial image using commercial software. Differences in the tex parameters, according to pathological factors, were analyzed using a Student’s t test or an ANOVA with Tukey’s test. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the association between tex parameters and recurrence-free survival (RFS) or overall survival (OS). Results: The mean follow-up time was 18.5 months, and there were 58 recurrences and 39 deaths. The mean of the positive pixel (MPP)-related factors was significantly lower in poorly differentiated tumors than in well-differentiated tumors as well as in cases with perineural invasion. The univariate Cox proportional hazards analysis showed a significant association between the tex parameters and RFS or OS. However, only tumor size was statistically significant after the multivariate analysis. Only tumor size and entropy with medium texture were significantly associated with OS after the multivariate analysis. Conclusions: Tumor size was a significant predictive factor for RFS and OS in PDAC patients. Although entropy with medium texture analysis was significantly associated with OS, there were also limitations in the texture analysis; thus, further study is necessary.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Prediction of liver remnant regeneration after living donor liver transplantation using preoperative CT texture analysis

Purpose: To predict the rate of liver regeneration after living donor liver transplantation (LDLT) using pre-operative computed tomography (CT) texture analysis. Materials and methods: 112 living donors who performed right hepatectomy for LDLT were included retrospectively. We measured the volume of future remnant liver (FLR) on pre-operative CT and the volume of remnant liver (LR) on follow-up CT, taken at a median of 123 days after transplantation. The regeneration index (RI) was calculated using the following equation: [(V{sub LR}−V{sub FLR})/V{sub FLR}] × 100. Computerized texture analysis of the semi-automatically segmented FLR was performed. We used a stepwise, multivariable linear regression to assess associations of clinical features and texture parameters in relation to RI and to make the best-fit predictive model. Results: The mean RI was 110.7 ± 37.8%, highly variable ranging from 22.4% to 247.0%. Among texture parameters, volume of FLR, standard deviation, variance, and gray level co-occurrence matrices (GLCM) contrast were found to have significant correlations between RI. In multivariable analysis, smaller volume of FLR (ß − 0.17, 95% CI − 0.22 to − 0.13) and lower GLCM contrast (ß − 1.87, 95% CI − 3.64 to − 0.10) were associated with higher RI. The regression equation predicting RI was following: RI = 203.82 + 10.42 × pre-operative serum total bilirubin (mg/dL) − 0.17 × V{sub FLR} (cm{sup 3}) − 1.87 × GLCM contrast (× 100). Conclusion: Volume of FLR and GLCM contrast were independent predictors of RI, showing significant negative correlations. Pre-operative CT with texture analysis can be useful for predicting the rate of liver regeneration in living donor of liver transplantation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

5-Fluorouracil induced liver toxicity in patients with colorectal cancer: role of computed tomography texture analysis as a potential biomarker

Purpose: To assess if CT texture analysis (TA) can serve as a biomarker of liver toxicity in patients with colorectal cancer treated with 5-fluorouracil (5-FU)-based chemotherapy. Methods: In this IRB-approved, HIPAA-compliant retrospective study, patients with colorectal cancer treated with 5-FU-based regimens during 2008–2010 were identified from institutional electronic database. Total 43 patients (23 women; mean age 56 years) with normal baseline liver function tests (LFTs), availability of baseline (pre-chemotherapy) and first follow-up CT (median 1.7 months, interquartile range (IQR) 1.5–2.5) performed during chemotherapy were included. Two single-slice ROI of right and left liver lobe were obtained on baseline and first follow-up CT for TA. Texture features [mean, entropy, kurtosis, skewness, mean of positive pixel, standard deviation (SD)] were extracted using a commercially available software (TexRAD; Feedback Medical Ltd, Cambridge, UK). Changes in texture parameters between baseline and follow-up CT were evaluated with Wilcoxon signed-rank test for patients with and without LFT elevation during chemotherapy. Results: Patients with LFT elevation (n = 34; 79%) showed significantly different mean, entropy, skewness, and SD (p values range 0.007–0.047) between baseline and first follow-up CT. No significant changes in features were observed in patients without LFT elevation (n = 9; 21%). In 19 patients (56%), first follow-up CT was performed before elevation of LFTs was observed. Conclusions: This proof-of-concept study shows that there are early changes in liver texture on first follow-up CT in patients with LFT elevation during 5-FU-based chemotherapy for colorectal cancer. In more than 50% of cases, these changes occur before LFT elevation becomes evident on blood tests.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Investigation into Texture and Structure of MA2-1PCh Magnesium Alloy after Equal Channel Angular Extrusion and Annealing by Quantitative X-Ray Texture Analysis and Electron Backscatter Diffraction

This article discusses application of quantitative X-ray texture analysis (QTXA) based on measurement of direct pole figures and electron backscatter diffraction (EBSD) aimed at study of peculiarities of formation of texture and structure of MA2-1pch magnesium alloy after equal channel angular extrusion (ECAE) and subsequent annealing. Shear bands with the width of 60 μm oriented at 45°–50° to the extrusion direction are revealed, where an inclined scattered basis texture is detected, and outside of these bands, a prismatic texture. As a consequence, basic sliding is activated in the shear bands, which is confirmed by the distribution of Schmid factors. The influence of texture and orientation of shear bands on anisotropy of strength mechanical properties of alloy is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

We investigated the association between the textural features obtained from {sup 18}F-FDG images, metabolic parameters (SUVmax{sub ,} SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws’ texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws’ approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws’ method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws’ approach could be useful in the discrimination of tumor stage.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers

Purpose: To assess CT texture features of small renal cell carcinomas (≤ 4cm) for association with key pathologic features including protein biomarkers. Methods: Quantitative CT texture analysis (CTTA) of small renal cancers (≤ 4cm) was performed on non-contrast and portal venous phase abdominal MDCT scans with an ROI drawn at the largest cross-sectional diameter of the tumor using commercially available software. Texture parameters including mean pixel attenuation, the standard deviation (SD) of the pixel distribution histogram, entropy, the mean of positive pixels, the skewness (i.e., asymmetry) of the pixel histogram, kurtosis (i.e., peakness) of the pixel histogram, and the percentage of positive pixels were correlated with pathologic data from surgical resection, including histology and nuclear grade, as well as microarray analysis in a subset (n = 40) including Ki67 index, CRP, and neovascularization (CD105/CD31). Results: Portal venous phase images were available in 249 patients (105 women, 144 men; mean age, 56.7 years) with tumors ≤ 4cm (mean, median, range, ± SD; 2.66, 2.60, 0.3–4.0 ± 0.85 cm). CT texture features of standard deviation, mean of the positive pixels, and entropy of the pixel histogram were significantly associated with histologic cell type (clear vs. non-clear; p < 0.001). Entropy and mean of the positive pixels also showed an association with nuclear grade, although not statistically significant. In the microarray analysis subset, kurtosis of the pixel histogram was associated with CD105/CD31 (p = 0.05). SD also showed some association with CD 105 positivity (p = 0.02) and CAIX expression (p = 0.01). Non-contrast CT images were available in 174 patients (72 women, 102 men; mean age, 57.5 years). Although the association with histology was not as strong as on the portal venous phase, in the subset of patients with microarray data, SD was found to correlate with CRP (p = 0.08), kurtosis with CRP (p = 0.004), CD105/CD31 (p = 0.002), and with Ki 67 index (p < 0.001). Conclusion: CT texture features were significantly associated with important histopathologic features in small renal cancers. These non-invasive measures can be performed retrospectively and may provide useful information when determining follow-up and treatment of small renal cancers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum?

Purpose: To determine if texture analysis can differentiate placenta accreta spectrum (PAS) from normal placenta on MRI. Methods: We performed retrospective image analysis of 80 patients, comprised of 46 patients with PAS and 34 patients without PAS. Histopathology was used as the reference standard. Sagittal single shot fast spin echo T2-weighted MRI sequences acquired from a single institution were analyzed. Placental heterogeneity was quantified using in-house software on a Matlab platform, including the standard deviation of pixel intensity, coefficient of variation, gray-level co-occurrence matrices (GLCM), histogram-oriented gradients (HOG), and fractal analysis with box sizes from 2 to 512. Two-tailed unpaired Student’s t test was used with statistical significance of p < 0.05. Results: PAS was associated with higher values for standard deviation of pixel intensity and fractal analysis at every box size. Fractal analysis at box sizes 256 (p = 0.011) and 32 (p = 0.021), and standard deviation of pixel intensity (p = 0.023) were the most statistically significant. Fractal values at box size 256 for PAS was 0.13 versus 0.090 for patients without PAS, while standard deviation of pixel intensity was 3.7 for PAS versus 2.5 for patients without PAS. No statistically significant association between PAS and GLCM, coefficient of variation, and HOG was found. Conclusion: Statistically significant differences were found between normal and abnormal groups using standard deviation of pixel intensity and fractal analysis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-F-R-13: Decoding 18F-FDG Uptake Heterogeneity for Primary and Lymphoma Tumors by Using Texture Analysis in PET Images

Purpose: To explore 18F-FDG uptake heterogeneity of primary tumor and lymphoma tumor by texture features of PET image and quantify the heterogeneity difference between primary tumor and lymphoma tumor. Methods: 18 patients with primary tumor and lymphoma tumor in lung cancer were enrolled. All patients underwent whole-body 18F-FDG PET/CT scans before treatment. Texture features, based on Gray-level Co-occurrence Matrix, second and high order matrices are extracted from code using MATLAB software to quantify 18F-FDG uptake heterogeneity. The relationships of volume between energy, entropy, correlation, homogeneity and contrast were analyzed. Results: For different cases, tumor heterogeneity was not the same. Texture parameters (contrast, entropy, and correlation) of lymphoma were lower than primary tumor. On the contrast, the texture parameters (energy, homogeneity and inverse different moment) of lymphoma were higher than primary tumor. Significantly, correlations were observed between volume and energy (primary, r=−0.194, p=0.441; lymphoma, r=−0.339, p=0.582), homogeneity (primary, r=−0.146, p=0.382; lymphoma, r=−0.193, p=0.44), inverse difference moment (primary, r=−0.14, p=0.374; lymphoma, r=−0.172, p=0.414) and a positive correlation between volume and entropy (primary, r=0.233, p=0.483; lymphoma, r=0.462, p=0.680), contrast (primary, r=0.159, p=0.399; lymphoma, r=0.341, p=0.584), correlation (primary, r=0.027, p=0.165; lymphoma, r=0.046, p=0.215). For the same patient, energy for primary and lymphoma tumor is equal. The volume of lymphoma is smaller than primary tumor, but the homogeneity were higher than primary tumor. Conclusion: This study showed that there were effective heterogeneity differences between primary and lymphoma tumor by FDG-PET image texture analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases

Purpose: The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy. Methods: The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy{sup 1}) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy{sup 2}, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis. Results: A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy{sup 1}, contrast, entropy{sup 2} and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602–0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814. Conclusion: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade

Purpose: Grades of pancreatic neuroendocrine neoplasms (PNENs) are associated with the choice of treatment strategies. Texture analysis has been used in tumor diagnosis and staging evaluation. In this study, we aim to evaluate the potential ability of texture parameters in differentiation of PNENs grades. Materials and methods: 37 patients with histologically proven PNENs and underwent pretreatment dynamic contrast-enhanced computed tomography examinations were retrospectively analyzed. Imaging features and texture features at contrast-enhanced images were evaluated. Receiver operating characteristic curves were used to determine the cut-off values and the sensitivity and specificity of prediction. Results: There were significant differences in tumor margin, pancreatic duct dilatation, lymph nodes invasion, size, portal enhancement ratio (PER), arterial enhancement ratio (AER), mean grey-level intensity, kurtosis, entropy, and uniformity among G1, G2, and pancreatic neuroendocrine carcinoma (PNEC) G3 (p < 0.01). Similar results were found between pancreatic neuroendocrine tumors (PNETs) G1/G2 and PNEC G3. AER and PER showed the best sensitivity (0.86–0.94) and specificity (0.92–1.0) for differentiating PNEC G3 from PNETs G1/G2. Mean grey-level intensity, entropy, and uniformity also showed acceptable sensitivity (0.73–0.91) and specificity (0.85–1.0). Mean grey-level intensity was also showed acceptable sensitivity (91% to 100%) and specificity (82% to 91%) in differentiating PNET G1 from PNET G2. Conclusions: Our data indicated that texture parameters have potential in grading PNENs, in particular in differentiating PNEC G3 from PNETs G1/G2.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Computed tomography-based texture analysis of bladder cancer: differentiating urothelial carcinoma from micropapillary carcinoma

Purpose: The purpose of the study is to determine the feasibility of using computed tomography-based texture analysis (CTTA) in differentiating between urothelial carcinomas (UC) of the bladder from micropapillary carcinomas (MPC) of the bladder. Methods: Regions of interests (ROIs) of computerized tomography (CT) images of 33 MPCs and 33 UCs were manually segmented and saved. Custom MATLAB code was used to extract voxel information corresponding to the ROI. The segmented tumors were input to a pre-existing radiomics platform with a CTTA panel. A total of 58 texture metrics were extracted using four different texture extraction techniques and statistically analyzed using a Wilcoxon rank-sum test to determine the differences between UCs and MPCs. Results: Of the 58 texture metrics extracted using the gray level co-occurrence matrix (GLCM) and gray level difference matrix (GLDM), 28 texture metrics were statistically significant (p < 0.05) for differences in tumor textures and 27 texture metrics were statistically significant (p < 0.05) for peritumoral fat textures. The remaining nine metrics extracted using histogram and fast Fourier transform analyses did not show significant differences between the textures of the tumors and their peritumoral fat. Conclusions: CTTA shows that MPC have a more heterogeneous texture compared to UC. As visual discrimination of MPC from UC from clinical CT scans are difficult, results from this study suggest that tumor heterogeneity extracted using GLCM and GLDM may be a good imaging aid in segregating MPC from UC. This tool can aid clinicians in further sub-classifying bladder cancers on routine imaging, a process which has potential to alter treatment and patient care.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection

Purpose: To evaluate the feasibility of using computed tomography texture analysis (CTTA) parameters for predicting malignant risk grade and mitosis index of gastrointestinal stromal tumors (GISTs), compared with visual inspection. Method and materials: CTTA was performed on portal phase CT images of 145 surgically confirmed GISTs (mean size: 42.9 ± 37.5 mm), using TexRAD software. Mean, standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis of CTTA parameters, on spatial scaling factor (SSF), 2–6 were compared by risk grade, mitosis rate, and the presence or absence of necrosis on visual inspection. CTTA parameters were correlated with risk grade. Diagnostic performance was evaluated with receiver operating characteristic curve analysis. Enhancement pattern, necrosis, heterogeneity, calcification, growth pattern, and mucosal ulceration were subjectively evaluated by two observers. Results: Three to four parameters at different scales were significantly different according to the risk grade, mitosis rate, and the presence or absence of necrosis (p < 0.041). MPP at fine or medium scale (r = − 0.547 to − 393) and kurtosis at coarse scale (r = 0.424–0.454) correlated significantly with risk grade (p < 0.001). HG-GIST was best differentiated from LG-GIST by MPP at SSF 2 (AUC, 0.782), and kurtosis at SSF 4 (AUC, 0.779) (all p < 0.001). CT features predictive of HG-GIST were density lower than or equal to that of the erector spinae muscles on enhanced images (OR 2.1; p = 0.037; AUC, 0.59), necrosis (OR, 6.1; p < 0.001; AUC, 0.70), heterogeneity (OR, 4.3; p < 0.001; AUC, 0.67), and mucosal ulceration (OR, 3.3; p = 0.002; AUC, 0.62). Conclusion: Using TexRAD, MPP and kurtosis are feasible in predicting risk grade and mitosis index of GISTs. CTTA demonstrated meaningful accuracy in preoperative risk stratification of GISTs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-F-J-207: Non-Small Cell Lung Cancer Patient Survival Prediction with Quantitative Tumor Textures Analysis in Baseline CT

Purpose: Chemo-radiation therapy (CRT) is widely used in treating patients with locally advanced non-small cell lung cancer (NSCLC). Determination of the likelihood of patient response to treatment and optimization of treatment regime is of clinical significance. Up to date, no imaging biomarker has reliably correlated to NSCLC patient survival rate. This pilot study is to extract CT texture information from tumor regions for patient survival prediction. Methods: Thirteen patients with stage II-III NSCLC were treated using CRT with a median dose of 6210 cGy. Non-contrast-enhanced CT images were acquired for treatment planning and retrospectively collected for this study. Texture analysis was applied in segmented tumor regions using the Local Binary Pattern method (LBP). By comparing its HU with neighboring voxels, the LBPs of a voxel were measured in multiple scales with different group radiuses and numbers of neighbors. The LBP histograms formed a multi-dimensional texture vector for each patient, which was then used to establish and test a Support Vector Machine (SVM) model to predict patients’ one year survival. The leave-one-out cross validation strategy was used recursively to enlarge the training set and derive a reliable predictor. The predictions were compared with the true clinical outcomes. Results: A 10-dimensional LBP histogram was extracted from 3D segmented tumor region for each of the 13 patients. Using the SVM model with the leave-one-out strategy, only 1 out of 13 patients was misclassified. The experiments showed an accuracy of 93%, sensitivity of 100%, and specificity of 86%. Conclusion: Within the framework of a Support Vector Machine based model, the Local Binary Pattern method is able to extract a quantitative imaging biomarker in the prediction of NSCLC patient survival. More patients are to be included in the study.

60 APPLIED LIFE SCIENCES↗